Optimal Beamforming Research Articles

Beamforming and data detection in intelligent reflecting surfaces‐assisted communication using deep learning

SummaryIn this article, we propose two deep neural network (DNN) architectures for an intelligent reflecting surface (IRS)‐assisted network with a single transmitter and multiple receivers. The first DNN is designed to obtain an optimal beamforming weight vector that maximizes the achievable sum rate over the IRS‐aided network. The beamforming vector is designed based on a pilot‐based channel estimation procedure, which requires only a small fraction of the available number of reflecting surfaces on the IRS to be active. The obtained vector is fed to the second DNN, which is designed for detecting information symbols from the transmitter. Our simulation study shows that the achievable rate obtained using the designed beamforming vector approaches the maximum achievable rate quickly, as the number of active elements increases. The impact of the number of active elements and the beamforming vector on the performance of the detector is also studied, in terms of the bit error rate (BER). We show that the BER performance of the second DNN is close to that of the optimal maximum likelihood detector. Further, we study the effect of the channel estimation errors on the performance of the DNNs. Moreover, we study the effect of the hyperparameters of the architectures on the training time and performance, in terms of root mean squared error and accuracy.

LMA-SLN beamforming optimization for sum-rate maximization in intelligent reflecting surface assisted NOMA Systems

Intelligent reflecting surfaces (IRS) can effectively improve the system performance of non-orthogonal multiple access (NOMA) systems. In this paper, we propose a Levenberg–Marquardt algorithm-based supervised learning network (LMA-SLN) to maximize the sum-rate of an IRS-assisted NOMA system. By decoupling the sum-rate maximization problem into the active and passive beamforming optimization sub-problems, we design an alternating optimization scheme to optimize the active and passive beamformings. Then, the LMA-SLN is trained with ideal channel state information (CSI) to obtain the optimized network parameters. Finally, the trained LMA-SLN is applied to optimize the active and passive beamformings without requiring CSI. The experimental results show that the proposed LMA-SLN scheme achieves the superior performance on improving the sum-rate.

Heterogeneous Drone Small Cells: Optimal 3D Placement for Downlink Power Efficiency and Rate Satisfaction

In this paper, we delve into the domain of heterogeneous drone-enabled aerial base stations, each equipped with varying transmit powers, serving as downlink wireless providers for ground users. A central challenge lies in strategically selecting and deploying a subset from the available drone base stations (DBSs) to meet the downlink data rate requirements while minimizing the overall power consumption. To tackle this, we formulate an optimization problem to identify the optimal subset of DBSs, ensuring wireless coverage with an acceptable transmission rate in the downlink path. Moreover, we determine their 3D positions for power consumption optimization. Assuming DBSs operate within the same frequency band, we introduce an innovative, computationally efficient beamforming method to mitigate intercell interference in the downlink. We propose a Kalai–Smorodinsky bargaining solution to establish the optimal beamforming strategy, compensating for interference-related impairments. Our simulation results underscore the efficacy of our solution and offer valuable insights into the performance intricacies of heterogeneous drone-based small-cell networks.

Multi-Objective NSGA-II Optimization for Broadband Beamforming with Spherical Harmonic Domain Assistance.

Sidelobe suppression is a major challenge in wideband beamforming for acoustic research, especially in high noise and reverberation environments. In this paper, we propose a multi-objective NSGA-II wideband beamforming method based on a spherical harmonic domain for spherical microphone arrays topology. The method takes white noise gain, directional index and maximum sidelobe level as the optimization objectives of broadband beamforming, adopts the NSGA-II optimization strategy with constraints to estimate the Pareto optimal solution, and provides three-dimensional broadband beamforming capability. Our method provides superior sidelobe suppression across different spherical harmonic orders compared to commonly used multi-constrained single-objective optimal beamforming methods. We also validate the effectiveness of our proposed method in a conference room setting. The proposed method achieves a white noise gain of 8.28 dB and a maximum sidelobe level of -23.42 dB at low frequency, while at high frequency it yields comparable directivity index results to both DolphChebyshev and SOCP methods, but outperforms them in terms of white noise gain and maximum sidelobe level, measuring 16.14 dB and -25.18 dB, respectively.

