PreprintPDF Available

Analysis and Optimization for IRS-Aided Multi-pair Communications Relying on Statistical CSI

Authors:
• Shanghai Normal University (China)
Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-pair communication system, in which multiple pairs of users exchange information via an IRS. We derive an approximate expression for the achievable rate when only statistical channel state information (CSI) is available. Then, a genetic algorithm (GA) is proposed to solve the rate maximization problem. In particular, both the scenarios of continuous phase shift (CPS) and discrete phase shift (DPS) are considered. Simulation results verify the correctness of our derived results and show that the proposed GA method has almost the same performance as the globally optimal solution obtained by the exhaustive search method. In addition, three bits for discretization are capable of achieving a large portion of the achievable rate for the CPS case.
Content may be subject to copyright.
arXiv:2007.11704v1 [eess.SP] 22 Jul 2020
1
Analysis and Optimization for IRS-Aided Multi-pair
Communications Relying on Statistical CSI
Zhangjie Peng, Tianshu Li, Cunhua Pan, Member, IEEE, Hong Ren Member, IEEE,
Wei Xu, Senior Member, IEEE, and Marco Di Renzo Fellow, IEEE
Abstract—In this paper, we investigate an intelligent reﬂecting
surface (IRS) assisted multi-pair communication system, in which
multiple pairs of users exchange information via an IRS. We
derive an approximate expression for the achievable rate when
only statistical channel state information (CSI) is available.
Then, a genetic algorithm (GA) is proposed to solve the rate
maximization problem. In particular, both the scenarios of
continuous phase shift (CPS) and discrete phase shift (DPS)
are considered. Simulation results verify the correctness of our
derived results and show that the proposed GA method has
almost the same performance as the globally optimal solution
obtained by the exhaustive search method. In addition, three
bits for discretization are capable of achieving a large portion of
the achievable rate for the CPS case.
Index Terms—Intelligent reﬂecting surface (IRS), statistical
channel state information (CSI), reconﬁgurable intelligent surface
(RIS) , multi-pair communication, genetic algorithm (GA)
I. INT RO DUC TI ON
Recently, with the development of meta-surfaces [1], a new
transmission technique named intelligent reﬂecting surface
(IRS), which is capable of reconﬁguring the channel into a
desirable form by carefully turning its phase shifts [2], has
been widely regarded as a promising technology for future
6G wireless communications [2]. Due to its appealing features
of low cost and low power consumption, IRS has attracted
extensive research attention [3]–[6].
Some initial efforts have been devoted to the study of
various IRS-assisted communication systems such as physical
layer security in [7], [8], multicell networks in [9], full duplex
system in [10], mobile edge computing in [11], and wireless
power transfer in [12]. However, to the best of our knowledge,
a paucity of contributions studied the IRS-assisted multi-pair
communication system, which is a typical communication
scenario due to the rapid increase of the number of machine
devices in future networks. On the other hand, the direct
communication links of the multi-pair communications may be
blocked in both indoor and outdoor applications. In particular,
the direct signals may be readily blocked by the trees and
Z. Peng and T. Li are with the College of Information, Mechanical and
Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
(e-mail: pengzhangjie@shnu.edu.cn, 1000479056@smail.shnu.edu.cn).
C. Pan and H. Ren are with the School of Electronic Engineering and
Computer Science at Queen Mary University of London, London E1 4NS,
U.K. (e-mail: c.pan,h.ren@qmul.ac.uk).
W. Xu is with National Mobile Communications Research Laboratory,
Southeast University, Nanjing 210096, China (e-mail: wxu@seu.edu.cn).
M. Di Renzo is with Universit´e Paris-Saclay, CNRS, CentraleSup´elec,
Laboratoire des Signaux et Systemes, Gifsur-Yvette, France (e-mail:
marco.direnzo@centralesupelec.fr).
