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RIS with Coupled Phase Shift and Amplitude: Capacity Maximization and Configuration Set Selection

Seyedkhashayar Hashemi, Masoud Ardakani, and Hai Jiang The authors are with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada (e-mail: {seyedkha, ardakani, hai1}@ualberta.ca).
Abstract

A reconfigurable intelligent surface (RIS) is a planar surface that can enhance the quality of communication by providing control over the communication environment. Reflection optimization is one of the pivotal challenges in RIS setups. While there has been lots of research regarding the reflection optimization of RIS, most works consider the independence of the phase shift and the amplitude of RIS reflection coefficients. In practice, the phase shift and the amplitude are coupled and according to a recent study, the relation between them can be described using a function. In our work, we consider a practical system model with coupled phase shift and amplitude. We develop an efficient method for achieving capacity maximization by finding the optimal reflection coefficients of the RIS elements. The complexity of our method is linear with the number of RIS elements and the number of discrete phase shifts. We also develop a method that optimally selects the configuration set of the system, where a configuration set means a discrete set of reflection coefficient choices that a RIS element can take.

Index Terms:
Reconfigurable intelligent surfaces, reflection optimization, practical system model, coupled phase shift and amplitude.

I Introduction

Reconfigurable intelligent surface (RIS) technology is considered as one of the key enabling components for 6G wireless communications. By providing control over the communication environment, the RIS technology offers numerous benefits such as energy efficiency, better coverage, higher data rate, lower cost, and more reliable communications [1].

A RIS is a planar surface consisting of a number of small elements [2]. The surfaces are reconfigurable, meaning that once a surface is deployed, the characteristics of its elements can be adjusted using a controller [3]. Each RIS element can be configured to modify the amplitude and the phase of its incident signal [4, 5, 6].

RIS technology can aid us in coverage extension by generating a virtual line-of-sight (LoS) channel when an obstacle blocks the direct channel between the transmitter and the receiver [7]. In such cases, appropriate adjustment of the reflection coefficients of the RIS elements is crucial. Reflection optimization plays an important part in the efficient usage of RIS and therefore is investigated in many recent studies [8, 9, 10, 11].

A commonly assumed scenario regarding RIS-aided communication systems is the multi-user setup, in which a base station communicates with several users with a RIS between them [12]. The problem is often formulated as a joint optimization of multiple variables [13]. The alternating optimization (AO) technique [14] is often used to solve the problem. At each step of AO, one variable is optimized at a time while the rest are fixed [15]. Several objectives can be considered for the optimization including sum-rate maximization [16], total transmit power minimization [17], energy efficiency maximization [18], and maximization of minimum rate [19] or signal-to-interference-plus-noise ratio (SINR) [20] for user fairness.

On the other hand, there are ways to break the multi-user scenario into a number of single-user sub-problems. Methods such as RIS partitioning [21] and distributed RIS deployment [22] can help us achieve this goal. In RIS partitioning, the surface is divided into several segments, each serving a particular user [23]. In a distributed deployment of RIS, instead of having one RIS serving multiple users, there will be multiple smaller surfaces each serving a particular user [24]. Since each surface (or each surface partition) is now responsible for only one user, the setup can now be considered as multiple single-user communications. Therefore, many studies that consider a single-user setup have applications in more general multi-user setups too.

In the reflection optimization of a single-user setup, ideally, the amplitudes and the phase shifts of the reflection coefficients of the RIS elements are assumed to be continuously and independently adjustable [25]. In this scenario, the optimal solution can be achieved by aligning the RIS-aided paths with the direct path from the transmitter to the receiver while keeping the maximal possible amplitude of the reflection coefficients [26]. However, achieving continuous adjustment for the phase shift is not possible in practice [27]. A more realistic assumption is to consider a finite number of discrete, evenly-spaced phase shifts [28]. Unlike the continuous case, the optimization procedure is challenging for the discrete case. Several suboptimal approaches can be used such as quantizing the solution obtained from the continuous-phase-shift optimization problem [29] or alternately optimizing the phase shifts [30]. To obtain the globally optimal solution, methods such as exhaustive search and branch-and-bound (BB) [31] can be used, but with high complexity. An efficient method is proposed in [32] that can obtain the global optimality with linear complexity.

While works in [29, 30, 31, 32] propose interesting solutions to determine RIS reflection coefficients with evenly-spaced discrete phase shifts, the assumption of having evenly-spaced phase shifts can be unrealistic in practical scenarios [33]. Hence the work in [34] considers an arbitrary set of phase shifts of RIS reflection coefficients and achieves an optimal solution to determine RIS reflection coefficients in linear complexity.

All the aforementioned works consider that the phase shifts and amplitudes of RIS reflection coefficients can be independently adjusted. However, such an assumption is not feasible for a real RIS implementation [35]. In [36], a practical model has been developed in which the amplitude is shown as a function of the phase shift, i.e., the amplitude and phase shift are coupled. The practical model has been used in several other works [37, 38]. In the literature, there has been no research that guarantees to achieve reflection optimization of RIS elements with the practical model. To fill this research gap, the following two major challenges should be addressed.

  • Given a configuration set (here a configuration set is defined as a discrete set of reflection coefficient choices that a RIS element can take), how to optimally determine the RIS reflection coefficients for each channel realization of the system such that the maximal capacity is achieved? This challenge is referred to as Capacity Maximization.

  • How to select a configuration set for the system such that the average system capacity (averaged over all possible channel realizations) is maximized? This challenge is referred to as Configuration Set Selection.

We address both challenges in this paper. The contributions of this paper are summarized as follows

  • Regarding capacity maximization with a given configuration set, we develop a method that yields the globally optimal RIS reflection coefficients that achieve capacity maximization. The complexity of our method is linear with the number of RIS elements and linear with the size of the configuration set.

  • To determine the optimal configuration set of the system, Monte Carlo simulations can be used, but with prohibitive complexity. To solve the problem in a much faster way, we theoretically prove that maximizing the average system capacity is approximately equivalent to maximizing the integral of a one-dimensional function. Thus, to get the optimal configuration set, we only need to find the configuration set in which the integral of the one-dimensional function is maximized. Our method is much faster than optimization based on Monte Carlo simulations, since for each configuration set, we only need to calculate an integral rather than running a large number of simulations. We also give a method to cut the running time of our method by almost half.

The remainder of this paper is structured as follows. Section II discusses the system model and the practical RIS model for coupled amplitude and phase shift of reflection coefficients. In Section III, given a configuration set, our proposed method is presented to optimally solve the capacity maximization problem with linear complexity. In Section IV, we present our method to optimally select a configuration set. Simulation results in Section V show the performance of our proposed methods as well as comparison with other methods. Section VI concludes the work.

II System Model and Practical RIS Reflection Coefficient Model

II-A System Model

This paper considers that a transmitter communicates with a receiver as shown in Fig. 1. There is also a RIS between the transmitter and the receiver. The received signal at the receiver can be written as

y[t]=hx[t]+w[t],y[t]=h\cdot x[t]+w[t], (1)

where y[t]y[t]\in\mathbb{C} is the received signal, x[t]x[t]\in\mathbb{C} is the transmitted signal, and w[t]𝒩(0,N0)w[t]\sim\mathcal{N}_{\mathbb{C}}(0,N_{0}) is the additive white Gaussian noise (AWGN). The channel between the transmitter and the receiver, denoted as hh\in\mathbb{C}, can be expressed as [26]

h=h0+n=1Nhnθnhn′′.h=h_{0}+\sum_{n=1}^{N}h^{\prime}_{n}\theta_{n}h^{\prime\prime}_{n}. (2)

In (2), NN is the number of RIS elements, h0h_{0}\in\mathbb{C} is the direct channel between the transmitter and the receiver, hnh^{\prime}_{n}\in\mathbb{C} is the channel between the transmitter and the nnth RIS element, θn=βnejαn\theta_{n}=\beta_{n}e^{j\alpha_{n}}\in\mathbb{C} is the reflection coefficient of the nnth RIS element, hn′′h^{\prime\prime}_{n}\in\mathbb{C} is the channel between the nnth RIS element and the receiver. Since h is a complex number, it will have a magnitude and a phase. In this paper, x\angle x denotes the phase of the complex number xx.

Refer to caption
Figure 1: The system model consisting of a transmitter, a receiver, and a RIS.

The overall channel from the transmitter to the receiver through the nnth RIS element can be expressed as

gn=hnθnhn′′.g_{n}=h^{\prime}_{n}\theta_{n}h^{\prime\prime}_{n}. (3)

It is also useful to define the cascaded channel coefficient vnv_{n}\in\mathbb{C} for the nnth RIS element as

vn=hnhn′′.v_{n}=h^{\prime}_{n}h^{\prime\prime}_{n}. (4)
Refer to caption
Figure 2: Relationship between the phase shift and the amplitude.

The channel capacity from the transmitter to the receiver can be calculated as

C=Blog2(1+P|h|2BN0)bits/s,C=B\log_{2}(1+\frac{P|h|^{2}}{BN_{0}})\quad\text{bits/s}, (5)

in which BB is the transmitted signal bandwidth, PP is the transmitted signal power, N0N_{0} is the noise power spectral density, and P|h|2BN0\frac{P|h|^{2}}{BN_{0}} is the signal-to-noise ratio (SNR).