Sensing-Efficient NOMA-Aided Integrated Sensing and Communication: A Joint Sensing Scheduling and Beamforming Optimization

Integrated sensing and communication (ISAC) system has been expected to play a vital role in future wireless networks and services. In this paper, we investigate a non-orthogonal multiple access (NOMA)-aided ISAC system in which the ISAC base station utilizes NOMA to serve multiple NOMA-users while performing radar sensing towards a group of sensing targets by leveraging the superimposed NOMA signals for NOMA-users. To investigate this problem, we formulate a joint optimization of the beamforming, the NOMA transmission duration and the sensing scheduling of sensing targets, with the objective of maximizing the sensing efficiency (i.e., the number of sensed targets within a time-interval) of ISAC system, while satisfying both the communication quality requirement of each NOMA-user and the sensing quality requirement of each selected targets. Despite that the formulated joint optimization problem is strictly non-convex, we propose a decomposition-based algorithm for solving the problem. Specifically, we exploit the successive convex approximation and penalty function method to equivalently transform the problem of optimizing the beamforming into a convex problem which can be addressed efficiently. Furthermore, with the optimal beamforming, we leverage the monotonic feature and adopt a bisection-search method to determine the NOMA transmission duration. Finally, we utilize an algorithm based on the cross-entropy learning to compute the optimal sensing scheduling. Numerical results verify the effectiveness of our proposed algorithms and show the performance advantage of our NOMA-aided ISAC system. Compared with some benchmark schemes, our NOMA-aided ISAC sensing scheduling scheme can achieve better performance in communication and sensing.

Joint Optimization of Bitrate Selection and Beamforming for Holographic Video Cooperative Streaming in VLC Systems

Joint Optimization of Bitrate Selection and Beamforming for Holographic Video Cooperative Streaming in VLC Systems

Differential beamforming using Rotman lens for wireless sensor networks

In this article, an optimal beamforming technique using a low cost intelligent Rotman lens antenna is proposed. The proposed technique enjoys full diversity with high coding gain, has a very low encoding and decoding complexity, and does not require channel knowledge at any transmitting or receiving antenna. From our simulation results, the proposed technique outperforms the best known non-coherent multi-antenna techniques. Furthermore, we prove that the BER expressions obtained from the theoretical analysis enjoy full diversity gain and match our conducted simulations.

Piecewise-DRL: Joint Beamforming Optimization for RIS-Assisted MU-MISO Communication System

With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), communication between users of different devices has become increasingly close. In practical scenarios, obstacles present between the transceiver may cause a deterioration in the quality of the received signals. Therefore, the reconfigurable intelligent surface (RIS) is employed to create virtual Line-of-Sight (LoS) channels in an IoT network. Specifically, this paper aims at maximizing the sum-rate of the RIS-assisted multi-user multiple input single output (MU-MISO) communication systems by jointly optimizing the phase shift matrix of the RIS and transmit beamforming. To solve the formulated non-convex problem, a piecewise-deep reinforcement learning (DRL) algorithm is proposed in this paper. Unlike the existing alternative optimization (AO) algorithms, the proposed algorithm avoids falling into the local optimal by using an exploration mechanism. Moreover, piecewise-DRL can reduce the action dimension, allowing the algorithm to obtain faster convergence. Simultaneously, this algorithm also ensures that the parameters of the two-part networks are updated to generate a larger system sum-rate by unsupervised joint optimization. Simulations in various circumstances reveal that the proposed approach is more robust and presents better stability and faster convergence than previous state-of-the-art algorithms while obtaining competitive performance.

Joint Passive Beamforming and Deployment Design for Dual Distributed-IRS Aided Communication

Intelligent reflecting surface (IRS) has emerged as a promising solution to enhance communication performance via passive signal reflection. In this paper, we propose a dual distributed-IRS aided communication system, where two IRSs are deployed in a distributed manner to extend the communication coverage of an access point (AP) to a target area. Specifically, we aim to maximize the worst-case signal-to-noise ratio (SNR) over all locations in the target area by jointly optimizing the passive beamforming and deployment for the two distributed IRSs. To solve this problem, we propose an efficient two-step algorithm by first designing the IRSs' passive beamforming optimization given IRSs' locations and then optimizing the IRS deployment. Our simulation results show that given the same total number of reflecting elements, deploying two distributed IRSs can achieve much better coverage performance than deploying one centralized IRS.

Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback.

Multiple Input and Multiple Output (MIMO) is a promising technology to enable spatial multiplexing and improve throughput in wireless communication networks. To obtain the full benefits of MIMO systems, the Channel State Information (CSI) should be acquired correctly at the transmitter side for optimal beamforming design. The analytical centre-cutting plane method (ACCPM) has shown to be an appealing way to obtain the CSI at the transmitter side. This paper adopts ACCPM to learn down-link CSI in both single-user and multi-user scenarios. In particular, during the learning phase, it uses the null space beamforming vector of the estimated CSI to reduce the power usage, which approaches zero when the learned CSI approaches the optimal solution. Simulation results show our proposed method converges and outperforms previous studies. The effectiveness of the proposed method was corroborated by applying it to the scattering channel and winner II channel models.