IRS
1
UA
UAi
U
AK
1a
g
ai
g
aK
g
1b
g
bK
g
1
UB
UBi
UBK
Fig. 1. System model for IRS-assisted multi-pair communication.
huge buildings in outdoor scenarios. In indoor scenarios, the
signals can be blocked by thick walls, especially in high-
frequency mmWave communication systems. We intend to
deploy an IRS between two sides of communication devices
to address the above issue. Compared with the existing papers
on relay-assisted multi-pair communications in [13]–[15], the
IRS has some appealing advantages. In speciﬁc, IRS requires
low power consumption and will not introduce noise since it
is passive and does not have signal processing units such as
sion between two sides can take place at the same time, while
the relay needs two time slots for the whole transmission.
This means the IRS can save transmission time, which is very
suitable for ultra-reliable low latency communication.
Against the above background, we study the transmission
design for an IRS-assisted multi-pair communication system.
Unlike most of the existing papers in [7]–[12] where in-
stantaneous channel state information (CSI) is assumed, we
consider the statistical CSI [16] that is much easier to obtain
since it varies much slowly. Speciﬁcally, our contributions are
threefold: 1) We derive the achievable rate; 2) We propose the
genetic algorithm (GA) method to solve the phase shift opti-
mization problem , where both the cases of continuous phase
shift (CPS) and discrete phase shift (DPS) are considered; 3)
Extensive simulation results are provided to demonstrate the
correctness of our derived results, and also show that three
bits are enough to discretize the phase shifts, which provides
The rest of the paper is organized as follows. In Section
II, we introduce the IRS-assisted multi-pair communication
system model. We derive the achievable rate in Section III
2
and optimize the phase shift in Section IV. Numerical results
are provided to demonstrate the correctness of our analysis in
Section V. In the end, we draw conclusions in Section VI.
II. SY S TE M MODE L
We consider an IRS-assisted multi-pair communication sys-
tem, where Kpairs of users exchange information via an IRS,
as shown in Fig. 1. The IRS consists of Lreﬂective elements,
which are capable of customizing the channel environment by
turning the phase shifts. The phase shift matrix Θis given by
Θ=diag(ejθ1,···, e,···, ej θL), where θis the phase
shift of the th reﬂective element. We denote the ith single-
antenna transmitter as UAi and the ith single-antenna receiver
as UBi , for i= 1, ..., K .
The channel between UAi and the IRS and that between the
IRS and UBi can be written as
gai =αaihai ,(1)
gbi =αbihbi ,(2)
where αai and αbi denote the large-scale fading coefﬁcients,
and giCL×1and hiCL×1denote the fast fading vectors.
and hbi can be expressed as
hai =rεi
εi+ 1hai +r1
εi+ 1 ˜
hai,(3)
hbi =sβi
βi+ 1 hbi +r1
βi+ 1 ˜
hbi,(4)
where εidenotes the Rician factor, ˜
hai CL×1and ˜
hbi
CL×1both denote the non-line-of-sight channel vector, each
element of which follows independent and identically distri-
bution of CN(0,1), and hai CL×1and hbi CL×1denote
the line-of-sight channel vector. In particular, hai and hbi can
be expressed as
hai = [1, ej2πd
λsinςi,···, ej2πd
λ(L1)sinςi]T,(5)
hbi = [1, ej2πd
λsinϕi,···, ej2πd
λ(L1)sinϕi]T,(6)
where ϕiand ςirepresent the ith pair of users’ AoA (angle
of arrival) and AoD (angle of departure), respectively. For
convenience, we will set d=λ
2in the rest of this paper.