II-B Practical RIS Reflection Coefficient Model

In general, the reflection coefficient of the nnth RIS element, denoted as θn\theta_{n}, is defined by two parameters, the amplitude (βn\beta_{n}) and the phase shift (αn\alpha_{n}). In other words,

θn=βnejαn.\theta_{n}=\beta_{n}e^{j\alpha_{n}}. (6)

In an ideal setup, the amplitude and the phase shift are independent and can take any possible value in a particular range

βn[0,1],αn[0,2π).\beta_{n}\in[0,1],\alpha_{n}\in[0,2\pi). (7)

However, the ideal setup is not valid for practical RIS. According to [36], for any circuit implementation of RIS, βn\beta_{n} and αn\alpha_{n} are not independent, and the relation between them can be expressed as

βn(αn)=(1βmin)(sin(αnϕ)+12)κ+βmin,\beta_{n}(\alpha_{n})=(1-\beta_{\min})\left(\frac{\sin(\alpha_{n}-\phi)+1}{2}\right)^{\kappa}+\beta_{\min}, (8)

where βmin\beta_{\min}, ϕ\phi, and κ\kappa are all non-negative constants. Such a practical model motivated us to consider the phase shift and the amplitude to be coupled rather than independent for our system model. For better illustration, Fig. 2 demonstrates an example for the relationship between βn\beta_{n} and αn\alpha_{n}.111According to [36], we use βmin=0.2\beta_{\min}=0.2, κ=1.6\kappa=1.6, and ϕ=0.43π\phi=0.43\pi in the example in Fig. 2.

Moreover, assuming a continuous adjustment for the phase shift is infeasible in practical setups. A better and more realistic assumption would be to consider a finite number of discrete choices of phase shift [31]. Therefore, in our setup, for each RIS element, say the nnth element, θn\theta_{n} is chosen from a configuration set, i.e., a finite set of KK choices:

θn{β^1ejα^1,β^2ejα^2,,β^Kejα^K},\theta_{n}\in\{{\hat{\beta}_{1}e^{j\hat{\alpha}_{1}}},{\hat{\beta}_{2}e^{j\hat{\alpha}_{2}}},...,{\hat{\beta}_{K}e^{j\hat{\alpha}_{K}}}\}, (9)

in which amplitude β^k\hat{\beta}_{k} and phase shift α^k\hat{\alpha}_{k} are constants and satisfy (8) for k{1,2,,K}k\in\{1,2,...,K\}.

III Optimal Solution for Capacity Maximization

For a system with a given configuration set as shown in (9), our goal is to maximize the capacity for each channel realization of the system by finding the optimal reflection coefficients of the RIS elements. As seen in (5), |h||h| should be maximized to get the maximal capacity. Therefore, θn\theta_{n} should be chosen in a way that the summation in (2) has the largest possible magnitude. The capacity maximization problem for any given channel realization of the system (i.e., given h0,v1,v2,,vNh_{0},v_{1},v_{2},...,v_{N}), therefore, can be formulated as:

maxθ1,θ2,θN\displaystyle\max_{\theta_{1},\theta_{2}...,\theta_{N}} |h|\displaystyle|h| (10)
s.t. θ1,θ2,θN{β^1ejα^1,β^2ejα^2,,β^Kejα^K}.\displaystyle\theta_{1},\theta_{2}...,\theta_{N}\in\{{\hat{\beta}_{1}e^{j\hat{\alpha}_{1}}},{\hat{\beta}_{2}e^{j\hat{\alpha}_{2}}},...,{\hat{\beta}_{K}e^{j\hat{\alpha}_{K}}}\}.

We will optimize the problem in (10) in two steps. Consider θn\theta_{n}^{*} as the optimal reflection coefficient for the nnth RIS element and hh^{*} as the resulting optimal channel between the transmitter and the receiver. First, assuming h\angle h^{*} (i.e., the phase of hh^{*}) is known, we will develop a method for determining θn\theta_{n}^{*} for all elements. We will discuss this step in detail in Section III-A.

In practice, h\angle h^{*} is not known at the beginning. Thus, in the next step, we have to go through all possibilities of h\angle h^{*} and find the one with the largest |h||h|. This may seem an impossible task since there will be infinite possibilities for h\angle h^{*}. However, we will prove that by going through just a finite number of possibilities for h\angle h^{*}, we will be able to find the optimal solution. Sections III-B \sim III-E give details of our method and related analysis, insights, and proofs.

III-A Determining θn\theta_{n}^{*} by Assuming h\angle h^{*} is Known

Assume h\angle h^{*} is known. Consider the nnth RIS element. According to (9), there will be KK choices for θn\theta_{n}. Thus, there will also be KK choices for gng_{n} (expression of gng_{n} is given in (3)), i.e., gn{gn,1,gn,2,,gn,K}g_{n}\in\{g_{n,1},g_{n,2},...,g_{n,K}\}, with

gn,k=vnβ^kejα^kg_{n,k}=v_{n}\hat{\beta}_{k}e^{j\hat{\alpha}_{k}} (11)

for k=1,2,,Kk=1,2,...,K.

Let us define h,gn,i\langle h^{*},g_{n,i}\rangle as:

h,gn,i=|h||gn,i|cos(hgn,i).\langle h^{*},g_{n,i}\rangle=|h^{*}|\cdot|g_{n,i}|\cos(\angle h^{*}-\angle g_{n,i}). (12)

If we view complex numbers hh^{*} and gn,ig_{n,i} as vectors in a complex plane, then h,gn,i\langle h^{*},g_{n,i}\rangle is actually the real inner product of vector hh^{*} and vector gn,ig_{n,i}.

We have the following theorem.

Theorem 1.

If h,gn,i\langle h^{*},g_{n,i}\rangle is the maximum among {h,gn,1,h,gn,2,,h,gn,K}\{\langle h^{*},g_{n,1}\rangle,\langle h^{*},g_{n,2}\rangle,...,\langle h^{*},g_{n,K}\rangle\}, then the optimal reflection coefficient of the nnth RIS element, denoted as gng_{n}^{*}, is gn,ig_{n,i}.

Proof.

Please refer to Appendix A.

According to  Theorem 1, among the KK choices for gng_{n} of the nnth RIS element, all we have to do is to find the one that has the largest h,gn,i\langle h^{*},g_{n,i}\rangle for i{1,2,,K}i\in\{1,2,...,K\}.

Also, according to (11), we have:

|gn,i|=|vn|β^i.|g_{n,i}|=|v_{n}|\hat{\beta}_{i}. (13)

Using (13), the right-hand side of (12) can now be updated as:

h,gn,i=|h||vn|β^icos(hgn,i)\langle h^{*},g_{n,i}\rangle=|h^{*}|\cdot|v_{n}|\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) (14)

in which gn,i=(vnβ^iejα^i)=vn+α^i\angle g_{n,i}=\angle(v_{n}\hat{\beta}_{i}e^{j\hat{\alpha}_{i}})=\angle v_{n}+\hat{\alpha}_{i} (from (11)).

As seen in (14), |h||h^{*}| and |vn||v_{n}| are the same for all the members of {h,gn,1,h,gn,2,,h,gn,K}\{\langle h^{*},g_{n,1}\rangle,\langle h^{*},g_{n,2}\rangle,...,\langle h^{*},g_{n,K}\rangle\}. Thus, finding the maximum h,gn,i\langle h^{*},g_{n,i}\rangle would be equivalent to finding the largest β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}). So if h\angle h^{*} is known, we can determine the optimal reflection coefficient for each RIS element (say the nnth RIS element) by finding the gn,ig_{n,i} with the largest β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}). From here on, we refer to the largest β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) as βncos(hgn)\beta_{n}^{*}\cos(\angle h^{*}-\angle g_{n}^{*}) and the corresponding gn,ig_{n,i} as gng_{n}^{*}.

III-B Converting Infinite Possibilities of h\angle h^{*} to a Finite Number of Possibilities

In Section III-A, we find the optimal reflection coefficient for each RIS element with given known h\angle h^{*}. However, in reality, h\angle h^{*} is unknown at the beginning. One method is to go through all possibilities of h\angle h^{*} to find the best one. Since there are infinite possibilities for h\angle h^{*}, this method will not be feasible. But as we will soon show, there is a way to go through a finite number of possibilities for h\angle h^{*} and still be able to find the global optimal solution. This is possible because gn(n=1,2,,N)g_{n}^{*}~{}(n=1,2,...,N) keep unchanged over a range of h\angle h^{*}.

To get a better picture, Fig. 3 shows an example for the nnth RIS element. In this example, there are K=4K=4 choices for θn\theta_{n}, where the four choices are selected randomly from the curve in Fig. 2. The resulting gn,1,gn,2,gn,3,gn,4g_{n,1},g_{n,2},g_{n,3},g_{n,4} in this example are 1.7279, 3.0369, 4.0841, 5.6549 rad, respectively. Recall that we should find the largest one among β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}), i{1,2,3,4}i\in\{1,2,3,4\}. Fig. 3 shows how the four curves β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) (i=1,2,3,4i=1,2,3,4) change with h[0,2π)\angle h^{*}\in[0,2\pi). For presentation brevity, we call curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) as “curve gn,i\angle g_{n,i}” in the legend of Fig. 3 and in the following discussion.