Development of an Optimum Beamforming Strategy for Outdoor Communication in Millimeter Wave Wireless Sensor Network

Development of an Optimum Beamforming Strategy for Outdoor Communication in Millimeter Wave Wireless Sensor Network

Covert Ambient Backscatter Communications With Multi-Antenna Tag

This paper presents a unicast beamforming network for covert ambient backscatter communications (AmBC). Different from the prior covert backscatter works using artificial noise or a power-variable RF source, our work achieves covertness by arming the backscatter tag with multiple antennas. The tag uses a subset of its antennas to modulate covert information on backscattered RF signals while using the other antennas to modulate overt message as a cover to hide the covert transmission from the warden. We first derive the warden’s optimum detection threshold to minimize the detection error probability. To fight against the optimum warden, the tag changes impedance matching on each antenna to vary the reflected power and make the reflected overt and covert signals beamforming at space. We derive the optimum beamforming vectors by solving the problems of maximizing the covert rate at the receiver and the detection error probability at the warden, respectively. To address the non-convex constraints, we use semi-definite relaxation (SDR) and obtain near optimal parameters by designing binarySearch algorithms. Numerical results confirm the superiority of our work to the state-of-the-art one, and the existence of tradeoffs between the covert rate and the detection error probability.

Robust Space-Time Adaptive Processing Method for GNSS Receivers in Coherent Signal Environments

In the coherent signal environments caused by multipath propagation, the interference suppression performance of the global navigation satellite systems (GNSS) receivers decreases sharply. In this paper, a robust space-time adaptive processing (STAP) method for GNSS receivers is proposed to suppress interferences in coherent signal environments, by using the modified space-time two-dimensional iterative adaptive approach (ST2D-IAA) spectrum estimation. This method applies the IAA algorithm to the ST2D signal model of GNSS receivers, and further modifies the ST2D-IAA algorithm to accurately estimate the power spectrum and noise power simultaneously. The space-time interference-plus-noise covariance matrix (STINCM) is reconstructed by using the estimated power spectrum and noise power in the interference angle region. Based on the reconstructed STINCM, we construct the STAP beamforming optimization problem for the space-time steering vector (STSV) error vector, and further correct the STSV of GNSS signal. Finally, the weight vector of STAP beamforming is calculated by using the reconstructed STINCM and the corrected STSV of GNSS signal. Simulation results show that the proposed method can suppress interferences in coherent signal environments and outperforms the current methods.

A Deep Learning Framework for Adaptive Beamforming in Massive MIMO Millimeter Wave 5G Multicellular Networks

The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network has the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system-level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, the EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required with respect to the non-ML framework.

An Energy-Efficient Optimization Method for High-Speed Rail Communication Systems Assisted by Intelligent Reflecting Surfaces (IRS)

This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages between base stations and trains. The joint optimization of base station beamforming and IRS phase shifts is formulated as a variable-coupled energy efficiency maximization problem, subject to the base station’s transmission power and the IRS unit’s modulus constraints. This is known to be an NP-hard problem, making it challenging to obtain the global optimal solution. To tackle the issue of optimization variable coupling, an alternating optimization is employed to decompose the original problem into two sub-problems: base station beamforming and IRS phase-shift optimization. The Dinkelbach method is utilized to convert the fractional objective function into a difference form; then, the successive convex approximation (SCA) algorithm is applied to transform non-convex constraints into convex ones, which are solved using CVX. The Riemann conjugate gradient (RCG) algorithm can effectively solve the difficult unit module constraint. Finally, an alternating iterative strategy is employed to converge to a suboptimal solution. Our simulation results demonstrate that the proposed algorithm significantly enhances system efficiency with low computational complexity. Specifically, when the number of IRS reflecting elements is 64, the system’s EE is improved by approximately 12.41%, 35.26%, and 37.96% compared to the semi-definite relaxation algorithm, the random phase shift approach, and no IRS scheme, respectively.