We assume the availability of statistical CSI at UAi , for
i= 1,···, K. The statistical CSI can be readily obtained
since it varies much slowly than the instantaneous CSI. The
signal received at UBi is given by
yi=gT
biΘ
K
X
j=1
pjgaj xj+ni
=pigT
biΘgai xi
|{z }
Desired signal
+
K
X
j=1,j6=i
pjgT
biΘgaj xj
|{z }
Inter-user interference
+ni
|{z}
Noise
,(7)
where xj∼ CN (0,1) represents the signal UAj transmits, pj
denotes the transmission power at UAj , and ni∼ CN(0, σ2
i)
is the additive white Gaussian noise (AWGN) of UBi , for
i= 1,···, K.
From (7), it is readily seen that yiconsists of three parts:
the desired signal which UB i wants to receive, the interference
produced by other multi-pair users and the noise. Furthermore,
the signal-to-interference plus noise ratio (SINR) for UB i is
given by
γi=piαbiαai hT
biΘhai 2
K
P
j=1,j6=ipjαbiαaj hT
biΘhaj 2+σ2
i
.(8)
Hence, the average achievable rate for UBi can be expressed
as
Ri=E{log2(1 + γi)}.(9)
Therefore, the sum achievable rate can be written as
C=
K
X
i=1
Ri.(10)
III. ACHI EVAB LE RATE ANA LYSIS
To analyze the performance of IRS-assisted multi-pair com-
munication systems, we ﬁrst introduce Theorem 1.
Theorem 1. The achievable rate of the ith receiver can be
approximated as
Rilog2
1 + piαbiαai
εiβii,i+L(εi+βi)+L
(εi+1)(βi+1)
K
P
j=1,j6=i
pjαbiαaj
εiβji,j +L(εi+βj)+L
(εi+1)(βj+1) +σ2
i
,
(11)
where i,i and i,j are deﬁned as
i,i =L+ 2 X
1m<nL
cos[θnθm+ (nm)π(sinϕi+sinςi)],
(12)
i,j =L+ 2 X
1m<nL
cos[θnθm+ (nm)π(sinϕi+sinςj)].
(13)
Proof: Using Lemma 1 in [17], Riin (9) can be approxi-
mated as
Rilog2
1 +
piαbiαai EnhT
biΘhai 2o
K
P
j=1,j6=ipjαbiαbj EnhT
biΘhaj 2o+σ2
i
.
(14)
The th element of hai and hbi can be written as follows:
[hai]=rεi
εi+ 1 ej(1)πsinϕi+r1
εi+ 1 (sℓi +jtℓi ),(15)
and
[hbi]=sβi
βi+ 1ej(1)πsinςi+r1
βi+ 1 (uℓi +jvℓi ),(16)
3
θ
...
θ
...
θ
...
Initial
population
Saved group
θ
...
θ
...
θ
...
Yes
No
Evaluation
Eq. (23)
Next generation
Evaluation
Eq. (23)
and Sort Selection
Function
and
Crossover
Function
Mutation
Function
Current generation
Mutable group
Unnamed group
Termination
Criterion
Reached?
Output the
best individual
in current
population
θ
Elites
Fig. 2. Genetic algorithm structure.
where sℓi ∼ N(0,1/2) and tℓi ∼ N(0,1/2) denote the
independent real and imaginary parts of [˜
hai], respectively.
uℓj ∼ N(0,1/2) and vℓj ∼ N(0,1/2) denote the independent
real and imaginary parts of [˜
hbi], respectively.
By substituting (15) and (16) into
hT
biΘhaj =
L
X
=1
[hbi]θ[haj ],(17)
we can obtain the real and imaginary parts, shown at the
bottom of the next page.
As we know
EnhT
biΘhaj 2o=E(hT
biΘhaj )2
real + (hT
biΘgaj )2
imag .
(20)
Substituting (18) and (19) into (20) and removing the terms
with zero value, we can rewrite (20) as
EnhT
biΘhaj 2o=εiβji,j +L(εi+βj) + L
(εi+ 1)(βj+ 1) .(21)
By substituting (21) into (14), we arrive at the ﬁnal result
in (11).