According to Section III-A, in Fig. 3 we are looking for the maximal of the four curves gn,i\angle g_{n,i} (i=1,2,3,4i=1,2,3,4) at each h\angle h^{*} value. The maximal of the four curves is the red curve shown in Fig. 3. In Fig. 3, o1,o2,o3,o4,p1,p2,p3,p4o_{1},o_{2},o_{3},o_{4},p_{1},p_{2},p_{3},p_{4} are h\angle h^{*} values of the intersections of the curves. From Fig. 3, when h\angle h^{*} changes from 0 to p1p_{1}, curve gn,4\angle g_{n,4} is always above the other three curves, and thus, gng^{*}_{n} remains unchanged (keeps as gn,4g_{n,4}). Similarly, when h\angle h^{*} changes within [p1,p2][p_{1},p_{2}], [p2,p3][p_{2},p_{3}], [p3,p4][p_{3},p_{4}], or [p4,2π)[p_{4},2\pi), gng^{*}_{n} remains as gn,1g_{n,1}, gn,2g_{n,2}, gn,3g_{n,3}, or gn,4g_{n,4}, respectively.

We refer to the interval of h\angle h^{*} (within [0,2π)[0,2\pi)) over which one curve stays above all other curves as the active interval of the curve. Thus, in Fig. 3, the active intervals of curves gn,1\angle g_{n,1}, gn,2\angle g_{n,2}, and gn,3\angle g_{n,3} are [p1,p2][p_{1},p_{2}], [p2,p3][p_{2},p_{3}], and [p3,p4][p_{3},p_{4}], respectively, and the active interval of curve gn,4\angle g_{n,4} is the union of two sub-intervals at the two sides of [0,2π)[0,2\pi): [p4,2π)[0,p1][p_{4},2\pi)\cup[0,p_{1}]. For presentation simplicity, we represent [p4,2π)[0,p1][p_{4},2\pi)\cup[0,p_{1}] as [p4,p1][p_{4},p_{1}]. As a summary, we have the following definition for an interval of h\angle h^{*} written as [x1,x2][x_{1},x_{2}] where x1,x2[0,2π)x_{1},x_{2}\in[0,2\pi):

  • if x1<x2x_{1}<x_{2}, then the interval is a continuous interval from x1x_{1} to x2x_{2} (e.g., the active intervals of curves gn,1\angle g_{n,1}, gn,2\angle g_{n,2}, and gn,3\angle g_{n,3});

  • if x1>x2x_{1}>x_{2}, then the interval is the union of two continuous sub-intervals located at the two sides of [0,2π)[0,2\pi), which is [x1,2π)[0,x2][x_{1},2\pi)\cup[0,x_{2}] (e.g., the active interval of gn,4\angle g_{n,4}).

In either case, x1x_{1} and x2x_{2} are called the left and right boundary of interval [x1,x2][x_{1},x_{2}].

We call the right boundary (h\angle h^{*} value) of the active interval of a curve gn,i\angle g_{n,i} as the active intersection of the curve. For example, p2p_{2} is the active intersection for curve gn,1\angle g_{n,1}, and p1p_{1} is the active intersection for curve gn,4\angle g_{n,4}.

Refer to caption
Figure 3: β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) versus h[0,2π)\angle h^{*}\in[0,2\pi) for the nnth RIS element.

Since gng_{n}^{*} (and θn\theta_{n}^{*}) remains unchanged if h\angle h^{*} is within an active interval, we do not need to go through the infinite possibilities of h\angle h^{*}. As long as we determine the active intervals, we can get gng_{n}^{*} (and θn\theta_{n}^{*}) in each active interval. For example, for Fig. 3, when h\angle h^{*} is within active interval [p1,p2][p_{1},p_{2}], [p2,p3][p_{2},p_{3}], [p3,p4][p_{3},p_{4}], and [p4,p1][p_{4},p_{1}], gng_{n}^{*} is gn,1g_{n,1}, gn,2g_{n,2}, gn,3g_{n,3}, and gn,4g_{n,4}, respectively.

We will show how to determine the active intervals of one RIS element in Section III-C, and extend the result to get the active intervals of all RIS elements in Section III-D.

III-C Determining Active Intervals of One RIS Element

For each RIS element, say the nnth element, its gng_{n} has KK choices: gn,1,gn,2,,gn,Kg_{n,1},g_{n,2},...,g_{n,K}, corresponding to KK curves called curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) (i=1,2,,Ki=1,2,...,K) over h[0,2π)\angle h^{*}\in[0,2\pi), similar to the curves in Fig. 3. Recall that gn,i=vn+α^i\angle g_{n,i}=\angle v_{n}+\hat{\alpha}_{i}.

To find the intersections (h\angle h^{*} values) of curves β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) and β^lcos(hgn,l)\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}), we need to solve the following equation:

β^icos(hgn,i)=β^lcos(hgn,l),il\displaystyle\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})=\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}),\ i\neq l (15)
\displaystyle\longrightarrow tan(h)=β^lcos(gn,l)β^icos(gn,i)β^isin(gn,i)β^lsin(gn,l).\displaystyle\tan(\angle h^{*})=\frac{\hat{\beta}_{l}\cos(\angle g_{n,l})-\hat{\beta}_{i}\cos(\angle g_{n,i})}{\hat{\beta}_{i}\sin(\angle g_{n,i})-\hat{\beta}_{l}\sin(\angle g_{n,l})}.

From (15), the two curves intersect at two points in the range of [0,2π)[0,2\pi): one at h=arctan(β^lcos(gn,l)β^icos(gn,i)β^isin(gn,i)β^lsin(gn,l))mod2π\angle h^{*}=\arctan(\frac{\hat{\beta}_{l}\cos(\angle g_{n,l})-\hat{\beta}_{i}\cos(\angle g_{n,i})}{\hat{\beta}_{i}\sin(\angle g_{n,i})-\hat{\beta}_{l}\sin(\angle g_{n,l})})\mod 2\pi and the other at h=(π+arctan(β^lcos(gn,l)β^icos(gn,i)β^isin(gn,i)β^lsin(gn,l)))mod2π\angle h^{*}=(\pi+\arctan(\frac{\hat{\beta}_{l}\cos(\angle g_{n,l})-\hat{\beta}_{i}\cos(\angle g_{n,i})}{\hat{\beta}_{i}\sin(\angle g_{n,i})-\hat{\beta}_{l}\sin(\angle g_{n,l})}))\mod 2\pi. Note that the two intersections are π\pi radians apart.

For the KK curves of the nnth RIS element, the total number of intersections will be 2(K2){2}{{K}\choose{2}}. Among all the intersections, only the active intersections matter to us. For example, as seen in Fig. 3, intersections o1o_{1} and o2o_{2} are not active and therefore not of any importance to our optimization goal.

Let us consider the intersections on a single curve. It has two intersections with any of the other K1K-1 curves. Thus, the total number of intersections on a single curve will be 2(K1)2(K-1). For example, in Fig. 3, since there are 4 curves in total, each curve has 2(41)=62(4-1)=6 intersections on it.

Considering the 2(K1)2(K-1) intersections on each curve, there will be an interval between every two consecutive intersections, making the total number of intervals on each curve being 2(K1)2(K-1).222The 2(K1)2(K-1) intersections partition range [0,2π)[0,2\pi) of h\angle h^{*} into 2(K1)+12(K-1)+1 intervals. As aforementioned, the union of the interval from 0 to the left-most intersection and the interval from the right-most intersection to 2π2\pi is viewed as a single interval. Note that by an “interval”, we mean an interval of h\angle h^{*}.

In the following theorem, we prove that the considered curve can have at most one active interval among all 2(K1)2(K-1) intervals.

Theorem 2.

For each curve of the nnth RIS element, at most one of the 2(K1)2(K-1) intervals is active.

Proof.

Please refer to Appendix B.

According to  Theorem 2, each curve will have at most one active interval. Recall that there are KK curves associated with each RIS element. Therefore, there will be at most KK active intervals for each RIS element. This means that the number of active intersections for each RIS element can at most be KK. Next, we will develop a method for finding the active intersections of each RIS element, say the nnth RIS element.

For the nnth RIS element, it has KK curves. Suppose In,i,lI_{n,i,l} is the interval at which curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) is above curve β^lcos(hgn,l)\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}), i.e., β^icos(hgn,i)>β^lcos(hgn,l)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})>\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}). In,i,lI_{n,i,l} can be determined with the help of the intersection points of the two curves as discussed at the beginning of Section III-C. For the example in Fig. 3, we have In,2,3=[o1,p3]I_{n,2,3}=[o_{1},p_{3}] and In,3,2=[p3,o1]I_{n,3,2}=[p_{3},o_{1}].

For curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}), its active interval denoted as In,iI_{n,i} can be determined as

In,i=l=1liKIn,i,l.\displaystyle I_{n,i}=\bigcap_{\begin{subarray}{c}l=1\\ l\neq i\end{subarray}}^{K}I_{n,i,l}. (16)

Note that \bigcap refers to the common range of intervals. The following function helps us find the common range of two intervals.

Common range calculator (CRC): The CRC function takes two intervals [l1,r1][l_{1},r_{1}] and [l2,r2][l_{2},r_{2}] as input intervals, and gets interval [l3,r3][l_{3},r_{3}] as the common range (CR) of the two input intervals. Here the intervals follow the interval definition in Section III-B, and ‘ll’ and ‘rr’ mean the left and right boundary of an interval, respectively. Table I summarizes the CRC calculation results for different cases (in which “No CR” means that the two input intervals do not have a range in common), and Fig. 4 provides a demonstration for each case in Table I.

