Downlink Beamforming Research Articles

A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization

A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization

Hybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization

Hybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization

Space-code division multiple access for broadband acoustic networks

We present an investigation into the design of an acoustic communication network, where multiple users are distributed across space and transmit and receive simultaneously in the same band to and from a common base station. Specifically, we focus on a system that utilizes orthogonal frequency division multiplexing as a modulation method, and allows users to transmit and receive in either synchronous or asynchronous fashion. To distinguish between users on the uplink, the base station employs a combination of code-division and space-division multiple access. The base station iteratively steers a beam to each stable propagation path of the desired user’s channel while placing nulls in the direction of other paths, as well as in the directions of interfering users. Finally, the multiple paths of the desired user are recombined before data detection. The process is repeated for each user. Broadband beamforming is employed to account for the broadband nature of acoustic signals. The beamformer coefficients on each carrier depend on the angles of signal arrivals, which are estimated during the uplink transmission and used to construct both the uplink and the downlink beamformer. On the downlink, the base station utilizes angle information to assemble beamforming weights and point in the direction of stable paths of the users. It superimposes multiuser signals and transmits the sum signal to the users. The signal intended for a given user reaches only that user, requiring just a simple detector. Each user is equipped by a single-element transducer. To demonstrate the design concepts, we conducted simulations using a shallow water channel model and performed experimental over-the-air tests in an indoor environment using an acoustic communications testbed. The results were excellent, thereby encouraging future implementations in practical systems.

External Passive Intermodulation Suppression by Near-Field Downlink Beamforming

External Passive Intermodulation Suppression by Near-Field Downlink Beamforming

A Survey on Challenges of Millimeter Wave Communications for Fifth-Generation Wireless Networks

Abstract-Cellular networks rely heavily on downlink beamforming; however, calculating beamformers that maximize the weighted sum rate (WSR) under power constraints is an NP-hard problem. A weighted minimum mean square error (WMMSE) algorithm variant is suggested for a MU-MISO downlink channel in order to address this trade-off between complexity and performance. This variation avoids bisection searches, eigendata compositions, and matrix inversions—all of which are challenging to implement as conventional network layers. With a reduced complexity, the suggested network architecture outperforms the WMMSE algorithm truncated to the same number of iterations and adapts well to variations in the channel distribution. Many use cases for vehicles that demand high capacity, extremely low latency, and high reliability can be made possible by 5G. To facilitate this, 5G For a MU-MISO downlink channel, a variation of the weighted minimum mean square error (WMMSE) algorithm is proposed to address this trade-off between complexity and performance. This variation avoids complex network layer implementations such as matrix inversions, eigendata compositions, and bisection searches. The suggested network architecture generalizes well to changes in the channel distribution and performs comparably to the WMMSE algorithm truncated to the same number of iterations, but at a lower complexity. Numerous automotive use cases requiring high capacity, extremely low latency, and high reliability can be made possible by 5G. 5G suggests using highly directed mm Wave system deployment and dense small cell technology to support this. To reduce the cost of signaling, however, enabling vehicular communication necessitates strong mobility management strategies. KEYWORD: Millimetre wave communications, propagation, channel measurements, channel models, MIMO, hybrid precoding, non-orthogonal multiple access (NOMA), multiple access techniques, simultaneous wireless information and power transfer (SWIPT), RF energy harvesting.

Deep Reinforcement Learning-Based Downlink Beamforming and Phase Optimization for RIS-Aided Communication System

Deep Reinforcement Learning-Based Downlink Beamforming and Phase Optimization for RIS-Aided Communication System

Spanning Tree Method for Over-the-Air Channel Calibration in 6G Cell-Free Massive MIMO

Cell-free massive multiple-input multiple-output (MIMO) is an attractive network in 6G communications that significantly increases the spectral efficiency. Operating in time-division duplex (TDD) mode, the downlink beamforming is achieved by the estimated uplink channel, which is equal to the downlink channel due to the property of channel reciprocity. However, the involvement of different radio frequency (RF) gains in transceiver antennas renders the whole channel non-reciprocal. Therefore, it is of great necessity to calibrate the bi-directional channel. In this paper, we focus on the issue of over-the-air channel calibration in cell-free system. Taking a toy scenario as an example, we examine the performance differences between the calibration methods of ‘Direct Process’ and ‘Indirect Process’. A novel low-cost calibration method based on spanning tree model is proposed specifically for this distributed AP scenario, where a calibration tree is established to calculate calibration coefficients. Numerical results manifest that higher accuracy of our method is achieved compared to the existing calibration methods in the literatures. Our method is less sensitive to the location of master AP compared to Argos, and practical applications under the impact of phase noise show the priority of our method compared to LS method.

