Joint Passive Beamforming and User Association Optimization for IRS-assisted mmWave Systems

Zhao, Dan; Lu, Hancheng; Wang, Yazheng; Sun, Huan

doi:10.1109/globecom42002.2020.9322586

Cited by 27 publications

(22 citation statements)

References 13 publications

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“…The authors in [ 29 ] proposed a dynamically successive access algorithm to obtain the APs-UEs association, where single IRS is implemented in the network which limits the superiority of multi IRSs deployment. Moreover, the authors in [ 30 ] used instantaneous channel state information (CSI) to design the user association (UA) and P-BF. In [ 31 ], the authors formulated an OP for a UA problem in an IRS aided mmWave network and solved it using a matching game assuming the availability of CSI in the base station and UE.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Based IRSs-UEs Association and IRSs Optimal Placement in Multi IRSs Aided Wireless System

Nor

Halunga

2022

Sensors

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Implementing intelligent reflecting surfaces (IRSs), in high frequency based beyond 5G networks, has become a necessity to overcome the harsh blockage issues that exist in these bands. IRSs can supply user equipment (UEs) with multi alternative virtual line of sight (LOS) links, hence enhancing the spectral efficiency (SE) of the system. As a result of deploying multi IRSs as communication assistants, the step of IRSs-UEs association is required to optimally assign each UE to its best IRS; consideration of the interference between different links is needed, to maximize the system performance. However, this process will be a time and power consuming problem, if conventional schemes, which exhaustively search all possible association patterns to find the optimum one for communication, is adapted. Although iterative search based schemes can reduce this complexity, they still need feedback signaling in real time. Hence, they will be inefficient in terms of power consumption and delay. Moreover, optimal placement of the multi-IRSs in the network, to enlarge the system performance, is still an open issue and needs to be studied. Consequently, in this paper, to handle the IRSs-UEs association problem, we propose a neural network (NN) based scheme using a multi-IRSs aided multi input multi output (MIMO) system. In this system, the estimated angles of arrival (AoAs) of UEs are used as input features for the NN, which is trained to associate each UE to its best IRS based on this information; then, within each IRS, passive beamforming is performed. Adapting this NN in online mode guarantees obtaining better performance while relaxing the complexity of association and increasing response time, giving a performance comparable to the exhaustive and iterative search based schemes. The proposed NN based scheme determines the association pattern without searching or feedback signals. Moreover, the proposed approach maintains the system SE nearly similar to the optimum performance obtained by the conventional scheme. Secondly, a criterion is suggested for optimal deployment of multi IRSs in the network, depending on maximizing the average summation UEs signal-to-interference-plus-noise ratio (SINR). Numerical results prove that this strategy outperforms a reference one, which aims to guarantee certain performance by maximizing minimum UE SINR. In contrast the proposed strategy achieves better system and per UE spectral efficiency.

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Section: Related Workmentioning

confidence: 99%

Neural Network Based IRSs-UEs Association and IRSs Optimal Placement in Multi IRSs Aided Wireless System

Nor

Halunga

2022

Sensors

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“…It can be seen that problem (32) is convex, which can be conveniently solved by utilizing 1 -norm optimization packages [37]. The detailed joint antenna selection and beamforming algorithm is presented in Algorithm 1.…”

Section: Bs Beamforming Vector Optimizationmentioning

confidence: 99%

“…Let d n = b n − NL r=1,r =n a n,r u r = αe jβ , and α = |d n |, β = arctan Im{dn} Re{dn} , ϕ n = arctan Im{u * n } Re{u * n } , that is u n = Joint antenna selection and beamforming 1 Initialization: η min , η max , given reflection coefficient matrices of all IRSs {Φ l } L l=1 ;Solve problem(32) and get the optimal solution as {p * } with η = (ηmin+ηmax) η max − η min < ε; Output: The beamforming vector p * .…”

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confidence: 99%

Intelligent Reflecting Surface-Assisted mmWave Communication With Lens Antenna Array

Wang

Zhao

et al. 2022

IEEE Trans. Cogn. Commun. Netw.

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In millimeter wave (mmWave) communication, lens antenna array has been considered as a potential technique to combat the severe path loss. However, blockage effect still exists. To address this issue, we design an intelligent reflecting surface (IRS) assisted wideband mmWave communication system with lens antenna array with limited radio frequency (RF) chains. This is the first attempt to design a mmWave communication system with the integration of IRS and lens antenna array. The designed system can realize high robust, low-complexity and costeffective mmWave communication. To achieve desired performance, we first derive the signal-to-interference-plus-noise ratio (SINR) expression with the intersymbol interference, then we formulate a SINR maximization problem, by jointly considering the beamforming vector at base station with limited RF chains and reflection coefficient matrices at IRS with discrete phase levels. This problem is intractable and non-convex. To solve it, we optimize the reflection coefficient matrices by quadratic transformation with low-complexity closed-form expression, and the beamforming vector is handled by sparse beamforming optimization, where the l0 norm is approximated by the l1 norm. Finally, simulation results demonstrate the advantages of our proposed algorithm compared with benchmark schemes, e.g., 5 dB SINR gain can be achieved compared with existing algorithm.

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“…For more complicated multi-BS cellular networks, the user association is one of the important procedures to further enhance the spectral efficiency [6]. Thus, the authors in [7] and [8] considered the optimization of RIS beamforming in a multi-BS cellular system and investigated the RIS-user and BS-user association, respectively. Furthermore, relationship between BSs and RISs are studied in [9], which introduces a BS-RIS-user association method to maximize the utility of users in a single-input single-output (SISO) downlink wireless network.…”

Section: Introductionmentioning

confidence: 99%

BS-RIS-User Association and Beamforming Designs for RIS-aided Cellular Networks

Liu¹,

Ni²,

Liu³

et al. 2021

Preprint

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Reconfigurable intelligent surface (RIS) has been regarded as a revolutionary and promising technology owing to its powerful feature of adaptively shaping wireless propagation environment. However, as a frequency-selective device, the RIS can only effectively provide tunable phase-shifts for signals within a certain frequency band. Thus, base-station (BS)-RISuser association is an important issue to maximize the efficiency and ability of the RIS in cellular networks. In this paper, we consider a RIS-aided cellular network and aim to maximize the sum-rate of downlink transmissions by designing BS-RIS-user association as well as the active and passive beamforming of BSs and RIS, respectively. A dynamically successive access algorithm is developed to design the user association. During the dynamical access process, an iterative algorithm is proposed to alternatively obtain the active and passive beamforming. Finally, the optimal BS-RIS association is obtained by an exhaustive search method. Simulation results illustrate the significant performance improvement of the proposed BS-RIS-user association and beamforming design algorithm.

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Joint Passive Beamforming and User Association Optimization for IRS-assisted mmWave Systems

Cited by 27 publications

References 13 publications

Neural Network Based IRSs-UEs Association and IRSs Optimal Placement in Multi IRSs Aided Wireless System

Neural Network Based IRSs-UEs Association and IRSs Optimal Placement in Multi IRSs Aided Wireless System

Intelligent Reflecting Surface-Assisted mmWave Communication With Lens Antenna Array

BS-RIS-User Association and Beamforming Designs for RIS-aided Cellular Networks

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