By using the CRC, we can get In,1,In,2,,In,KI_{n,1},I_{n,2},...,I_{n,K}, i.e., the KK active intervals of the KK curves associated with the nnth RIS element. Based on the active intervals, we can get the active intersections for the active intervals of the nnth RIS element.

TABLE I: CRC Calculation Results
Index Case l3l_{3} r3r_{3}
II (l1<r1)&(l2<r2)(l_{1}<r_{1})\&(l_{2}<r_{2})
&max(l1,l2)<min(r1,r2)\&\max(l_{1},l_{2})<\min(r_{1},r_{2}) max(l1,l2)\max(l_{1},l_{2}) min(r1,r2)\min(r_{1},r_{2})
IIII (l1<r1)&(l2<r2)(l_{1}<r_{1})\&(l_{2}<r_{2})
&max(l1,l2)>min(r1,r2)\&\max(l_{1},l_{2})>\min(r_{1},r_{2}) No CR No CR
IIIIII (l1>r1)&(l2>r2)(l_{1}>r_{1})\&(l_{2}>r_{2}) max(l1,l2)\max(l_{1},l_{2}) min(r1,r2)\min(r_{1},r_{2})
IVIV (l1>r1)&(l2<r2)(l_{1}>r_{1})\&(l_{2}<r_{2})
&(r2<l1)&(r1<l2)\&(r_{2}<l_{1})\&(r_{1}<l_{2}) No CR No CR
VV (l1>r1)&(l2<r2)(l_{1}>r_{1})\&(l_{2}<r_{2})
&(r2>l1)\&(r_{2}>l_{1}) max(l1,l2)\max(l_{1},l_{2}) max(r1,r2)\max(r_{1},r_{2})
VIVI (l1>r1)&(l2<r2)(l_{1}>r_{1})\&(l_{2}<r_{2})
&(l2<r1)\&(l_{2}<r_{1}) min(l1,l2)\min(l_{1},l_{2}) min(r1,r2)\min(r_{1},r_{2})
VIIVII (l1<r1)&(l2>r2)(l_{1}<r_{1})\&(l_{2}>r_{2})
&(r2<l1)&(r1<l2)\&(r_{2}<l_{1})\&(r_{1}<l_{2}) No CR No CR
VIIIVIII (l1<r1)&(l2>r2)(l_{1}<r_{1})\&(l_{2}>r_{2})
&(r2>l1)\&(r_{2}>l_{1}) min(l1,l2)\min(l_{1},l_{2}) min(r1,r2)\min(r_{1},r_{2})
IXIX (l1<r1)&(l2>r2)(l_{1}<r_{1})\&(l_{2}>r_{2})
&(l2<r1)\&(l_{2}<r_{1}) max(l1,l2)\max(l_{1},l_{2}) max(r1,r2)\max(r_{1},r_{2})
Refer to caption
Refer to caption
Figure 4: Demonstration for the nine cases in Table I.

III-D Determining the Active Intersections of All Elements

After we get the active intersections for the nnth RIS element, we can quickly get active intersections for any other RIS element, say the mmth RIS element, as follows.

Since gn,i=vn+α^i\angle g_{n,i}=\angle v_{n}+\hat{\alpha}_{i}, we have:

β^icos(hgn,i)=β^icos(hvnα^i).\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})=\hat{\beta}_{i}\cos(\angle h^{*}-\angle v_{n}-\hat{\alpha}_{i}). (17)

So β^icos(hvnα^i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle v_{n}-\hat{\alpha}_{i}) is the iith curve of the nnth RIS element. If we shift this curve to the left by vnvm\angle v_{n}-\angle v_{m} (i.e., we replace h\angle h^{*} with h+vnvm\angle h^{*}+\angle v_{n}-\angle v_{m}), we will get curve β^icos(h+vnvmvnα^i)\hat{\beta}_{i}\cos(\angle h^{*}+\angle v_{n}-\angle v_{m}-\angle v_{n}-\hat{\alpha}_{i}) = β^icos(hvmα^i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle v_{m}-\hat{\alpha}_{i}) = β^icos(hgm,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{m,i}), which is the iith curve of the mmth RIS element. Therefore, if we shift all the curves of the nnth RIS element by the constant value of vnvm\angle v_{n}-\angle v_{m}, we will end up with the curves of the mmth RIS element. As a result, the active intersections of the mmth RIS element would be the active intersections of the nnth RIS element shifted by vnvm\angle v_{n}-\angle v_{m}. Thus, simply by shifting, we can calculate the active intersections of the rest of the elements.

III-E Determining Overall Optimal Reflection Coefficients

In Section III-D, we have determined the active intersections for all RIS elements. Since each RIS element has at most KK active intervals and at most KK active intersections, there will be at most NKNK active intersections in total for all NN RIS elements. The active intersections partition the range [0,2π)[0,2\pi) of h\angle h^{*} into at most NKNK regions. As discussed in Section III-B, when h\angle h^{*} is within one region, θ1,θ2,,θN\theta_{1}^{*},\theta_{2}^{*},...,\theta_{N}^{*} remain unchanged. Thus, we only need to go through at most NKNK regions of h\angle h^{*}, find θ1,θ2,,θN\theta_{1}^{*},\theta_{2}^{*},...,\theta_{N}^{*} and the corresponding channel capacity CC when h\angle h^{*} is within each region, and pick up the largest channel capacity and select the θ1,θ2,,θN\theta_{1}^{*},\theta_{2}^{*},...,\theta_{N}^{*} in the corresponding region as the overall optimal reflection coefficients for the NN RIS elements. Since we only need to go through at most NKNK regions, our method has a complexity linear with NN and KK.

IV Configuration Set Selection

In Section III, we have demonstrated how capacity maximization is performed for any channel realization of the system, assuming a given configuration set, i.e., a set of KK reflection coefficient choices {β^1ejα^1,β^2ejα^2,,β^Kejα^K}\{{\hat{\beta}_{1}e^{j\hat{\alpha}_{1}}},{\hat{\beta}_{2}e^{j\hat{\alpha}_{2}}},...,{\hat{\beta}_{K}e^{j\hat{\alpha}_{K}}}\} as shown in (9). In this section, our target is to select the optimal configuration set for the considered system such that the expected capacity is maximized.

In the literature, most of the works assume that the phase shifts of the reflection coefficient choices are evenly spaced [29, 30, 31, 32], i.e., {α^1,α^2,,α^K}={0,2πK,4πK,2π(K1)K}\{\hat{\alpha}_{1},\hat{\alpha}_{2},...,\hat{\alpha}_{K}\}=\{0,\frac{2\pi}{K},\frac{4\pi}{K}...,\frac{2\pi(K-1)}{K}\}. This setting is reasonable when the amplitude and phase shift of a reflection coefficient can be adjusted independently. However, in our system, the amplitude and phase shift are coupled. Thus, in general, a configuration set with evenly spaced phase shifts of the reflection coefficients does not guarantee optimality.

Since amplitude is a function of phase shift, selecting a configuration set is equivalent to selecting KK phase shifts: α^1,α^2,,α^K\hat{\alpha}_{1},\hat{\alpha}_{2},...,\hat{\alpha}_{K}. As we can see in Fig. 2, the phase shifts can be any value within [0,2π)[0,2\pi). Thus, theoretically, there are an infinite number of possible configuration sets for the system. To make the setup feasible, we consider selecting KK phase shifts from a large number, denoted as MM (MKM\gg K), of evenly spaced phase shifts over the range [0,2π)[0,2\pi). We have the following notation definitions.

  • Denote the set of MM evenly spaced phase shifts as Ω\Omega.

  • Define a KK-size subset of Ω\Omega as a subset of Ω\Omega with the size of the subset being KK.

  • Define Φ\Phi as the set of all KK-size subsets of Ω\Omega.

So we have |Φ|=(MK)|\Phi|={M\choose K}. Therefore, our objective is to select a KK-size subset of Ω\Omega, denoted Ψ\Psi, such that the expected capacity of the system is maximized. In the sequel, Ψ\Psi is also called a configuration set of the system.

The configuration set selection problem can be formulated as

Ψ=argmaxΨΦ\displaystyle\Psi^{*}=\operatorname*{arg\,max}_{\Psi\in\Phi} 𝔼(CΨ),\displaystyle\mathbb{E}(C_{\Psi}^{*}), (18)

where CΨC_{\Psi}^{*} is the maximal capacity over a channel realization of the system with the configuration set Ψ\Psi, and the expectation 𝔼()\mathbb{E}(\cdot) is over all channel realizations of the system.

An intuitive method to solve the configuration set selection problem in (18) is to use Monte Carlo simulations, referred to as Monte Carlo Simulation based (MCSB) method. In this method, we go through all (MK){M}\choose{K} options of Ψ\Psi. For each option, we simulate a large number, denoted RR, of channel realizations of the system. For each realization, we calculate the maximum capacity using the method described in Section III. Then for the option, we average the achievable maximal capacity associated over all RR channel realizations. In the end, we select the option with the largest average achievable capacity.

In MCSB method, a large number of channel realizations are required for each of the (MK){M}\choose{K} options of Ψ\Psi. Moreover, at each channel realization, the capacity maximization method in Section III must be applied which has a complexity of 𝒪(NK)\mathcal{O}(NK). Thus, the total complexity of the MCSB method is 𝒪((MK)RNK)\mathcal{O}({{M}\choose{K}}RNK), which is time-consuming. Next, we will propose a much faster method that does not require any Monte Carlo simulations and has insights.