Downlink massive full dimension-multiple input multiple output downlink beamforming analysis at 3.5 GHz using coordinated ON-OFF switching

<p><span lang="EN-US">The long-term evolution and advancement (LTE-A) of the 5G wireless network depends critically on energy consumption. Many existing solutions focus on limiting power constraints and consequently system coverage. So, improving the antenna array elements of the base station (BS) can solve this issue. In this paper, introduce a coordinated ON-OFF switching method in the massive full dimensional multiple input multiple output (massive-FD-MIMO) system. It enhances the radiation pattern of the antenna array element by adjusting the angular power spectra at the BS. By the way, it allows to select the minimum number of antennas for effective beamforming toward specific user equipment’s (UEs). In this context, part of antenna element should be active mode and remining should be sleep mode at the time of signal beamforming. The multipath spatial profiles are decided the beamforming frequency band with minimize energy consumption. As part of the method, we used a conjugated beamforming with power optimization scheme to determine the individual antenna potential and fading channel condition, power optimization is performed. This method quality of service, reliability, energy consumption and data rate can all be evaluated by experimenting with different-sized antenna arrays such as 16×16, 32×32, 64×64 and 128×128.</span></p>

Latency Minimization for IRS-Aided NOMA MEC Systems With WPT-Enabled IoT Devices

Mobile edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks, and thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are colocated with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This paper applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency division and non-orthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multi-variable non-convex problem, we split the target problem into several sub-problems and provide a nearoptimal low-complexity ID clustering scheme. Afterwards, we derive optimal solutions to these sub-problems, and a lowcomplexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.

Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink

Full-dimensional (FD) multi-user massive multiple-input multiple output (m-MIMO) systems employ large two-dimensional (2D) rectangular antenna arrays to control both the azimuth and elevation angles of signal transmission. We introduce the sum of two outer products of the azimuth and elevation beamforming vectors having moderate dimensions as a new class of FD beamforming. We show that this low-complexity class is capable of outperforming 2D beamforming relying on the single outer product of the azimuth and elevation beamforming vectors. It is also capable of performing close to its FD counterpart of massive dimensions in terms of either the users' minimum rate or their geometric mean rate (GM-rate), or sum rate (SR). Furthermore, we also show that even FD beamforming may be outperformed by our outer product-based improper Gaussian signaling solution. Explicitly, our design is based on low-complexity algorithms relying on convex problems of moderate dimensions for max-min rate optimization or on closed-form expressions for GM-rate and SR maximization.

Optimal Energy Signal Design for Multiuser MISO WPCNs With Non-Linear Energy Harvesting Circuits

The optimal energy signal design for wireless powered communication networks (WPCNs) enabling energy-sustainable communication for a large number of low-power devices is still an open problem in practical systems. In this work, we study a multi-user WPCN, where a multi-antenna base station (BS) sends an energy signal to multiple single-antenna users, which, in turn, harvest energy from the received signal and utilize it for information transmission in the uplink. In contrast to the existing works on multiple-input single-output (MISO) WPCN design, in this paper, we jointly optimize the energy signal waveform and downlink beamforming at the BS for energy harvesting (EH) devices described by non-linear circuit-based models. To this end, we assume that the BS broadcasts a pulse-modulated signal employing multiple energy signal vectors and we formulate an optimization problem for the joint design of the downlink transmit energy signal vectors, their number, the durations of the transmit pulses, and the time allocation policy for minimization of the average transmit power at the BS. We show that for single-user WPCNs, a single energy signal vector, which is collinear with the maximum ratio transmission (MRT) vector and drives the EH circuit at the user device into saturation, is optimal. Next, for the general multi-user case, we show that the optimal signal design requires a maximum number of energy signal vectors that exceeds the number of users by one and propose an algorithm to obtain the optimal energy signal vectors. Since the complexity of the optimal design is high, we also propose two suboptimal schemes for WPCN design. First, for asymptotic massive WPCNs, where the ratio of the number of users to the number of BS antennas, i.e., the system load, tends to zero, we show that the optimal downlink transmit signal can be obtained in closed-form and comprises a sequence of weighted sums of MRT vectors. Next, based on this result, for general WPCNs with finite system loads, we propose a suboptimal closed-form MRT-based design and a suboptimal semidefinite relaxation (SDR)-based scheme. Our simulation results reveal that the proposed optimal scheme and suboptimal SDR-based design achieve nearly identical performance and outperform two baseline schemes, which are based on linear and sigmoidal EH models. Furthermore, we show that, if the system load of the WPCN is low, the performance gap between the proposed suboptimal solutions is small and becomes negligible as the number of BS antennas tends to infinity.

