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Most research studies on deep learning (DL) applied to the physical layer of wireless communication do not put forward the critical role of the accuracy-generalization trade-off in developing and evaluating practical algorithms. To highlight the disadvantage of this common practice, we revisit a data decoding example from one of the first papers introducing DL-based end-to-end wireless communication systems to the research community and promoting the use of artificial intelligence (AI)/DL for the wireless physical layer. We then put forward two key trade-offs in designing DL models for communication, namely, accuracy versus generalization and compression versus latency. We discuss their relevance in the context of wireless communications use cases using emerging DL models, including large language models (LLMs). Finally, we summarize our proposed evaluation guidelines to enhance the research impact of DL on wireless communications. These guidelines are an attempt to reconcile the empirical nature of DL research with the rigorous requirement metrics of wireless communications systems. 

Beam management is a strategy to unify beamforming and channel state information (CSI) acquisition with large antenna arrays in 5G. Codebooks serve multiple uses in beam management including beamforming reference signals, CSI reporting, and analog beam training. In this paper, we propose and evaluate a machine learning-refined codebook design process for extremely large multiple-input multiple- output (X-MIMO) systems. We propose a neural network and beam selection strategy to design the initial access and refinement codebooks using end-to-end learning from beamspace representations. The algorithm, called Extreme-Beam Management (X-BM), can significantly improve the performance of extremely large arrays as envisioned for 6G and capture realistic wireless and physical layer aspects. Our results show an 8dB improvement in initial access and overall effective spectral efficiency improvements compared to traditional codebook methods. 

Impedance-matching networks affect power transfer from the radio frequency (RF) chains to the antennas. Their design impacts the signal to noise ratio (SNR) and the achievable rate. In this paper, we maximize the information-theoretic achievable rate of a multiple-input-single-output (MISO) system with wideband matching constraints. Using a multiport circuit theory approach with frequency-selective scattering parameters, we propose a general framework for optimizing the MISO achievable rate that incorporates Bode-Fano wideband matching theory. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We apply this framework to a single electric Chu’s antenna and an array of dipole antennas. We compare the optimized achievable rate obtained numerically with other benchmarks like the ideal achievable rate computed by disregarding matching constraints and the achievable rate obtained by using sub-optimal matching strategies like conjugate matching and frequency-flat transmission. We also propose a practical methodology to approximate the achievable rate bound by using the optimal transmission coefficient to derive a physically realizable matching network through the ADS software. 

Reconfigurable arrays mold the propagation en- vironment to benefit wireless systems. We use single-port polarization-reconfigurable antennas in a wideband multiple- input multiple-output (MIMO) system and demonstrate the efficacy of reconfiguration techniques based on analytical channel models. We apply a double-directional channel model to show that polarization reconfiguration acts as an additional precoding step on an unpolarized channel. We use Jensen’s inequality to upper bound the spectral efficiency and leverage the relaxed objective to derive closed-form expressions for the optimal polarization angles at each antenna. We also derive upper bounds on the performance of a polarization reconfigurable system and develop an efficient procedure for polarization reconfiguration that aims to maximize these upper bounds. Numerical results show that the proposed simplified methods achieve near-optimal in wideband MIMO settings. 

Beam codebooks are a recent feature to en- able high dimension multiple-input multiple-output in 5G. Codebooks comprised of customizable beamforming weights can be used to transmit reference signals and aid the channel state information (CSI) acquisition process. Codebooks are also used for quantizing feedback follow- ing CSI measurement. In this paper, we unify the beam management stages–codebook design, beam sweeping, feed- back, and data transmission–to characterize the impact of codebooks throughout the process. We then design a neural network to find codebooks that improve the overall system performance. The proposed neural network is built on translating codebook and feedback knowledge into a consistent beamspace basis similar to a virtual channel model to generate initial access codebooks. This beamspace codebook algorithm is designed to directly integrate with current 5G beam management standards without changing the feedback format or requiring additional side infor- mation. Our simulations show that the neural network codebooks improve over traditional codebooks, even in dispersive sub-6GHz environments. We further use our framework to evaluate CSI feedback formats with regard to multi-user spectral efficiency. Our results suggest that optimizing codebook performance can provide valuable performance improvements, but optimizing the feedback configuration is also important in sub-6GHz bands. 

Multi-band operation in wireless networks can improve data rates by leveraging the benefits of propagation in different frequency ranges. Distinctive beam management procedures in different bands complicate band assignment because they require considering not only the channel quality but also the associated beam management overhead. Reinforcement learning (RL) is a promising approach for multi-band operation as it enables the system to learn and adjust its behavior through environmental feedback. In this paper, we formulate a sequential decision problem to jointly perform band assignment and beam management. We propose a method based on hierarchical RL (HRL) to handle the complexity of the problem by separating the policies for band selection and beam management. We evaluate the proposed HRL-based algorithm on a realistic channel generated based on ray-tracing simulators. Our results show that the proposed approach outperforms traditional RL approaches in terms of reduced beam training overhead and increased data rates under a realistic vehicular channel. 

Massive multiple-input multiple-output (MIMO) is an important technology in fifth generation (5G) cellular networks and beyond. To help design the beamforming at the base station, 5G has introduced new support in the form of flexible feedback and configurable antenna array geometries that allow for arbitrarily massive phys- ical arrays. In this article, we present an overview of MIMO throughout the mobile standards, highlight the new beam-based feedback system in 5G NR, and de- scribe how this feedback system enables massive MIMO through beam management. Finally, we conclude with challenges related to massive MIMO in 5G. 

Dynamic metasurface antennas (DMA) have been proposed for massive multiple-input multiple-output (MIMO) and millimeter wave applications due to their ability to cre- ate dense, energy-efficient arrays. In this paper, we integrate DMAs into a realistic wireless environment to compare their performance in spectral and energy efficiency with a conventional phased array. We implement a practical transmitter architecture for the DMA and phased array to account for the power consumption and hardware constraints of the radio frequency (RF) front end. Simulation results for a MISO scenario show that while the DMA performs worse in spectral efficiency than an active phased array, the power consumption savings from the reconfigurable component enable better performance in energy efficiency. Therefore, DMAs can provide an energy-efficient alternative to typical phased arrays.