IV-A Integral Maximization Based (IMB) Configuration Set Selection

Before we present our method, we introduce two functions Fn(h)F_{n}(\angle h^{*}) and S(h)S(\angle h^{*}) as follows.

In Section III, for the nnth RIS element, we have shown that we should get the maximal of curves β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}), i{1,2,,K}i\in\{1,2,...,K\} for any h[0,2π)\angle h^{*}\in[0,2\pi) . Accordingly, for the nnth RIS element, we can define Fn(h)F_{n}(\angle h^{*}) as the maximal of the curves for a given h\angle h^{*} value, as

Fn(h)=maxi{1,2,,K}β^icos(hgn,i).F_{n}(\angle h^{*})=\max_{i\in\{1,2,...,K\}}\ \hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}). (19)

Fig. 5 shows an example of function Fn(h)F_{n}(\angle h^{*}) with K=4K=4. For an RIS with NN elements, we have NN different functions: F1(h),F2(h),,FN(h)F_{1}(\angle h^{*}),F_{2}(\angle h^{*}),...,F_{N}(\angle h^{*}).

Also define function S(h)S(\angle h^{*}) as

S(h)=maxi{1,2,,K}β^icos(hα^i).S(\angle h^{*})=\max_{i\in\{1,2,...,K\}}\ \hat{\beta}_{i}\cos(\angle h^{*}-\hat{\alpha}_{i}). (20)

Accordingly, we have

S(h)\displaystyle S(\angle h^{*}) =maxi{1,2,,K}β^icos(hα^i)\displaystyle=\max_{i\in\{1,2,...,K\}}\ \hat{\beta}_{i}\cos(\angle h^{*}-\hat{\alpha}_{i}) (21)
=maxi{1,2,,K}β^icos(hα^ivn+vn)\displaystyle=\max_{i\in\{1,2,...,K\}}\ \hat{\beta}_{i}\cos(\angle h^{*}-\hat{\alpha}_{i}-\angle v_{n}+\angle v_{n})
=maxi{1,2,,K}β^icos(hgn,i+vn)\displaystyle=\max_{i\in\{1,2,...,K\}}\ \hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}+\angle v_{n})
=Fn(h+vn).\displaystyle=F_{n}(\angle h^{*}+\angle v_{n}).

Equation (21) means that if we shift curve Fn(h)F_{n}(\angle h^{*}), for any nn, to the left by vn\angle v_{n}, then we can get curve S(h)S(\angle h^{*}). From (21), we also have

Fn(h)=S(hvn).F_{n}(\angle h^{*})=S(\angle h^{*}-\angle v_{n}). (22)

Next, we introduce our method for configuration set selection.

Consider CΨC_{\Psi}^{*} (the maximal capacity over a channel realization of the system with the configuration set Ψ\Psi). For presentation simplicity, we omit subscript ‘Ψ\Psi’ and write CΨC_{\Psi}^{*} as CC^{*} in the sequel. For CC^{*}, its expectation over channel realizations is expressed as

𝔼(C)=\displaystyle\mathbb{E}(C^{*})= 𝔼(Blog2(1+P|h|2BN0))\displaystyle\mathbb{E}(B\log_{2}(1+\frac{P|h^{*}|^{2}}{BN_{0}})) (23)
=\displaystyle= B𝔼(log2(1+P|h|2BN0))\displaystyle B\ \mathbb{E}(\log_{2}(1+\frac{P|h^{*}|^{2}}{BN_{0}}))
=\displaystyle= BlimR1Rr=1Rlog2(1+P|hr|2BN0)\displaystyle B\lim_{R\rightarrow\infty}\frac{1}{R}\sum_{r=1}^{R}\log_{2}(1+\frac{P|h_{r}^{*}|^{2}}{BN_{0}})

in which RR (a very large number) is the number of channel realizations, and hrh_{r}^{*} means the optimal overall channel from the transmitter to the receiver in the rrth realization.

Refer to caption
Figure 5: An example demonstrating Fn(h)F_{n}(\angle h^{*})

The term |hr||h_{r}^{*}| in (23) is expressed as:

|hr|=|h0+n=1Ngn|\displaystyle|h_{r}^{*}|=\left|h_{0}+\sum_{n=1}^{N}g_{n}^{*}\right| (24)
=|h0,hr+n=1Ngn,hr|hr||\displaystyle=\left|\frac{\langle h_{0},h_{r}^{*}\rangle+\sum_{n=1}^{N}\langle g_{n}^{*},h_{r}^{*}\rangle}{|h_{r}^{*}|}\right|
=||h0|cos(hrh0)+n=1N|vn|βncos(hrgn)|\displaystyle=\left||h_{0}|\cos(\angle h_{r}^{*}-\angle h_{0})+\sum_{n=1}^{N}|v_{n}|\beta_{n}^{*}\cos(\angle h_{r}^{*}-\angle g_{n}^{*})\right|
=||h0|cos(hrh0)+n=1N|vn|Fn(hr)|.\displaystyle=\left||h_{0}|\cos(\angle h_{r}^{*}-\angle h_{0})+\sum_{n=1}^{N}|v_{n}|F_{n}(\angle h_{r}^{*})\right|.

We assume |v1|,|v2|,,|vN||v_{1}|,|v_{2}|,...,|v_{N}| are approximately the same and are equal to constant cc.333This is a common assumption when RIS is at the far field of the transmitter and the receiver [26]. Also assuming a weak direct path (|h0|0|h_{0}|\approx 0), |hr||h_{r}^{*}| can be further expressed from (24) as

|hr|\displaystyle|h_{r}^{*}| |cn=1NFn(hr)|\displaystyle\approx\left|c\sum_{n=1}^{N}F_{n}(\angle h_{r}^{*})\right| (25)
=(i)|cn=1NS(hrvn)|\displaystyle\overset{\text{(i)}}{=}\left|c\sum_{n=1}^{N}S(\angle h_{r}^{*}-\angle v_{n})\right|
(ii)|cN2π02πS(x)𝑑x|.\displaystyle\overset{\text{(ii)}}{\approx}\left|\frac{cN}{2\pi}\int_{0}^{2\pi}S(x)\,dx\right|.

Here step (i) is from (22), and step (ii) is to use the integral to replace Riemann sum. As we can see in (25), |hr||h_{r}^{*}| is proportional to |02πS(x)𝑑x||\int_{0}^{2\pi}S(x)\,dx|.

Theorem 3.

02πS(x)𝑑x\int_{0}^{2\pi}S(x)\,dx is always non negative.

Proof.

From (20) we have

S(h)=maxi{1,2,,K}β^icos(hα^i)β^1cos(hα^1)\displaystyle S(\angle h^{*})=\max_{i\in\{1,2,...,K\}}\ \hat{\beta}_{i}\cos(\angle h^{*}-\hat{\alpha}_{i})\geq\hat{\beta}_{1}\cos(\angle h^{*}-\hat{\alpha}_{1})
02πS(x)𝑑x02πβ^1cos(xα^1)𝑑x=0\displaystyle\longrightarrow\int_{0}^{2\pi}S(x)\,dx\geq\int_{0}^{2\pi}\hat{\beta}_{1}\cos(x-\hat{\alpha}_{1})\,dx=0
02πS(x)𝑑x0\displaystyle\longrightarrow\int_{0}^{2\pi}S(x)\,dx\geq 0

According to (23), (25) we have

𝔼(C)=BlimR1Rr=1Rlog2(1+P|hr|2BN0)\displaystyle\mathbb{E}(C^{*})=B\lim_{R\rightarrow\infty}\frac{1}{R}\sum_{r=1}^{R}\log_{2}(1+\frac{P|h_{r}^{*}|^{2}}{BN_{0}}) (26)
=\displaystyle= BlimR1Rr=1Rlog2(1+P|cN2π02πS(x)𝑑x|2BN0)\displaystyle B\lim_{R\rightarrow\infty}\frac{1}{R}\sum_{r=1}^{R}\log_{2}(1+\frac{P|\frac{cN}{2\pi}\int_{0}^{2\pi}S(x)\,dx|^{2}}{BN_{0}})
=\displaystyle= BlimR1Rr=1Rlog2(1+Pc2N2|02πS(x)𝑑x|24π2BN0)\displaystyle B\lim_{R\rightarrow\infty}\frac{1}{R}\sum_{r=1}^{R}\log_{2}(1+\frac{Pc^{2}N^{2}|\int_{0}^{2\pi}S(x)\,dx|^{2}}{4\pi^{2}BN_{0}})
=\displaystyle= BlimR1RRlog2(1+Pc2N2|02πS(x)𝑑x|24π2BN0)\displaystyle B\lim_{R\rightarrow\infty}\frac{1}{R}R\log_{2}(1+\frac{Pc^{2}N^{2}|\int_{0}^{2\pi}S(x)\,dx|^{2}}{4\pi^{2}BN_{0}})
=\displaystyle= Blog2(1+Pc2N2|02πS(x)𝑑x|24π2BN0)\displaystyle B\log_{2}(1+\frac{Pc^{2}N^{2}|\int_{0}^{2\pi}S(x)\,dx|^{2}}{4\pi^{2}BN_{0}})
=\displaystyle= Blog2(1+Pc2N2(02πS(x)𝑑x)24π2BN0),\displaystyle B\log_{2}(1+\frac{Pc^{2}N^{2}(\int_{0}^{2\pi}S(x)\,dx)^{2}}{4\pi^{2}BN_{0}}),

in which the last equality comes from the fact that 02πS(x)𝑑x\int_{0}^{2\pi}S(x)\,dx is always non-negative (Theorem 3). According to (26), since log(x)\log(x) is an increasing function, maximizing 𝔼(C)\mathbb{E}(C^{*}) would be equivalent to maximizing 02πS(x)𝑑x\int_{0}^{2\pi}S(x)\,dx, which is a major insight of our method.

