Multiple-input multiple-output (MIMO) communication has led to immense enhancements in data rates and efficient spectrum management. The evolution of MIMO, though, has been accompanied by increased hardware complexity and array sizes, causing the system power consumption to increase. Despite past advances in power-efficient hybrid architectures, new solutions are needed to enable extremely large-scale MIMO deployments for 6G and beyond. In this paper, we introduce a novel architecture that integrates low-power reconfigurable antennas with both digital and analog precoding. Thistri-hybridapproach addresses key limitations in traditional and hybrid MIMO systems by improving power consumption and adds a new layer for signal processing. We provide an analysis of the proposed architecture and compare its performance with existing solutions, including fully-digital and hybrid MIMO systems. The results demonstrate significant improvements in energy efficiency, highlighting the potential of the tri-hybrid system to meet the growing demands of future wireless networks. We conclude the paper with a summary of design and implementation challenges, including the need for technological advancements in reconfigurable array hardware and tunable antenna parameters.

Dynamic metasurface antennas (DMAs) are leaky-wave antennas with frequency-reconfigurable slots. While resonant frequency of individual slots on the DMA can be independently configured in a wide frequency range, the DMA’s beamforming performance across different operating frequencies in the available range has not been studied yet. We show that the dominant beam direction and the operating frequency of a DMA are interdependent. Using this observation, we derive a closed-form expression for line-of-sight (LOS) beamforming gain maximization to jointly configure the operating frequency and per-slot resonant frequency configuration of the DMA. A benefit of this approach in the LOS channel case is that all slots are tuned to the same resonant frequency, eliminating the need for a computationally expensive search across all feasible resonant frequency configurations. We also apply this approach to mitigate beam training overhead in large arrays by proposing a single-shot beam training algorithm that estimates the optimal resonant frequency configuration through simultaneous probing of multiple angular directions across frequencies. Simulation results show that our proposed approach offers a comparable achievable rate to true-time-delay (TTD) based systems, but with lower power consumption and hardware cost.

Dynamic metasurface antennas (DMA) provide low-power beamforming through reconfigurable radiative slots. Each slot has a tunable component that consumes low power compared to typical analog components like phase shifters. This makes DMAs a potential candidate to minimize the power consumption of multiple-input multiple-output (MIMO) antenna arrays. In this paper, we investigate the use of DMAs in a wideband communication setting with practical DMA design characteristics. We develop approximations for the DMA beamforming gain that account for the effects of waveguide attenuation, element frequency-selectivity, and limited reconfigurability of the tunable components as a function of the signal bandwidth. The approximations allow for key insights into the wideband performance of DMAs in terms of different design variables. We develop a simple successive beamforming algorithm to improve the wideband performance of DMAs by sequentially configuring each DMA element. Simulation results for a line-of-sight (LOS) wideband system show the accuracy of the approximations with the simulated DMA model in terms of spectral efficiency. We also find that the proposed successive beamforming algorithm increases the overall spectral efficiency of the DMA-based wideband system compared with a baseline DMA beamforming method.

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. Based on this formulation, we derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. The analysis shows that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. To overcome this, a shift-of-origin transformation simplifies the optimization, leading to a closed-form expression for the parasitic reactance. The solution generalizes to arrays with multiple active and parasitic elements operating in multipath channels. The proposed hybrid architecture with parasitic elements outperforms conventional architectures in terms of energy efficiency.

Multi-mode fiber (MMF) significantly increases data rates by spatially multiplexing over parallel optical channels. Analog radio-over-fiber (A-RoF) achieves lower latency and greater bandwidth as compared to digital systems but is limited by nonlinear interference in single-mode fiber (SMF). MMF reduces this interference by transmitting signals across multiple spatial modes. In this work we present a MMF aided A-RoF model for uplink MIMO systems. The linear model considers fiber impairments and noise propagation over the optical and wireless link, assuming an optimum receiver at the baseband unit. We compare the spectral efficiency of MMFaided A-RoF with wavelength division multiplexing (WDM) aided A-RoF, showing MMF reduces nonlinear interference and supports higher data rates, though it is limited by a number of optical modes and wireless channels.

A dynamic metasurface antenna (DMA) is a slottedwaveguide antenna with tunable components that allow for beamforming with low power consumption. The resonant frequencies of the individual DMA slot elements are reconfigurable, which can be leveraged for wideband communications to support larger bandwidths. The vast majority of prior work on DMAs focuses on narrowband applications for DMAs. In this paper, we develop a beamforming algorithm to improve the performance of a multicarrier DMA-based wireless system with large signal bandwidths. We build upon prior DMA signal models to incorporate different frequency-selective aspects of the physical DMA elements, and investigate the impact of different DMA designs on wideband performance. We find that the proposed beamforming algorithm outperforms a baseline beamforming algorithm in terms of spectral efficiency and data rates under significant frequencyselectivity in the multi-carrier channel and DMA elements.

Dynamic metasurface antennas (DMAs) enable beamforming through low-power components that reconfigure each radiating element. Previous studies on a single user multiple-input-single-output (MISO) system with transmit DMA focused on maximizing beamforming gain in narrowband scenarios. In this paper, we analyze the beamforming performance of DMAs in wideband scenarios. By leveraging DMA's frequency reconfigurability, our proposed approach dynamically adjusts both the transmission frequency and element configuration, achieving a higher beamforming gain than the narrowband method. We also propose the optimal waveguide refractive index and DMA element spacing to obtain an equal beamforming gain as the power hungry true-time-delay (TTD) architecture in the desired angular range. We evaluate the achievable rate performance and show that the rate approaches that of TTD when the DMA tuning range increases.