Substituting (11) into (10), we obtain the sum achievable
rate. According to Theorem 1, when αbi,αai , AoA, AoD,
εi, and σiremain unchanged, the sum achievable rate is
determined by the number of pairs of users K, transmission
power pi, phase shift matrix Θand the number of reﬂective
elements L.
IV. PHA SE SHI FT OP T IM IZATI ON
To obtain the maximal sum achievable rate, we solve the
phase shift optimization problem, where both the scenarios of
CPS and DPS are taken into account.
(hT
iΘgj)real =1
p(εi+ 1)(βj+ 1)
L
X
=1 pεiβj{cos[θ+ (1)π(sinϕi+sinςj)] }
+1
p(εi+ 1)(βj+ 1)
L
X
=1
εi{cosθ[cos[(1)πsinϕi]uℓj sin[(1)πsinϕi]vℓj ]
sinθ[cos[(1)πsinϕi]uℓj +sin[(1)πsinϕi]vℓj ]}
+pβj{cosθ[cos[(1)πsinςj]sℓi sin[(1)πsinςj]tℓi]
sinθ[cos[(1)πsinςj]tℓi +sin[(1)πsinςj]sℓi]}
+cosθ(sℓiuℓj tℓi vℓj )sinθ(sℓi vℓj +tℓiuℓj )(18)
(hT
iΘgj)imag =1
p(εi+ 1)(βj+ 1)
L
X
=1 pεiβj{sin[θ+ (1)π(sinϕi+sinςj)] }
+1
p(εi+ 1)(βj+ 1)
L
X
=1
εi{sinθ[cos[(1)πsinϕi]uℓj sin[(1)πsinϕi]vℓj ]
+cosθ[cos[(1)πsinϕi]uℓj +sin[(1)πsinϕi]vℓj ]}
+pβj{cosθ[sin[(1)πsinςj]sℓi sin[(1)πsinςj]tℓi ]
+cosθ[cos[(1)πsinςj]tℓi +sin[(1)πsinςj]sℓi]}
+sinθ(sℓiuℓj tℓi vℓj ) + cosθ(sℓi vℓj +tℓiuℓj )(19)
4
A. Optimal CPS Design
To begin with, we consider the CPS case. The optimization
problem is formulated as
max
Θ
K
X
i=1
log2(1 + γi)(22a)
s.t. θ[0,2π)= 1,···, L. (22b)
The computational complexity of solving Problem (22) by
using conventional optimization methods is prohibitively high,
since the data rate expression is a complex expression of the
optimization variables. As a result, a GA method is proposed.
The algorithm structure is shown in Fig.2. We denote each in-
dividual as a 1 ×L phase shift vector θ=[θ1,···, θ,···, θL]
and θas its th gene. First, Ntindividuals are generated, by
setting each genes randomly distributed within [0,2π), known
as the initial population (with population size of Nt= 100).
Besides, the evaluation of each individual is evaluated by the
ﬁtness function:
f(θ) = 1
PK
i=1 log2(1 + γi).(23)
We make a list sorted by ﬁtness function value (lowest to
highest). The individuals who achieve better evaluation have
lower ﬁtness function values, and appear higher on the list.
Second, ﬁrst Nsindividuals (Ns= 50) in the list are placed in
the saved group and then pass into the unnamed group directly.
Then, the selection function is capable of obtaining one candi-
date from Ntindividuals. We can repeat the selection function
procedure until the number of candidates satisﬁes the request.
The crossover function operates on two candidates (obtained
by selection function), and generates two crossover children.
The number of children generated by crossover function is
Nc, which satisﬁes Nt=Ns+Nc. Then, we place the children
in the unnamed group. Third, elites are the individuals who
have lowest ﬁtness function value in the unnamed group (Ns
saved individuals and Ncchildren), with elites size of Ne=
1. Except for elites, the rest of individuals are placed in the
mutable group. Thus, this group contains (Np-Ne)individuals,
and (Np-Ne)Lgenes. Each gene is capable of mutating to a
random number between 0 and 2πwith mutation rate Pm=
0.1 by utilizing mutation function. Thus, we have produced
the current generation population, with elites and mutated
individuals, on each iteration. Fourth, repeat the iterative
method mentioned above until the termination criterion is
reached. Finally, the individual with the lowest ﬁtness function
value in the current generation is chosen as the output of the
algorithm. The selection function and crossover function are
illustrated as follows.