So in our method, when we go through all (MK){M}\choose{K} options of Ψ\Psi, we no longer need to consider Monte Carlo simulations with a large number of channel realizations. For each option, we only need to compute 02πS(x)𝑑x\int_{0}^{2\pi}S(x)\,dx. The Ψ\Psi option corresponding to the largest 02πS(x)𝑑x\int_{0}^{2\pi}S(x)\,dx will be considered as the optimal configuration set. Since our method finds the maximal integral of S(x)S(x), we call our method Integral Maximization Based (IMB) method.

Refer to caption
Figure 6: Curve βn(αn)\beta_{n}(\alpha_{n}) vs. αn\alpha_{n} over phase shift range [ϕπ,ϕ+π)[\phi^{\prime}-\pi,\phi^{\prime}+\pi), with an example to choose M=20M=20 points on the curve.

IV-B Search Space Compression (SSC)

In Section IV-A, given Ω\Omega (a set of MM evenly distributed phase shifts over range [0,2π[0,2\pi)), the proposed IMB method selects the best option of Ψ\Psi among all (MK){M}\choose{K} options. Next, we show how we should pick up the MM evenly distributed phase shifts over range [0,2π)[0,2\pi).

Picking up MM evenly distributed phase shifts over range [0,2π)[0,2\pi) is actually picking up MM points on the curve in Fig. 2 over phase shift range [0,2π)[0,2\pi). Note that in Fig. 2, the curve βn(αn)\beta_{n}(\alpha_{n}) is symmetric over line αn=ϕ\alpha_{n}=\phi^{\prime} (in which ϕ=ϕ+π2\phi^{\prime}=\phi+\frac{\pi}{2} with ϕ\phi being a constant) if we view the range of αn\alpha_{n} as (,)(-\infty,\infty). Thus, we select to pick up MM points on the curve over phase shift range [ϕπ,ϕ+π)[\phi^{\prime}-\pi,\phi^{\prime}+\pi), as shown in Fig. 6.444Picking up MM points over phase shift range [0,2π)[0,2\pi) is equivalent to picking up MM points over phase shift range [ϕπ,ϕ+π)[\phi^{\prime}-\pi,\phi^{\prime}+\pi). The curve in Fig. 6 is perfectly symmetric over line αn=ϕ\alpha_{n}=\phi^{\prime}.

To pick up MM points on the symmetric curve in Fig. 6, intuitively the MM points should be symmetric over line αn=ϕ\alpha_{n}=\phi^{\prime}. Accordingly, Ω\Omega should be expressed as

Ω={ϕπ+πM,ϕπ+3πM,,ϕπ+(2M1)πM}.\displaystyle\Omega=\{\phi^{\prime}-\pi+\frac{\pi}{M},\phi^{\prime}-\pi+\frac{3\pi}{M},...,\phi^{\prime}-\pi+\frac{(2M-1)\pi}{M}\}. (27)

Fig. 6 also shows an example of how M=20M=20 points can be chosen on the curve.

Recall that for a given Ω\Omega, our proposed IMB method should go through all (MK){M}\choose{K} options of Ψ\Psi. Next, we show that for Ω\Omega given in (27), some options of Ψ\Psi yield the same 02πS(x)𝑑x\int_{0}^{2\pi}S(x)\,dx, and thus, we actually do not have to go through all (MK){M}\choose{K} options.

Consider an option Ψ\Psi from Φ\Phi (recalling that Φ\Phi is the set of all KK-size subsets of Ω\Omega). In Ψ\Psi, we have KK phase shifts, which are corresponding to KK points (among the MM points) on the curve in Fig. 6. For presentation simplicity, we denote Ψ\Psi as Ψ={ψ1,ψ2,,ψK}\Psi=\{\psi_{1},\psi_{2},...,\psi_{K}\}, in which ψ1,ψ2,,ψK\psi_{1},\psi_{2},...,\psi_{K} are the KK reflection coefficient choices of option Ψ\Psi (which are also KK points among the MM points on the curve in Fig. 6). Now consider another option from Φ\Phi, denoted Ψ={ψ1,ψ2,,ψK}\Psi^{\dagger}=\{\psi^{\dagger}_{1},\psi^{\dagger}_{2},...,\psi^{\dagger}_{K}\}, in which point ψk\psi^{\dagger}_{k} and point ψk\psi_{k} (k=1,2,,Kk=1,2,...,K) are symmetric over the symmetric line αn=ϕ\alpha_{n}=\phi^{\prime} in Fig. 6. In other words, ψk\psi^{\dagger}_{k} and ψk\psi_{k} have the same amplitude but their phases are mirrored over the symmetric line, i.e., ψk+ψk2=ϕ+π2\frac{\angle\psi^{\dagger}_{k}+\angle\psi_{k}}{2}=\phi+\frac{\pi}{2}. We say option Ψ\Psi^{\dagger} and option Ψ\Psi are mirrored option to each other. For the two options, the S(x)S(x) function is denoted as SΨ(x)S_{\Psi}(x) and SΨ(x)S_{\Psi^{\dagger}}(x), respectively. We have

SΨ(x)\displaystyle S_{\Psi^{\dagger}}(x) =maxk=1,2,,K|ψk|cos(xψk)\displaystyle=\max_{k=1,2,...,K}|\psi^{\dagger}_{k}|\cos(x-\angle\psi^{\dagger}_{k}) (28)
=maxk=1,2,,K|ψk|cos(x(2ϕ+πψk))\displaystyle=\max_{k=1,2,...,K}|\psi_{k}|\cos(x-(2\phi+\pi-\psi_{k}))
=maxk=1,2,,K|ψk|cos((2ϕ+πx)ψk)\displaystyle=\max_{k=1,2,...,K}|\psi_{k}|\cos((2\phi+\pi-x)-\psi_{k})
=SΨ(2ϕ+πx).\displaystyle=S_{\Psi}(2\phi+\pi-x).

Then we have

02πSΨ(x)𝑑x\displaystyle\int_{0}^{2\pi}S_{\Psi^{\dagger}}(x)\,dx =02πSΨ(2ϕ+πx)𝑑x\displaystyle=\int_{0}^{2\pi}S_{\Psi}(2\phi+\pi-x)\,dx (29)
=(iii)2π0SΨ(2ϕ+π+x)𝑑x\displaystyle\overset{\text{(iii)}}{=}\int_{-2\pi}^{0}S_{\Psi}(2\phi+\pi+x)\,dx
=(iv)2ϕπ2ϕ+πSΨ(x)𝑑x\displaystyle\overset{\text{(iv)}}{=}\int_{2\phi-\pi}^{2\phi+\pi}S_{\Psi}(x)\,dx
=(v)02πSΨ(x)𝑑x.\displaystyle\overset{\text{(v)}}{=}\int_{0}^{2\pi}S_{\Psi}(x)\,dx.

Here in step (iii) we replace x-x by xx, in step (iv) we replace 2ϕ+π+x2\phi+\pi+x with xx, and in step (v) we use the fact that SΨ(x)S_{\Psi}(x) is a periodical function with period 2π2\pi.

Equation (29) shows that for the two options Ψ\Psi and Ψ\Psi^{\dagger}, the integral of SΨ(x)S_{\Psi}(x) and SΨ(x)S_{\Psi^{\dagger}}(x) are the same. Thus, we only need to check one of the two options. We call this as Search Space Compression (SSC).

  • If KK is an even number, then among all (MK){M}\choose{K} options of Ψ\Psi, some options are identical to their mirrored options, and the number of such options is (M2K2){\lfloor\frac{M}{2}\rfloor}\choose{\frac{K}{2}}. Thus, the total number of options of Ψ\Psi that need to be checked is (M2K2)+(MK)(M2K2)2{{\lfloor\frac{M}{2}\rfloor}\choose{\frac{K}{2}}}+\frac{{{M}\choose{K}}-{{\lfloor\frac{M}{2}\rfloor}\choose{\frac{K}{2}}}}{2}, which is approximately (MK)/2{{{M}\choose{K}}}/{2} since MM is large.

  • If KK is an odd number, there will be two cases. When MM is even, each option is different from its mirrored option, and thus, the total number of options of Ψ\Psi that need to be checked is (MK)/2{{{M}\choose{K}}}/{2}. When MM is odd, the number of options of Ψ\Psi that are identical to their mirrors is (M12K12){{\frac{M-1}{2}}\choose{\frac{K-1}{2}}}. Thus, the total number of options of Ψ\Psi that need to be checked will be (M12K12)+(MK)(M12K12)2{{\frac{M-1}{2}}\choose{\frac{K-1}{2}}}+\frac{{{M}\choose{K}}-{{\frac{M-1}{2}}\choose{\frac{K-1}{2}}}}{2}, which is approximately (MK)/2{{{M}\choose{K}}}/{2} since MM is large.

Therefore, by the SSC method, the number of options that should be checked is cut approximately by half.

V Numerical Results

In this section, we will evaluate the performance of our proposed methods for capacity maximization and configuration set selection. In the following simulations, the parameters are set according to Table II unless specified otherwise.