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio (CR), cooperative spectrum sensing (CSS) employs a fusion of multiple decisions from various secondary user (SU) nodes at a central fusion center (FC) to detect spectral holes not utilized by the primary user (PU). The energy detector (ED) is a well-established technique of spectrum sensing (SS). However, a major challenge in designing an energy detector-based SS is the requirement of correct knowledge for the distribution of decision statistics. Usually, the Gaussian assumption is employed for the received statistics, which is not true in real practice, particularly with a limited number of samples. Another big challenge in the CSS task is choosing an optimal fusion strategy. To tackle these issues, we have proposed a beamforming-assisted ED with a heuristic-optimized CSS technique that utilizes a more accurate distribution of decision statistics by employing the characterization of the indefinite quadratic form (IQF). Two heuristic algorithms, genetic algorithm with multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO), are developed to design optimum beamforming and optimum fusion weights that can maximize the global probability of detection pd while constraining the global probability of false alarm pf to below a required level. The simulation results are presented to validate the theoretical findings and to asses the performance of the proposed algorithm.

Joint Unmanned Aerial Vehicle Location and Beamforming and Caching Optimization for Cache-Enabled Multi-Unmanned-Aerial-Vehicle Networks

Due to the advantages such as high flexibility, low cost and easy implementation offered by unmanned aerial vehicles (UAVs), a UAV-assisted network is regard as an appealing solution to a seamless coverage, high disaster-tolerant and on-demand wireless system. In this paper, we focus on the downlink transmission in a cache-enabled UAV-assisted wireless communication network, where UAVs cache popular content from a macro base station in advance and cooperatively transfer the content to users. We aim to minimize the average transmission latency of the system and to formulate an optimization problem that jointly optimizes the UAV location, beamforming and caching strategy. However, the formulated problem is very challenging because of its non-convexity and the highly coupled optimization variables. To solve this resulting problem efficiently, we decompose it into two subproblems, namely UAV location and beamforming optimization, and UAV caching strategy optimization. The first subproblem is an NP-hard joint optimization problem, while the second one is a linear programing problem. By adopting the first-order Taylor expansion, we propose a convex optimization algorithm based on the difference-of-convex (DC) method. Specifically, we bring out a method to apply linear approximation in the DC-based algorithm, which is particularly suitable to the problems involving complicated summations. The numerical results demonstrate that the proposed DC-based iterative optimization algorithm can efficiently reduce the average transmission latency of the system.

Passive Reflection Optimization for IRS-Aided Multicast Beamforming With Discrete Phase Shifts

Intelligent reflecting surface (IRS) can bring promising advantages to enhance the communication system performance by reconfiguring wireless channels dynamically. The design of passive IRS reflection has attracted high attention and most existing works have considered continuous phase shift for IRS reflection. However, this is difficult to realize in practice due to hardware limitations and discrete phase shifts are applied for practical IRSs instead. In this letter, the information multicast beamforming problem in an IRS-aided multi-user system is considered, where the minimum receive signal-to-noise-ratio among the users is maximized by optimizing the IRS reflections with discrete phase shifts. By leveraging semi-definite relaxation (SDR) and gradient descent/ascent (GDA), two solutions are presented with high/low computational complexity, respectively. Simulation results show the efficacy of the proposed GDA-based algorithm as compared to the SDR-based and other benchmark schemes.

Joint Node Association and Beamforming for Millimeter Wave Networks With Multiple Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surface (RIS) is a promising technology to increase the spatial degrees-of-freedom in sparse millimeter wave (mmWave) channels. In RIS-assisted mmWave networks with multiple nodes, however, optimization of node-association and beamforming (BF) may require a large channel training overhead. This paper proposes a joint node-association and cooperative BF scheme to maximize the weighted sum rate with a low overhead based on a two-step design. In the first step, only slowly varying channel information are used to determine the node-association, analog active BF, and passive BF. In the second step, the baseband BF and power allocation are designed only using the effective channel information with reduced dimension. It is shown that the proposed scheme outperforms benchmark schemes and is scalable to the numbers of nodes.

Intelligent Reflecting Surface Backscatter Enabled Uplink Coordinated Multi-Cell MIMO Network

This paper proposes an intelligent reflecting surface (IRS) backscatter based uplink coordinated transmission strategy for a radio cellular network, where IRS serves as a transmitter enabling uplink transmission from each user to the associated base station (BS). To be specific, the considered network is made up of multiple cells, each of which consists of one multi-antenna BS and its served users. While one multi-antenna power beacon (PB) is deployed to radiate energy-bearing electromagnetic wave, the radio signal received by each IRS is modulated to send its connective user’s information to the associated BS. Based on such a network frame, this paper aims to maximize the weighted sum rate (WSR) under the constraints of total transmit power and reflecting phase shifts by joint optimization of active beamforming at the PB, passive beamforming at the IRSs and uplink user scheduling. To address this challenging problem, fractional programming (FP), alternative optimization and weighted bipartite matching are employed to convert the logarithm objective function into a more tractable form and to handle the optimization variables. The simulation results demonstrate the achievable WSR of the considered network.