Joint Communications and Sensing Hybrid Beamforming Design via Deep Unfolding

Joint communications and sensing (JCAS) is envisioned as a key feature in future wireless communications networks. In massive MIMO-JCAS systems, hybrid beamforming (HBF) is typically employed to achieve satisfactory beamforming gains with reasonable hardware cost and power consumption. Due to the coupling of the analog and digital precoders in HBF and the dual objective in JCAS, JCAS-HBF design problems are very challenging and usually require highly complex algorithms. In this paper, we propose a fast HBF design for JCAS based on deep unfolding to optimize a tradeoff between the communications rate and sensing accuracy. We first derive closed-form expressions for the gradients of the communications and sensing objectives with respect to the precoders and demonstrate that the magnitudes of the gradients pertaining to the analog precoder are typically smaller than those associated with the digital precoder. Based on this observation, we propose a modified projected gradient ascent (PGA) method with significantly improved convergence. We then develop a deep unfolded PGA scheme that efficiently optimizes the communications-sensing performance tradeoff with fast convergence thanks to the well-trained hyperparameters. In doing so, we preserve the interpretability and flexibility of the optimizer while leveraging data to improve performance. Finally, our simulations demonstrate the potential of the proposed deep unfolded method, which achieves up to 33.5% higher communications sum rate and 2.5 dB lower beampattern error compared with the conventional design based on successive convex approximation and Riemannian manifold optimization. Furthermore, it attains up to a 65% reduction in run time and computational complexity with respect to the PGA procedure without unfolding.

Analysis of Oversampling in Uplink Massive MIMO-OFDM with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have emerged as an efficient solution for massive multiple-input multiple-output (MIMO) systems to reap high data rates with reasonable power consumption and hardware complexity. In this paper, we analyze the performance of oversampling in uplink massive MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems with low-resolution ADCs. Considering both the temporal and spatial correlation of the quantization distortion, we derive an approximate closed-form expression of an achievable sum rate, which reveals how the oversampling ratio (OSR), the ADC resolution, and the signal-to-noise ratio (SNR) jointly affect the system performance. In particular, we demonstrate that oversampling can effectively improve the sum rate by mitigating the impact of the quantization distortion, especially at high SNR and with very low ADC resolution. Furthermore, we show that the considered low-resolution massive MIMO-OFDM system can achieve the same performance as the unquantized one when both the SNR and the OSR are sufficiently high. Numerical simulations confirm our analysis.

Deep Unfolding Enabled Constant Modulus Waveform Design for Joint Communications and Sensing

Joint communications and sensing (JCAS) systems have recently emerged as a promising technology to utilize the scarce spectrum in wireless networks and to reuse the same hardware to save infrastructure costs. In practical JCAS systems, dual functional constant-modulus waveforms can be employed to avoid signal distortion in nonlinear power amplifiers. However, the designs of such waveforms are very challenging due to the nonconvex constant-modulus constraint. The conventional branch-and-bound (BnB) method can achieve optimal solution but at the cost of exponential complexity and long run time. In this paper, we propose an efficient deep unfolding method for the constant-modulus waveform design in a multiuser multiple-input multiple-output (MIMO) JCAS system. The deep unfolding model has a sparsely-connected structure and is trained in an unsupervised fashion. It achieves good communications-sensing performance tradeoff while maintaining low computational complexity and low run time. Specifically, our numerical results show that the proposed deep unfolding scheme achieves a similar achievable rate compared to the conventional BnB method with 30 times faster execution time.

Fairness Enhancement of UAV Systems with Hybrid Active-Passive RIS

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV's location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38%-63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

Joint Communications and Sensing Design for Multi-Carrier MIMO Systems

In conventional joint communications and sensing (JCAS) designs for multi-carrier multiple-input multiple-output (MIMO) systems, the dual-functional waveforms are often optimized for the whole frequency band, resulting in limited communications--sensing performance tradeoff. To overcome the limitation, we propose employing a subset of subcarriers for JCAS, while the communications function is performed over all the subcarriers. This offers more degrees of freedom to enhance the communications performance under a given sensing accuracy. We first formulate the rate maximization under the sensing accuracy constraint to optimize the beamformers and JCAS subcarriers. The problem is solved via Riemannian manifold optimization and closed-form solutions. Numerical results for an 8x4 MIMO system with 64 subcarriers show that compared to the conventional subcarrier sharing scheme, the proposed scheme employing 16 JCAS subcarriers offers 60% improvement in the achievable communications rate at the signal-to-noise ratio of 10 dB. Meanwhile, this scheme generates the sensing beampattern with the same quality as the conventional JCAS design.