Algorithm 1: Selection Function
1Generate a random number rbetween 0 and Nt;
2Take the [r]th individual in the list ([r]denotes the
nearest maximum integer to r).
1) Selection function: The selection function is proposed to
obtain candidates for crossover function. The individuals with
Algorithm 2: Crossover Function
1Get θ1= [θ(1)
1,···, θ(1)
,···, θ(1)
L]and
θ2= [θ(2)
1,···, θ(2)
,···, θ(2)
L]selected by Algorithm 1;
2Identify a crossover point [1, L]randomly;
3Crossover θ1and θ2at crossover point ;
4Obtain two children
θc1 = [θ(1)
1,···, θ(1)
, θ(2)
+1,···, θ(2)
L]and
θc2 = [θ(2)
1,···, θ(2)
, θ(1)
+1,···, θ(1)
L].
higher ﬁtness function values will have a lower probability to
be selected. The selection function is described in Algorithm
1.
2) Crossover Function: The crossover function operates on
θ1and θ2and generates two children θc1 and θc2 . The
crossover function is derived under a single-point crossing
algorithm and described in Algorithm 2.
B. Optimal DPS Design
In real scenarios, the IRS only has a limited number of phase
shifts. We assume that each reﬂective element is encoded with
Bbits, and thus 2Bvalues of phase shifts can be chosen to
enhance the signal reﬂected by the IRS [18]. We denote the
DPS matrix as ˆ
Θ=diag(ejˆ
θ1,···, ejˆ
θ,···, ejˆ
θL), where ˆ
θ
is the DPS of the th reﬂective element. Replacing the CPSs
in Problem (22) by the DPSs, the optimization problem for
the DPS scenario can be formulated as
max
ˆ
Θ
K
X
i=1
log2(1 + γi)(24a)
s.t. ˆ
θ∈ {0,2π/2B,···,2π(2B1)/2B}
= 1,···, L. (24b)
It is observed that Problem (24) is similar to Problem (22).
Accordingly, the above GA method proposed for the CPS
scenario can also be extended to this DPS scenario. For this
DPS scenario, we only need to replace CPSs with DPSs in the
above GA method.
V. NUM ER ICA L RES ULTS
We evaluate the impact of different factors on the sum
achievable rate. We assume that the Rician factor is εi= 10,
the noise power is σ2
i= 1, and the transmission power is
denoted as SNR = pi, for i= 1, ..., K. Furthermore, the other
parameters are summarized in Table I.
In Fig. 3, we draw the sum rate versus SNR for analytical
and Monte-Carlo simulation results with two bits by the
proposed GA method. We observe that the analytical results
are consistent with the Monte-Carlo simulation results, which
veriﬁes the correctness of the derivations. In addition, we
can also ﬁnd that the sum rate increases with the number of
reﬂective elements L, since more energy is reﬂected.
Fig. 4 depicts the sum rate versus SNR with two bits
for various schemes. As expected, the proposed GA and
exhaustive search method achieve higher sum rate than that of
random method. It is interesting to observe that the proposed
5
TABLE I
PAR AM ET ER S FO R SI M UL ATI ON
No. of pairs AoA AoD αai and αbi
15.5629 1.1450 0.0023
25.6486 0.6226 0.0285
33.9329 3.0773 0.0025
40.8663 1.2142 0.0012
51.3685 5.6290 0.0550
61.1444 0.6226 0.0141
−15 −10 −5 0 5 10 15 20 25
0
1
2
3
4
5
6
7
8
SNR (dB)
Sum Rate (bits/s/Hz)
L = 16,K = 6
L = 8,K = 6
simulation
Fig. 3. Sum rate versus SNR with B= 2 and GA.