V-A Capacity Maximization

We simulate our capacity maximization method in Section III as well as three benchmark methods as follows.

  • Exhaustive search method: we go through all KNK^{N} possibilities of {θ1,θ2,,θN\theta_{1},\theta_{2},...,\theta_{N}} to find the optimal phases.

  • Closest point projection (CPP): CPP is a heuristic algorithm used in [26]. The idea of this algorithm is to align all RIS channels toward the direct channel as much as possible. In other words, gng_{n}^{*} would be the one that maximizes cos(h0gn,i)\cos(\angle h_{0}-\angle g_{n,i}).

  • Improved CPP: In the CPP method in [26], phase shift and amplitude of a reflection coefficient can be independently adjusted. Since we consider βn\beta_{n} and αn\alpha_{n} to be coupled, we make some changes to the original CPP method by using our result in Theorem 1 as follows. Instead of aligning all RIS channels toward the direct channel, we maximize the inner product of each RIS channel with the direct channel. This means we are maximizing the projection of all RIS channels on the direct channel. Thus, gng_{n}^{*} will now be the one that maximizes β^icos(h0gn,i)\hat{\beta}_{i}\cos(\angle h_{0}-\angle g_{n,i}). This method is called improved CPP.

TABLE II: Parameter values for simulation results
Parameter Value Parameter Value
BB 11  MHz PBN0\frac{P}{BN_{0}} 100100  dB
βmin\beta_{\min} 0.20.2 ϕ\phi 0.43π0.43\pi
κ\kappa 1.61.6 MM 2020
|vn||v_{n}| 140-140  dB vn\angle v_{n} \sim Uniform[0,2π)[0,2\pi)
|h0||h_{0}| 140-140  dB h0\angle h_{0} 0

In our simulations, the reflection coefficient of each RIS element is chosen from KK choices as shown in (9), while the KK choices have evenly distributed phase shifts, i.e., α^k=(k1)×2πK,k=1,2,,K\hat{\alpha}_{k}=\frac{(k-1)\times 2\pi}{K},k=1,2,...,K.

Fig. 7 shows how capacity changes with the number of elements for different algorithms with K=2K=2. According to (2) and (5), we expect the capacity to be an increasing function of NN, which is verified by the four curves in Fig. 7. As seen in Fig. 7, for all values of NN, our proposed method yields the same capacity as the exhaustive search method, which means that our method can achieve optimality with linear complexity. The original CPP and the improved CPP have the same performance. This happens because KK is set to two, hence α^1\hat{\alpha}_{1} and α^2\hat{\alpha}_{2} are π\pi radians apart. Thus, cos(h0gn,1)\cos(\angle h_{0}-\angle g_{n,1}) and cos(h0gn,2)\cos(\angle h_{0}-\angle g_{n,2}) will have opposite signs. As a result, the amplitude no longer matters.

Refer to caption
Figure 7: Capacity versus the number of elements (with K=2K=2 choices of reflection coefficients).

In Fig. 8, KK is set to 4. Our method and the exhaustive search method still have the optimal performance. By using our result in Theorem 1, improved CPP outperforms the original CPP but still yields a suboptimal solution.

Refer to caption
Figure 8: Capacity versus the number of elements (with K=4K=4 choices of reflection coefficients).

In Fig. 9, we examine how the strength of the direct channel affects the performance of the mentioned methods. According to (2), (5), the capacity is expected to be an increasing function of |h0||h_{0}|. At |h0|=140dB|h_{0}|=-140~{}\text{dB}, the proposed method has a noticeable advantage over the improved CPP. But as |h0||h_{0}| increases, the gap between the two diminishes. The reason is that when the direct channel becomes stronger, h0h_{0} will become the dominant term in (2), and thus, its phase and amplitude greatly affect hh^{*}. As a result, h0\angle h_{0} will become an appropriate approximation for h\angle h^{*}. In other words, the improved CPP would be a proper estimate for our proposed method when the direct channel is strong.

Refer to caption
Figure 9: Capacity versus |h0||h_{0}|.

V-B Configuration Set Selection

Next, we use simulations to evaluate the performance of our configuration set selection method IMB as well as the IMB method enhanced with the SSC method (denoted as “IMB+SSC”). As a comparison, we also simulate two other methods: 1) the MCSB method with R=1,000R=1,000 channel realizations for each simulation setup, and 2) the evenly distributed configuration set selection method in which the phase shifts of the reflection coefficients in the configuration set are evenly distributed (i.e., {α^1,α^2,,α^K}={0,2πK,4πK,2π(K1)K}\{\hat{\alpha}_{1},\hat{\alpha}_{2},...,\hat{\alpha}_{K}\}=\{0,\frac{2\pi}{K},\frac{4\pi}{K}...,\frac{2\pi(K-1)}{K}\}).

Fig. 10 demonstrates the performance of different methods used for configuration set selection. As the MCSB method uses Monte Carlo Simulations, it can be viewed as the optimal method. As we can see in Fig. 10, MCSB achieves the maximal capacity, while our IMB and IMB+SSC have the same performance with almost negligible difference from the performance of MCSB, which means that our IMB and IMB+SSC achieve an almost-optimal performance, and the SSC method reduces search space without any performance degradation. The evenly distributed configuration set selection method has less capacity than MCSB, IMB, and IMB+SSC.

Since IMB and IMB+SSC have the same capacity performance, we do not show the results of IMB+SSC in Figs. 11-13.

Fig. 11 depicts how capacity changes with KK in our IMB method and the evenly distributed configuration set selection method. The performance of MCSB method is not shown in this figure, due to the prohibitive simulation time needed for the MCSB method. As we expected, in both IMB and the evenly distributed configuration set selection methods, increasing KK would provide us with a capacity gain. The gain is large for small values of KK (e.g. from K=2K=2 to K=4K=4). This suggests that increasing KK to a large number would be unnecessary and considering small values for KK (e.g., K=8K=8) would be sufficient.

Refer to caption
Figure 10: Capacity versus the number of elements for different configuration set selection methods.
Refer to caption
Figure 11: Capacity versus the number of choices of reflection coefficients (K)(K)

Next, we will discuss how the parameters in the reflection coefficient model in (8) influences the performance of different configuration set selection methods.

Fig. 12 shows how βmin\beta_{\min} affects the capacity of the configuration set selection methods. According to (8), βmin\beta_{\min} represents the amount of loss in an RIS element. High βmin\beta_{\min} indicates that the element has low loss whereas low βmin\beta_{\min} implies that the element is quite lossy. Thus, we expect the capacity to be an increasing function of βmin\beta_{\min} for all methods. When βmin=1\beta_{\min}=1, equation (8) reduces to βn(αn)=1\beta_{n}(\alpha_{n})=1, which means that the amplitude βn\beta_{n} and phase shift αn\alpha_{n} are not coupled anymore, and thus, any set of KK reflection coefficients whose phase shifts are evenly spaced would be the optimal solution. So all methods yield the same capacity at βmin=1\beta_{\min}=1. It can also be observed that our proposed method (IMB) is most effective when the RIS elements are highly lossy, i.e., when βmin\beta_{\min} is small.

Refer to caption
Figure 12: Capacity versus βmin\beta_{\min}.

We can see the effect of κ\kappa of (8) on the performances of the configuration set selection methods in Fig. 13. Similar to βmin\beta_{\min}, κ\kappa is also an indicator of the degree of loss in an RIS element. In contrast to βmin\beta_{\min}, the value κ\kappa is proportional to the amount of loss. As a result, we expect the achievable capacity to be a decreasing function of κ\kappa. Similar to Fig. 12, in the lossless scenario (κ=0\kappa=0), we have βn(αn)=1\beta_{n}(\alpha_{n})=1, and thus, all methods achieve the same capacity.

Refer to caption
Figure 13: Capacity versus κ\kappa.

Next, we demonstrate the benefit of IMB+SSC compared to IMB. Fig. 14 demonstrates the processing time for IMB and IMB+SSC. The processing time is defined as the time that a method takes during determining the configuration set. As we can see, the IMB+SSC method is almost twice as fast as the original IMB. Fig. 15 shows the number of options of Ψ\Psi that each method has to go through. As we expected, when SSC is applied to IMB, the number of searched options gets almost halved resulting in a more compact search space.

Refer to caption
Figure 14: Processing time versus MM.
Refer to caption
Figure 15: Number of searched options of Ψ\Psi versus MM.

VI Conclusion

In this paper, we work on the discrete reflection optimization of RIS elements. In contrast to most works in the literature, we consider a practical setup in which the amplitude and the phase shift of RIS elements are coupled. To maximize the capacity of a system with a given configuration set, we develop an algorithm that yields the global optimal reflection coefficients of RIS elements with linear complexity. We also develop an efficient method called “IMB” that finds the optimal configuration set. Our method is based on our insightful finding that maximizing the average system capacity is approximately equivalent to maximizing the integral 02πS(x)𝑑x\int_{0}^{2\pi}S(x)\,dx. Numerical results show that our capacity maximization method and configuration selection method have apparent gains in terms of channel capacity. In this work, we investigate a single-user setup. However, as discussed in Section I, techniques such as RIS partitioning and/or distributed RIS deployment can help us straightforwardly extend our methods to multi-user setups.