Antenna behaviors such as mutual coupling, near-field propagation, and polarization cannot be neglected in signal and channel models for wireless communication. We present an electromagnetic-based array manifold that accounts for several complicated behaviors and can model arbitrary antenna configurations. We quantize antennas into a large number of Hertzian dipoles to develop a model for the radiated array field. The resulting abstraction provides a means to predict the electric field for general non-homogeneous array geometries through a linear model that depends on the point source location, the position of each Hertzian dipole, and a set of coefficients obtained from electromagnetic simulation. We then leverage this model to formulate a beamforming gain optimization that can be adapted to account for polarization of the receive field as well as constraints on the radiated power density. Numerical results demonstrate that the proposed method achieves accuracy that is close to that of electromagnetic simulations. By leveraging the developed array manifold for beamforming, systems can achieve higher beamforming gains compared to beamforming with less accurate models.

Incorporating the sub-6 GHz band into 5G networks can improve data rates by leveraging the benefits of propagation across frequency ranges. Sequential decision making algorithms such as deep reinforcement learning (DRL) can adaptively select a band over time to take full advantage of the multi-band operation. The distinctive beam management procedure between the sub-6 GHz band and the millimeter wave (mmWave) band, though, pose a sample efficiency challenge for DRL algorithms. In this paper, we use hierarchical reinforcement learning (HRL) to divide and conquer the joint band assignment and beam management problem. The proposed HRL-based method uses rate feedback for intermittent band determination and frequent beam management mode decisions. We show with numerical evaluation that the proposed algorithm achieves a quicker increase in data rate compared to baselines and identify off-policy correction methods as a key factor for this enhancement.

Dynamic metasurface antennas (DMA) provide a solution to form compact, cost-effective, energy-efficient multiple-input-multiple output (MIMO) arrays. In this paper, we implement a practical hierarchical codebook with a realistic DMA design through electromagnetic simulations. We leverage existing DMA models to derive a novel method for enhancing the beamforming gain. We find that the proposed method provides better coverage and spectral efficiency results than prior methods. We also present and verify a new technique for creating wide beamwidths through the DMA and hierarchical codebook. Additionally, we use a detailed transmitter architecture model to determine the power consumption savings of the DMA compared to a typical phased array. The DMA largely outperforms a passive phased array in terms of spectral and energy efficiency due to high component loss from a high-resolution passive phase shifter. While the DMA provides lower spectral efficiency results than the active phased array, the DMA achieves a higher energy efficiency because of the significant power consumption for the active phase shifters. Therefore, we find that DMAs in a realistic wireless environment provide sufficient coverage and spectral efficiency compared to typical phased arrays while maintaining a substantially lower power consumption.

Reconfigurable arrays mold the propagation environment 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.

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.

Dynamic metasurface antennas (DMA) have been proposed for massive multiple-input multiple-output (MIMO) and millimeter wave applications due to their ability to create 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.

Conventional achievable rate analysis using Shannon's theory does not assume practical constraints imposed by Bode-Fano wideband matching theory. This leads to an achievable rate bound that cannot be attained by practical matching networks. In this paper, we generalize the information-theoretic achievable rate of a single-input-single-output (SISO) system by incorporating wideband matching constraints at the transmitter. 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 also propose a practical strategy to design a physically realizable matching network through the ADS software which attains the achievable rate bound with near-optimality. In simulations, we apply this framework to a Chu's antenna and compare the achievable rate performance with the conventional conjugate matching strategy.

Future wearable and portable devices with multiple transmit antennas operating below 6 GHz are constrained by regulatory limitations on the level of electromagnetic radiation a user can be exposed to, measured using the specific absorption rate (SAR). Signaling designs that are optimized to include SAR constraints can improve the performance of uplink transmission. These signaling schemes could include closed-loop beamforming, closed-loop precoding, and space-time coding, which have all been shown to achieve increased rates when optimized as a function of SAR. Previous research addressed SAR constrained optimization only within a single coherence time block. In this paper, we present transmit policies that dynamically allocate user electromagnetic radiation exposure over time. We propose three exposure allocation methods - optimal, uniform, and asymptotic - in the practical case with causal channel state information (CSI), and an on-off transmission approach for the low SAR-to-noise ratio regime. Our results demonstrate that the performance of SAR-aware transmission can be further improved by exploiting frequency and time diversity.

There has been a strong interest in understanding hybrid precoding tradeoffs for millimeter wave (mmW) multi-input multi-output (MIMO) systems. A common assumption in most of these works is that the analog part of the hybrid precoder can only be designed with phase shifters. The consequent search for analog and digital precoding matrices is solved with different black box-type optimization algorithms. In contrast, this work motivates an analog precoding structure at the base-station end that can be realized with both phase shifters and gain controls. Such a structure is necessary for interference management in multi- user transmissions and is easily realized with low complexity and cost. We then propose a feedback framework of the top-P beams over a beam alignment phase from each user. This framework allows the base-station to reconstruct the channel matrix between it and each user, and to manage interference with a simple zeroforcing solution. Such a structured solution is implemented with the amplitude and phase control of the analog part of the hybrid precoder. We finally illustrate the performance improvement with the proposed solution over simpler constructions such as beam steering that can be implemented with phase shifters alone.