Energy Efficiency of Rate-Splitting Multiple Access, and Performance Benefits over SDMA and NOMA

Rate-Splitting Multiple Access (RSMA) is a general and powerful multiple access framework for downlink multi-antenna systems, and contains Space-Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA) as special cases. RSMA relies on linearly precoded rate-splitting with Successive Interference Cancellation (SIC) to decode part of the interference and treat the remaining part of the interference as noise. Recently, RSMA has been shown to outperform both SDMA and NOMA rate-wise in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths and qualities of Channel State Information at the Transmitter). Moreover, RSMA was shown to provide spectral efficiency and QoS enhancements over NOMA at a lower computational complexity for the transmit scheduler and the receivers. In this paper, we build upon those results and investigate the energy efficiency of RSMA compared to SDMA and NOMA. Considering a multiple-input single-output broadcast channel, we show that RSMA is more energy-efficient than SDMA and NOMA in a wide range of user deployments (with a diversity of channel directions and channel strengths). We conclude that RSMA is more spectrally and energy-efficient than SDMA and NOMA.Comment: 6 pages, 5 figure

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a BS serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information, the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and QoS requirements. The JASPD overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tries all valid antenna subsets. Although approaching the (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a DNN to establish underlaying relations between the key system parameters and the selected antennas. The proposed L-ASPD is robust against the number of users and their locations, BS's transmit power, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves higher effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.Comment: accepted to the IEEE Transactions on Wireless Communication

An Adaptive Approach for the Joint Antenna Selection and Beamforming Optimization

Adaptive beamforming techniques are widely known for their capability of leveraging the performance of antenna arrays. The effectiveness of such techniques typically increases as the number of antennas grows. In contrast, computational and hardware costs very often limit the deployment of beamforming in large-scale arrays. To circumvent this problem, antenna selection strategies have been developed aiming to maintain much of the performance gain obtained by using a large array while keeping computational and hardware costs at acceptable levels. In this context, the present paper is dedicated to the development of two new adaptive algorithms for solving the problem of joint antenna selection and beamforming for uplink reception in mobile communication systems. Both algorithms are based on an alternating optimization strategy and are designed to operate with a limited number of radio-frequency chains. The main difference between the proposed algorithms is that the first is formulated by considering the minimum mean-square error (MMSE) criterion, while the second is based on the minimum-variance distortionless-response (MVDR) approach. The numerical simulation results confirm the effectiveness of the proposed algorithms

Hybrid Beamforming Design for Millimeter Wave Massive MIMO Communications

Wireless connectivity is a key driver for the digital transformation that is changing how people communicate, do business, consume entertainment and search for information. As the world advances into the fifth generation (5G) and beyond-5G (B5G) of wireless mobile technology, new services and use cases are emerging every day, bringing the demand to expand the broadband capability of mobile networks and to provide ubiquitous access and specific capabilities for any device or application. To fulfill these demands, 5G and B5G systems will rely on innovative technologies, such as the ultradensification, the mmWave, and the massive MIMO. To bring together these technologies, 5G and B5G systems will employ hybrid analog-digital beamforming, which separates the signal processing into the baseband (digital) and the radio-frequency (analog) domains. Unlike conventional beamforming, where every antenna is connected to an RF chain, and the signal is entirely processed in the digital domain, hybrid beamforming uses fewer RF chains than the total number of antennas, resulting in a less expensive and less energy-consuming design. The analog beamforming is usually implemented using switching networks or phase-shifting networks, which impose severe hardware constraints making the hybrid beamforming design very challenging. This thesis addresses the hybrid analog-digital beamforming design and is organized into three parts. In the first part, two adaptive algorithms for solving the switching-network-based hybrid beamforming design problem, also known as the joint antenna selection and beamforming (JASB) problem, are proposed. The adaptive algorithms are based on the minimum mean square error (MMSE) and minimum-variance distortionless response (MVDR) criteria and employ an alternating optimization strategy, in which the beamforming and the antenna selection are designed iteratively. The proposed algorithms can attain high levels of SINR while strictly complying with the hardware limitations. Moreover, the proposed algorithms have very low computational complexity and can track channel variations, making them suitable for non-stationary environments. Numerical simulations have validated the effectiveness of the algorithms in different operation scenarios. The second part addresses the phase-shifting-network-based hybrid beamforming design for narrowband mmWave massive MIMO systems. A novel joint hybrid precoder and combiner design is proposed. The analog precoder and combiner design is formulated as constrained low-rank channel decomposition, which can simultaneously harvest the array gain provided by the massive MIMO system and suppress intra-user and inter-user interferences. The constrained low-rank channel decomposition is solved as a series of successive rank-1 channel decomposition, using the projected block coordinate descent method. The digital precoder and combiner are obtained from the optimal SVD-based solution for the single-user case and the regularized channel diagonalization method for the multi-user case. Simulation results have demonstrated that the proposed design can consistently attain near-optimal performance and provided important insights into the method's convergence and its performance under practical phase-shifter quantization constraints. Finally, the phase-shifting-network-based hybrid beamforming design for frequency-selective mmWave massive MIMO-OFDM systems is considered in the third part. The hybrid beamforming design for MIMO-OFDM systems is significantly more challenging than for narrowband MIMO systems since, in these systems, the analog precoder and combiner are shared among all subcarriers and must be jointly optimized. Thus, by leveraging the OFDM systems' multidimensional structure, the analog precoder and combiner design is formulated as constrained low-rank Tucker2 tensor decomposition and solved by a successive rank-(1,1) tensor decomposition using the projected alternate least square (ALS) method. The digital precoder and combiner are obtained on a per-subcarrier basis using the techniques presented in the second part. Numerical simulations have confirmed the design effectiveness, demonstrating its ability to consistently attain near-optimal performance and outperform other existing design in nearly all scenarios. They also provided insights into the convergence of the proposed method and its performance under practical phase-shifter quantization constraints and highlighted the differences between this design and that for the narrowband massive MIMO systems