Handover-Aware Downlink Beamforming Design for LEO Multibeam Satellite Communications

Because of their capability of providing seamless connectivity, low-Earth-orbit (LEO) satellites will play a vital role in integrated satellite–terrestrial paradigms. However, high-speed satellite motion brings frequent handovers, during which redundant transmission and even loss of data easily occur. In this letter, we propose a handover-aware downlink beamforming design. To mitigate the impact of inter-satellite handover, we reduce the blocklength of coverage-edge users, aiming at completing transmission within the remaining visible time (RVT). Hence, the capacity of these users should be evaluated in the finite-block length (FBL) regime. After obtaining a surrogate of the FBL capacity function with a low-complexity method, we formulate the beamforming optimization model as a semidefinite programming (SDP) problem and solve it with CVX. The simulation results show that the proposed scheme can reach a 99.35% transmission success rate before inter-satellite handover.

Multi-Agent Deep Reinforcement Learning Based Downlink Beamforming in Heterogeneous Networks

We consider a heterogeneous network (HetNet), where multiple access points (APs) of potentially different transmission capacities serve users simultaneously via beamforming in the same spectrum band. We propose a beamforming framework that exploits multi-agent deep reinforcement learning (DRL) for the HetNet to maximize the system downlink sum-rate. In our framework, each AP acts as an agent, which is equipped with an online policy deep neural network (DNN) and an online Q-function DNN. The former generates an AP’s beamforming vector based only on local observations in a time slot, while the latter evaluates the appropriateness of this beamforming vector. We present a distributed-updating-centralized-rewarding scheme to train the policy DNNs and Q-function DNNs of all the APs in an online trial-and-error way. Under this scheme, all the APs take the system downlink sum-rate in a recent time slot (informed by a central controller) as their identical one-step reward. Trained by the experience items with centralized rewards in every time slot, the weight vectors of each AP’s local DNNs will be updated in the direction to the global optimum. Simulation results demonstrate that the proposed framework converges fast and outperforms the benchmark beamforming methods in terms of the system downlink sum-rate performance.

Joint Uplink Power Control, Downlink Beamforming, and Mode Selection for Secrecy Cell-Free Massive MIMO With Network-Assisted Full Duplexing

In this article, we investigate a secrecy cell-free massive multiple-input multiple-output system with network-assisted full duplexing for both noncolluding eavesdropper (Eve) and colluding Eve cases, where the access points (APs) are designed to serve downlink users and uplink users simultaneously and flexibly in the presence of Eves. To confirm the secrecy transmission, we consider the problem of jointly optimizing duplex mode selection and secrecy transceivers to maximize the overall secrecy spectral efficiency, where the information signals at APs are injected with artificial noise (AN) to prevent interception of information by Eves. Considering that downlink secure beamforming, uplink transmission power, and uplink receivers are tightly coupled in both the objective function and the constraints, we propose a two-loop strategy to solve the optimization problem. In particular, we propose a quantum-inspired tabu search algorithm to find a better duplex mode in the outer loop for each iteration. Then, for the inner loop, we propose a successive convex approximation based iterative algorithm to optimize the transceivers and AN by fixing the duplex mode selection vectors obtained from the outer loop. To address the proposed inner loop design subproblem, we resolve the subproblem by a series of nonconvex-convex approximate methods, such as the equivalent transformation and the iterative concave-convex procedure. The simulation results show that the proposed solution can achieve a higher secrecy spectral efficiency gain than the fixed-mode duplex scheme and simple greedy duplex mode selection schemes and achieve secrecy spectral efficiency performance similar to that of the optimal exhaustive search solution for both noncolluding Eve and colluding Eve cases.

Joint Optimization Framework for User Clustering, Downlink Beamforming, and Power Allocation in MIMO NOMA Systems

In this paper, we investigate the application of downlink beamforming along with non-orthogonal multiple access (NOMA) in a multi-user multiple-input multiple-output (MIMO) system. The joint optimization framework for user clustering, downlink beamforming and power allocation scheme is formulated as a novel mixed-integer non-linear program (MINLP), where the aim is to minimize the total transmission power while satisfying quality-of-service (QoS), user clustering and power constraints. Owing to the non-convexity and combinatorial nature of the problem, obtaining an optimal solution is challenging. To tackle this issue, we first develop an algorithm based on branch-and-bound (BB), whereby the feasible space is successively partitioned and searched by means of lower and upper bounds on the objective function. While this algorithm is shown to return an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -optimal solution within a finite number of iterations, it entails high computational complexity. Considering this limitation, we then reformulate the original problem into a more tractable form and conceive a low-complexity algorithm for its solution based on the penalty dual-decomposition technique. The proposed joint design algorithms for MIMO NOMA are evaluated by means of simulations over mmWave channels. Results show significant improvements in terms of total transmit power and spectral efficiency compared to benchmark approaches.