GA method has almost the same performance as the globally
optimal solution obtained by the exhaustive search method.
Fig. 5 shows the sum rate versus the number of coding
bits for the scenarios of CPS and DPS with SNR = 20 dB.
The sum rate by using DPSs increases rapidly when Bis
small, while the curve gradually becomes saturated when B
is large. It is well known using a large number of coding bits
to control the phase shifts incurs high hardware cost and power
consumption. The ﬁgure shows that three bits for discretization
can achieve a large portion of the sum rate, which provides
VI. CO NCL US I ON
In this paper, we investigated IRS-assisted communications
for multiple pairs of users. We derived the approximate ex-
pression for the achievable rate. We proposed the GA method
to achieve the maximal achievable rate by optimizing the
phase shifts, where both the scenarios of CPS and DPS were
considered. Simulation results veriﬁed the correctness of our
derivations.
REF ERE NC ES
[1] T. J. Cui, M. Q. Qi, X. Wan, J. Zhao, and Q. Cheng, “Coding
metamaterials, digital metamaterials and programmable metamaterials,”
Light: Science & Applications, vol. 3, no. 10, p. e218, Oct. 2014.
[2] F. Tariq, M. Khandaker, K.-K. Wong, M. Imran, M. Bennis, and
M. Debbah, “A speculative study on 6G.” [Online]. Available:
https://arxiv.org/abs/1902.06700
[3] C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and
C. Yuen, “Reconﬁgurable intelligent surfaces for energy efﬁciency in
wireless communication,IEEE Trans. Wireless Commun., vol. 18, no. 8,
pp. 4157–4170, Aug. 2019.
−15 −10 −5 0 5 10 15 20 25 30
0
1
2
3
4
5
6
7
SNR (dB)
Sum Rate (bits/s/Hz)
GA (L = 8,K = 6)
exhaustive (L = 8,K = 6)
GA (L = 8,K = 2)
exhaustive (L = 8,K = 2)
random (L = 8,K = 6)
random (L = 8,K = 2)
Fig. 4. Sum rate versus SNR with B= 2 by various schemes.
1 2 3 4 5 6 7 8
2.5
3
3.5
4
4.5
5
5.5
6
6.5
Number of coding bits B
Sum Rate (bits/s/Hz)
CPSs (L = 16,K = 6)
DPSs (L = 16,K = 6)
CPSs (L = 8,K = 6)
DPSs (L = 8,K = 6)
Fig. 5. Sum rate versus coding bit with SNR = 20 dB.
[4] Q. Wu and R. Zhang, “Intelligent reﬂecting surface enhanced wireless
network via joint active and passive beamforming,IEEE Trans. Wireless
Commun., vol. 18, no. 11, pp. 5394–5409, Nov. 2019.
[5] C. Huang, S. Hu, G. C. Alexandropoulos, A. Zappone, C. Yuen,
R. Zhang, M. Di Renzo, and M. Debbah, “Holographic MIMO surfaces
for 6G wireless networks: Opportunities, Challenges, and Trends.
[Online]. Available: https://arxiv.org/abs/1911.12296v1
[6] M. Di Renzo, M. Debbah, D.-T. Phan-Huy, A. Zappone, M.-S.
Alouini, C. Yuen, V. Sciancalepore, G. C. Alexandropoulos, J. Hoydis,
H. Gacanin, J. de Rosny, A. Bounceu, G. Lerosey, and M. Fink, “Smart
idea whose time has come,EURASIP J. Wireless Commun. Netw., vol.
2019, p. 129, May 2019.