Appendix A Proof of Theorem 1

We use proof by contradiction. Assume gngn,ig_{n}^{*}\neq g_{n,i}. Let us consider gn=gn,l,(l{1,2,,K},li)g_{n}^{*}=g_{n,l},(l\in\{1,2,...,K\},l\neq i). From the optimal reflection coefficients of all RIS elements that achieve hh^{*}, if we replace the reflection coefficient of the nnth RIS element with gn,ig_{n,i}, then the overall channel from the transmitter to the receiver is denoted as h=hgn,l+gn,ih^{\dagger}=h^{*}-g_{n,l}+g_{n,i}. Since we assume that gn,ig_{n,i} is not optimal, |h||h^{\dagger}| should be smaller than |h||h^{*}|. We will have:

|h|2<|h|2\displaystyle|h^{\dagger}|^{2}<|h^{*}|^{2} (30)
\displaystyle\longrightarrow |hgn,l+gn,i|2<|h|2\displaystyle|h^{*}-g_{n,l}+g_{n,i}|^{2}<|h^{*}|^{2}
\displaystyle\longrightarrow |hgn,l|2+|gn,i|2+2hgn,l,gn,i<|h|2\displaystyle|h^{*}-g_{n,l}|^{2}+|g_{n,i}|^{2}+2\langle h^{*}-g_{n,l},g_{n,i}\rangle<|h^{*}|^{2}
(i)\displaystyle\overset{\text{(i)}}{\longrightarrow} |hgn,l|2+|gn,i|2+2h,gn,i\displaystyle|h^{*}-g_{n,l}|^{2}+|g_{n,i}|^{2}+2\langle h^{*},g_{n,i}\rangle
2gn,l,gn,i<|h|2\displaystyle-2\langle g_{n,l},g_{n,i}\rangle<|h^{*}|^{2}
(step (i) is due to additivity property of inner product)
\displaystyle\longrightarrow |h|2+|gn,l|22h,gn,l+|gn,i|2+2h,gn,i\displaystyle|h^{*}|^{2}+|g_{n,l}|^{2}-2\langle h^{*},g_{n,l}\rangle+|g_{n,i}|^{2}+2\langle h^{*},g_{n,i}\rangle
2gn,l,gn,i<|h|2\displaystyle-2\langle g_{n,l},g_{n,i}\rangle<|h^{*}|^{2}
\displaystyle\longrightarrow |gn,l|2+|gn,i|22gn,l,gn,i2h,gn,l\displaystyle|g_{n,l}|^{2}+|g_{n,i}|^{2}-2\langle g_{n,l},g_{n,i}\rangle-2\langle h^{*},g_{n,l}\rangle
+2h,gn,i<0\displaystyle+2\langle h^{*},g_{n,i}\rangle<0
\displaystyle\longrightarrow |gn,lgn,i|22h,gn,l+2h,gn,i<0\displaystyle|g_{n,l}-g_{n,i}|^{2}-2\langle h^{*},g_{n,l}\rangle+2\langle h^{*},g_{n,i}\rangle<0
\displaystyle\longrightarrow |gn,lgn,i|2+2(h,gn,ih,gn,l)<0.\displaystyle|g_{n,l}-g_{n,i}|^{2}+2(\langle h^{*},g_{n,i}\rangle-\langle h^{*},g_{n,l}\rangle)<0.

|gn,lgn,i|2|g_{n,l}-g_{n,i}|^{2} is a non-negative number. Since h,gn,i\langle h^{*},g_{n,i}\rangle is the maximum among {h,gn,1,h,gn,2,,h,gn,K}\{\langle h^{*},g_{n,1}\rangle,\langle h^{*},g_{n,2}\rangle,...,\langle h^{*},g_{n,K}\rangle\}, 2(h,gn,ih,gn,l)2(\langle h^{*},g_{n,i}\rangle-\langle h^{*},g_{n,l}\rangle) is also a non-negative number. Thus, we have reached a contradiction in the last line of (30). The proof is now complete.

Appendix B Proof of Theorem 2

Consider curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}). Assume the interval between two consecutive intersections q1q_{1} and q2q_{2} on the curve555Here q1q_{1} and q2q_{2} are h\angle h^{*} values of the two intersections. is an active interval. Assume q2q_{2} is the intersection in common between curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) and curve β^lcos(hgn,l)\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}). The interval from q1q_{1} to q2q_{2} is assumed to be active for curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}). Thus, we have β^icos(hgn,i)>β^lcos(hgn,l),h(q1,q2)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})>\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}),\forall\angle h^{*}\in(q_{1},q_{2}). At the beginning of Section III-C, we have proved that the intersections in common between each pair of curves are π\pi radians apart. Therefore, we can say β^icos(hgn,i)>β^lcos(hgn,l),h(q2π,q2)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})>\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}),\forall\angle h^{*}\in(q_{2}-\pi,q_{2}).666Recall that each interval is defined within [0,2π)[0,2\pi) in Section III-B. Here interval (q2π,q2)(q_{2}-\pi,q_{2}) actually means (q2πmod2π,q2)(q_{2}-\pi\mod 2\pi,~{}q_{2}). We use (q2π,q2)(q_{2}-\pi,q_{2}) for presentation simplicity. In general, when we write an interval as (x1,x2)(x_{1},x_{2}), it actually means (x1mod2π,x2mod2π)(x_{1}\mod 2\pi,~{}x_{2}\mod 2\pi). We will have:

β^icos(hgn,i)>β^lcos(hgn,l),\displaystyle\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})>\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}), (31)
h(q2π,q2)\displaystyle\forall\angle h^{*}\in(q_{2}-\pi,q_{2})
β^icos(hgn,i)<β^lcos(hgn,l),\displaystyle\longrightarrow-\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})<-\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}),
h(q2π,q2)\displaystyle\forall\angle h^{*}\in(q_{2}-\pi,q_{2})
β^icos(h+πgn,i)<β^lcos(h+πgn,l),\displaystyle\rightarrow\hat{\beta}_{i}\cos(\angle h^{*}+\pi-\angle g_{n,i})<\hat{\beta}_{l}\cos(\angle h^{*}+\pi-\angle g_{n,l}),
h(q2π,q2)\displaystyle\forall\angle h^{*}\in(q_{2}-\pi,q_{2})
β^icos(hgn,i)<β^lcos(hgn,l),\displaystyle\rightarrow\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})<\hat{\beta}_{l}\cos(\angle h^{*}-\angle g_{n,l}),
h(q2,q2+π).\displaystyle\forall\angle h^{*}\in(q_{2},q_{2}+\pi).

According to (31), β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) cannot be the maximum curve (i.e., the curve above all other curves) h(q2,q2+π)\forall\angle h^{*}\in(q_{2},q_{2}+\pi). Now, assume q1q_{1} is the intersection in common between β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) and β^lcos(hgn,l)\hat{\beta}_{l^{\prime}}\cos(\angle h^{*}-\angle g_{n,l^{\prime}}). We have β^icos(hgn,i)>β^lcos(hgn,l),h(q1,q2)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})>\hat{\beta}_{l^{\prime}}\cos(\angle h^{*}-\angle g_{n,l^{\prime}}),\forall\angle h^{*}\in(q_{1},q_{2}). Since the interval from q1q_{1} to q2q_{2} is active for curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}), we have β^icos(hgn,i)>β^lcos(hgn,l),h(q1,q1+π)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})>\hat{\beta}_{l^{\prime}}\cos(\angle h^{*}-\angle g_{n,l^{\prime}}),\forall\angle h^{*}\in(q_{1},q_{1}+\pi). We will have:

β^icos(hgn,i)>β^lcos(hgn,l),\displaystyle\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})>\hat{\beta}_{l^{\prime}}\cos(\angle h^{*}-\angle g_{n,l^{\prime}}), (32)
h(q1,q1+π)\displaystyle\forall\angle h^{*}\in(q_{1},q_{1}+\pi)
β^icos(hgn,i)<β^lcos(hgn,l),\displaystyle\longrightarrow-\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})<-\hat{\beta}_{l^{\prime}}\cos(\angle h^{*}-\angle g_{n,l^{\prime}}),
h(q1,q1+π)\displaystyle\forall\angle h^{*}\in(q_{1},q_{1}+\pi)
β^icos(hπgn,i)<β^lcos(hπgn,l),\displaystyle\rightarrow\hat{\beta}_{i}\cos(\angle h^{*}-\pi-\angle g_{n,i})<\hat{\beta}_{l^{\prime}}\cos(\angle h^{*}-\pi-\angle g_{n,l^{\prime}}),
h(q1,q1+π)\displaystyle\forall\angle h^{*}\in(q_{1},q_{1}+\pi)
β^icos(hgn,i)<β^lcos(hgn,l),\displaystyle\rightarrow\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i})<\hat{\beta}_{l^{\prime}}\cos(\angle h^{*}-\angle g_{n,l^{\prime}}),
h(q1π,q1).\displaystyle\forall\angle h^{*}\in(q_{1}-\pi,q_{1}).

According to (32), β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}) cannot be the maximum curve h(q1π,q1)\forall\angle h^{*}\in(q_{1}-\pi,q_{1}). Since (q1π,q1)(q1,q2)(q2,q2+π)(q_{1}-\pi,q_{1})\cup(q_{1},q_{2})\cup(q_{2},q_{2}+\pi) covers the whole [0,2π)[0,2\pi) range, there will be no active interval outside (q1,q2)(q_{1},q_{2}) for curve β^icos(hgn,i)\hat{\beta}_{i}\cos(\angle h^{*}-\angle g_{n,i}). This completes the proof.

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