D2D Assisted Multi-Antenna Coded Caching

A device-to-device (D2D) aided multi-antenna coded caching scheme is proposed to improve the average delivery rate and reduce the downlink (DL) beamforming complexity. Novel beamforming and resource allocation schemes are proposed where local data exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange and DL transmission. In the D2D phase, subsets of users are selected to share content with the adjacent users directly. In this regard, a low complexity D2D mode selection algorithm is proposed to find the appropriate set of users for the D2D phase with comparable performance to the optimal exhaustive search. During the DL phase, the base station multicasts the remaining data requested by all the users. We identify scenarios and conditions where D2D transmission can reduce the delivery time. Furthermore, we demonstrate how adding the new D2D phase to the DL-only scenario can significantly reduce the beamformer design complexity in the DL phase. The results further highlight that by partly delivering requested data in the D2D phase, the transmission rate can be boosted due to more efficient use of resources during the subsequent DL phase. As a result, the overall content delivery performance is greatly enhanced, especially in the finite signal-to-noise (SNR) regime.

Continual Learning-Based Fast Beamforming Adaptation in Downlink MISO Systems

Beamforming is an effective way to improve spectrum efficiency in multi-antenna systems. However, conventional iterative algorithms suffering from high computational delay renders beamforming solutions outdated and deep learning (DL) based realtime solutions suffer from the “task mismatch” problem, leading to severe performance deterioration. To resolve these issues, this letter proposes a beamforming adaptation scheme, named continual learning based beamforming neural network (CL-BNN), which can continuously learn and optimize the downlink beamforming in dynamic environment. The proposed CL-BNN scheme is model-driven and uses domain/expert knowledge to reduce training complexity. By combining our customized selection criteria into the structure of the CL-BNN, which selects the appropriate samples from the existing data and newly received data into a memory set for model training, taking into account the objective function, such that the tradeoff between maintaining performance of the trained task and adapting to the new task is achieved. Simulation results show that the proposed CL-BNN scheme achieves good adaptability in dynamic environments with low complexity.

Multi-User Downlink Beamforming Using Uplink Downlink Duality With 1-Bit Converters for Flat Fading Channels

The increased power consumption of high-resolution data converters at higher carrier frequencies and larger bandwidths is becoming a bottleneck for communication systems. In this paper, we consider a fully digital base station equipped with 1-bit analog-to-digital (in uplink) and digital-to-analog (in downlink) converters on each radio frequency chain. The base station communicates with multiple single antenna users with individual SINR constraints. We first establish the uplink downlink duality principle under 1-bit hardware constraints under an uncorrelated quantization noise assumption. We then present a linear solution to the multi-user downlink beamforming problem based on the uplink downlink duality principle. The proposed solution takes into account the hardware constraints and jointly optimizes the downlink beamformers and the power allocated to each user. Optimized dithering obtained by adding dummy users to the true system users ensures that the uncorrelated quantization noise assumption is true under realistic settings. Detailed simulations carried out using 3GPP channel models generated from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quadriga</i> show that our proposed solution outperforms state of the art solutions in terms of the ergodic sum and minimum rate especially when the number of users is large. We also demonstrate that the proposed solution significantly reduces the performance gap from non-linear solutions in terms of the uncoded bit error rate at a fraction of the computational complexity.

Joint Design of Transmit Antenna Selection and Beamforming for MIMO NOMA Networks

This letter investigates the joint design of transmit antenna selection (AS) and beamforming for downlink multiple-input-multiple-output (MIMO) networks, in which non-orthogonal multiple access (NOMA) transmission scheme is applied. We aim to maximize the achievable sum rate under the AS constraint and successful successive interference cancellation (SIC) constraints. The penalty dual decomposition (PDD) method coupled with the weighted mean squared error minimization (WMMSE) method is proposed to tackle this problem and a local optimal solution is obtained. Simulation results are provided to show the effectiveness of the proposed scheme.

Learning How to Transfer From Uplink to Downlink via Hyper-Recurrent Neural Network for FDD Massive MIMO

In order to unlock the full advantages of massive multiple-input multiple-output (MIMO) in the downlink, the base station (BS) must leverage information about the downlink fading channels. However, in frequency division duplex (FDD) systems, full channel reciprocity does not hold, and acquiring information about the downlink channels generally requires downlink pilot transmission followed by uplink feedback. Prior work proposed to design pilot transmission, feedback, and channel state information (CSI) estimation, or directly downlink beamforming, via deep learning in an end-to-end manner. While previous work only used downlink pilots in a single slot, in this work, we introduce an enhanced end-to-end design that leverages partial uplink-downlink reciprocity and temporal correlation of the fading processes by utilizing jointly downlink and uplink pilots across multiple time slots. The proposed method is based on a novel deep learning architecture – HyperRNN – that combines hypernetworks and recurrent neural networks (RNNs) to optimize the transfer of long-term invariant channel features from uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a lower normalized mean square error (NMSE) performance in terms of channel estimation, and that it attains a larger achievable sum-rate when applied to multi-user beamforming, as compared to the state of the art.