[7] H. Shen, W. Xu, S. Gong, Z. He, and C. Zhao, “Secrecy rate
maximization for intelligent reﬂecting surface assisted multi-antenna
communications,IEEE Commun. Lett., vol. 23, no. 9, pp. 1488–1492,
Sep. 2019.
[8] X. Yu, D. Xu, Y. Sun, D. W. K. Ng, and R. Schober, “Robust
and secure wireless communications via intelligent reﬂecting surfaces.
[Online]. Available: https://arxiv.org/abs/1912.01497
[9] C. Pan, H. Ren, K. Wang, W. Xu, M. Elkashlan, A. Nallanathan,
and L. Hanzo, “Multicell MIMO communications relying on intelligent
reﬂecting surfaces,IEEE Trans. Wireless Commun., early access, May.
08, 2020, doi:10.1109/TWC.2020.2990766.
[10] Z. Peng, Z. Zhang, C. Pan, L. Li, and A. L. Swindlehurst, “Multiuser
full-duplex two-way communications via intelligent reﬂecting surface.
[Online]. Available: https://arxiv.org/abs/2006.05147
[11] T. Bai, C. Pan, Y. Deng, M. Elkashlan, and L. Hanzo, “Latency
minimization for intelligent reﬂecting surface aided mobile edge
computing.” [Online]. Available: https://arxiv.org/abs/1910.07990
[12] C. Pan, H. Ren, K. Wang, M. Elkashlan, A. Nallanathan, J. Wang,
6
and L. Hanzo, “Intelligent reﬂecting surface aided MIMO broad-
casting for simultaneous wireless information and power transfer,”
IEEE J. Select. Areas Commun., early access, Jun. 08, 2020,
doi:10.1109/JSAC.2020.3000802.
[13] J. Xu, Y. Wang, W. Xu, S. Jin, H. Shen, and X. You, “On uplink
performance of multiuser massive MIMO relay network with limited
rf chains,IEEE Trans. Veh. Technol., early access, Jun. 02, 2020,
doi:10.1109/TVT.2020.2999345.
[14] W. Xu, J. Liu, S. Jin, and X. Dong, “Spectral and energy efﬁciency of
multi-pair massive MIMO relay network with hybrid processing,” IEEE
Trans. Commun., vol. 65, no. 9, pp. 3794–3809, Sep. 2017.
[15] S. Jin, X. Liang, K.-K. Wong, X. Gao, and Q. Zhu, “Ergodic rate analysis
for multipair massive MIMO two-way relay networks,” IEEE Trans.
Wireless Commun., vol. 14, no. 3, pp. 1480–1491, Mar. 2015.
[16] C. Guo, Y. Cui, F. Yang, and L. Ding, “Outage probability
analysis and minimization in intelligent reﬂecting surface-assisted
MISO systems,” IEEE Commun. Lett., early access, Feb. 20, 2020,
doi:10.1109/LCOMM.2020.2975182.
[17] Q. Zhang, S. Jin, K.-K. Wong, H. Zhu, and M. Matthaiou, “Power
scaling of uplink massive MIMO systems with arbitrary-rank channel
means,” IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 966–981,
Oct. 2014.
[18] H. Zhang, B. Di, L. Song, and Z. Han, “Reconﬁgurable intelligent
surfaces assisted communications with limited phase shifts: How many
phase shifts are enough?” IEEE Trans. Veh. Technol., vol. 69, no. 4, pp.
4498–4502, Apr. 2020.
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Future wireless networks are expected to evolve toward an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices. They will also be capable of sensing, controlling, and optimizing the wireless environment to fulfill the visions of low-power, high-throughput, massively-connected, and low-latency communications. A key conceptual enabler that is recently gaining increasing popularity is the HMIMOS that refers to a low-cost transformative wireless planar structure comprised of sub-wavelength metallic or dielectric scattering particles, which is capable of shaping electromagnetic waves according to desired objectives. In this article, we provide an overview of HMIMOS communications including the available hardware architectures for reconfiguring such surfaces, and highlight the opportunities and key challenges in designing HMIMOS-enabled wireless communications.