Beam Squint Analysis and Mitigation via Hybrid Beamforming Design in THz Communications

We investigate the beam squint effect in uniform planar arrays (UPAs) and propose an efficient hybrid beamforming (HBF) design to mitigate the beam squint in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems operating at terahertz band. We first analyze the array gain and derive the closed-form beam squint ratio that characterizes the severity of the beam squint effect on UPAs. The effect is shown to be more severe with a higher fractional bandwidth, while it can be significantly mitigated when the shape of a UPA approaches a square. We then focus on the HBF design that maximizes the system spectral efficiency. The design problem is challenging due to the frequency-flat nature and hardware constraints of the analog beamformer. We overcome the challenges by proposing an efficient decoupling design in which the digital and analog beamformers admit closed-form solutions, which facilitate practical implementations. Numerical results validate our analysis and show that the proposed HBF design is robust to beam squint, and thus, it outperforms the state-of-the-art methods in wideband massive MIMO systems.

AI-Empowered Hybrid MIMO Beamforming

Hybrid multiple-input multiple-output (MIMO) is an attractive technology for realizing extreme massive MIMO systems envisioned for future wireless communications in a scalable and power-efficient manner. However, the fact that hybrid MIMO systems implement part of their beamforming in analog and part in digital makes the optimization of their beampattern notably more challenging compared with conventional fully digital MIMO. Consequently, recent years have witnessed a growing interest in using data-aided artificial intelligence (AI) tools for hybrid beamforming design. This article reviews candidate strategies to leverage data to improve real-time hybrid beamforming design. We discuss the architectural constraints and characterize the core challenges associated with hybrid beamforming optimization. We then present how these challenges are treated via conventional optimization, and identify different AI-aided design approaches. These can be roughly divided into purely data-driven deep learning models and different forms of deep unfolding techniques for combining AI with classical optimization.We provide a systematic comparative study between existing approaches including both numerical evaluations and qualitative measures. We conclude by presenting future research opportunities associated with the incorporation of AI in hybrid MIMO systems.

Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems

Hybrid beamforming (HBF) is a key enabler for wideband terahertz (THz) massive multiple-input multiple-output (mMIMO) communications systems. A core challenge with designing HBF systems stems from the fact their application often involves a non-convex, highly complex optimization of large dimensions. In this paper, we propose HBF schemes that leverage data to enable efficient designs for both the fully-connected HBF (FC-HBF) and dynamic sub-connected HBF (SC-HBF) architectures. We develop a deep unfolding framework based on factorizing the optimal fully digital beamformer into analog and digital terms and formulating two corresponding equivalent least squares (LS) problems. Then, the digital beamformer is obtained via a closed-form LS solution, while the analog beamformer is obtained via ManNet, a lightweight sparsely-connected deep neural network based on unfolding projected gradient descent. Incorporating ManNet into the developed deep unfolding framework leads to the ManNet-based FC-HBF scheme. We show that the proposed ManNet can also be applied to SC-HBF designs after determining the connections between the radio frequency chain and antennas. We further develop a simplified version of ManNet, referred to as subManNet, that directly produces the sparse analog precoder for SC-HBF architectures. Both networks are trained with an unsupervised training procedure. Numerical results verify that the proposed ManNet/subManNet-based HBF approaches outperform the conventional model-based and deep unfolded counterparts with very low complexity and a fast run time. For example, in a simulation with 128 transmit antennas, it attains a slightly higher spectral efficiency than the Riemannian manifold scheme, but over 1000 times faster and with a complexity reduction of more than by a factor of six (6).

Network-Aided Intelligent Traffic Steering in 6G ORAN: A Multi-Layer Optimization Framework

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (ORAN). So far, however, the applicability of ORAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in ORAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

Switch-based Hybrid Beamforming Transceiver Design for Wideband Communications with Beam Squint

The frequency-selectivity of beamforming, known as the beam squint, significantly impacts the performance of a system with large signal bandwidths. Standard hybrid beamforming (HBF) transceiver architectures based on frequency-independent phase shifters (PS-HBF) are sensitive to the phases and physical directions with limited capability to compensate for the detrimental effects of the beam squint. Motivated by the fact that switches are phase-independent and more power/cost efficient than phase shifters, we consider the switch-based HBF (SW-HBF) for wideband large-scale multiple-input multiple-output systems in this paper. We first derive a closed-form expression of the beam squint ratio of the PS-HBF architecture and compare the expected array gains of both SW-HBF and PS-HBF architectures. The results show that SW-HBF is more robust to the beam squint effect. We then focus on the SW-HBF design to maximize the system spectral efficiency. The formulated problem is a non-convex mixed-integer program. We propose three efficient suboptimal SW-HBF algorithms based on tabu search and projected gradient ascend. Simulations show that the proposed SW-HBF schemes achieve near-optimal performance with low complexity. Furthermore, they attain up to 26% spectral efficiency and 90% energy efficiency enhancements compared to the PS-HBF scheme.