Article
Full-text available
Article
Full-text available
Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS’s power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the nonconvexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.
Article
Full-text available
An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Speciﬁcally, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we ﬁrst adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternatively optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results conﬁrm that employing IRSs in SWIPT beneﬁcially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.
Article
Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). Based on information exchanged by the base station (BS) and all users, this approach can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unitmodulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas.
Article
This paper considers a multiuser massive multiple-input multiple-output uplink with the help of an analog amplify-and-forward relay. The base station equips a large array of $N_d$ antennas but is supported by a far smaller number of radio-frequency chains. By first deriving new results for a cascaded phase-aligned two-hop channel, we obtain a tight bound for the ergodic rate in closed form for both perfect and quantized channel phase information. The rate is characterized as a function of a scaled equivalent signal-to-noise ratio of the two-hop channel. It implies that the source and relay powers can be respectively scaled down as $1/N_d^a$ and $1/N_d^{1-a} (0 \leq a \leq 1)$ for an asymptotically unchanged sum rate. Then for the rate maximization, the problem of power allocation is optimized with closed-form solutions. Simulation results verified the observations of our derived results.
Article
Intelligent reflecting surface (IRS) is gradually being recognized as a promising technology for improving spectral and energy efficiency of wireless systems. In this letter, we study an IRS-assisted multiple-input single-output (MISO) system where a base station (BS) with multiple antennas arranged in a uniform rectangular array (URA) serves a single-antenna user with the help of an IRS with multiple elements arranged in a URA. We consider a Rician fading model, where the non-line of sight (NLoS) components vary slowly and the line of sight (LoS) components do not change. To reduce costs for channel estimation and phase adjustment, we adopt fixed maximum-ratio transmission (MRT) at the BS, fixed phase shifts at the IRS and constant rate transmission. First, we obtain the expression of the outage probability. Then, we minimize the outage probability by optimizing the phase shifts of the IRS. Finally, we obtain the expression of the asymptotically optimal outage probability in the high signal-to-noise ratio (SNR) regime. We also characterize the impacts of several key system parameters on the optimal outage probability to reveal important design insights.
Article
Reconfigurable intelligent surface~(RIS) has drawn a great attention worldwide as it can create favorable propagation conditions by controlling the phase shifts of the reflected signals at the surface to enhance the communication quality. However, the practical RIS only has limited phase shifts, which will lead to the performance degradation. In this letter, we evaluate the performance of an uplink RIS assisted communication system by giving an approximation of the achievable data rate, and investigate the effect of limited phase shifts on the data rate. In particular, we derive the required number of phase shifts under a data rate degradation constraint. Numerical results verify our analysis.
Article
Intelligent reflecting surface (IRS) is a revolutionary and transformative technology for achieving spectrum and energy efficient wireless communication cost-effectively in the future. Specifically, an IRS consists of a large number of low-cost passive elements each being able to reflect the incident signal independently with an adjustable phase shift so as to collaboratively achieve three-dimensional (3D) passive beamforming without the need of any transmit radio-frequency (RF) chains. In this paper, we study an IRS-aided single-cell wireless system where one IRS is deployed to assist in the communications between a multi-antenna access point (AP) and multiple single-antenna users. We formulate and solve new problems to minimize the total transmit power at the AP by jointly optimizing the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the IRS, subject to users’ individual signal-to-interference-plus-noise ratio (SINR) constraints. Moreover, we analyze the asymptotic performance of IRS’s passive beamforming with infinitely large number of reflecting elements and compare it to that of the traditional active beamforming/relaying. Simulation results demonstrate that an IRS-aided MIMO system can achieve the same rate performance as a benchmark massive MIMO system without using IRS, but with significantly reduced active antennas/RF chains. We also draw useful insights into optimally deploying IRS in future wireless systems.