MIMO with 1-bit Pre/Post-Coding Resolution: A Quantum Annealing Approach

In this paper, we study the problem of digital pre/post-coding design in multiple-input multiple-output (MIMO) systems with 1-bit resolution per complex dimension. The optimal solution that maximizes the received signal-to-noise ratio relies on an NP-hard combinatorial problem that requires exhaustive searching with exponential complexity. By using the principles of alternating optimization and quantum annealing (QA), an iterative QA-based algorithm is proposed that achieves near-optimal performance with polynomial complexity. The algorithm is associated with a rigorous mathematical framework that casts the pre/post-coding vector design to appropriate real-valued quadratic unconstrained binary optimization (QUBO) problems. Experimental results in a state-of-the-art D-WAVE QA device validate the efficiency of the proposed algorithm. To further improve the efficiency of the D-WAVE quantum device, a new pre-processing technique which preserves the quadratic QUBO matrix from the detrimental effects of the Hamiltonian noise through non-linear companding, is proposed. The proposed pre-processing technique significantly improves the quality of the D-WAVE solutions as well as the occurrence probability of the optimal solution.

Wireless Information and Energy Transfer in the Era of 6G Communications

Wireless information and energy transfer (WIET) represents an emerging paradigm which employs controllable transmission of radio-frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned 6G use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective co-design of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions such as resource allocation and waveform design need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical but also practical aspects. The comprehensive overview of the state of the art of WIET presented in this paper highlights the potentials of WIET systems as well as their overall benefits in 6G networks.

A Machine Learning Approach for Optimizing Hybrid Quantum Noise Clusters for Gaussian Quantum Channel Capacity

This work contributes to the advancement of quantum communication by visualizing hybrid quantum noise in higher dimensions and optimizing the capacity of the quantum channel by using machine learning (ML). Employing the expectation maximization (EM) algorithm, the quantum channel parameters are iteratively adjusted to estimate the channel capacity, facilitating the categorization of quantum noise data in higher dimensions into a finite number of clusters. In contrast to previous investigations that represented the model in lower dimensions, our work describes the quantum noise as a Gaussian Mixture Model (GMM) with mixing weights derived from a Poisson distribution. The objective was to model the quantum noise using a finite mixture of Gaussian components while preserving the mixing coefficients from the Poisson distribution. Approximating the infinite Gaussian mixture with a finite number of components makes it feasible to visualize clusters of quantum noise data without modifying the original probability density function. By implementing the EM algorithm, the research fine-tuned the channel parameters, identified optimal clusters, improved channel capacity estimation, and offered insights into the characteristics of quantum noise within an ML framework.

Transmit and Receive Antenna Port Selection for Channel Capacity Maximization in Fluid-MIMO Systems

In this letter, we study a discrete optimization problem, namely, the maximization of channel capacity in fluid multiple-input multiple-output (fluid-MIMO) systems through the selection of antenna ports/positions at the transmitter and the receiver. First, we present a new joint convex relaxation (JCR) problem by using an upper bound on the channel capacity and exploiting the binary nature of optimization variables. Then, we develop and analyze two optimization algorithms with different performance-complexity tradeoffs: the first is based on JCR and reduced exhaustive search (JCR&RES), while the second on JCR and alternating optimization (JCR&AO). Finally, numerical results show that the proposed algorithms significantly outperform two baseline schemes, the random port selection and the conventional MIMO setup.

Optimizing Reconfigurable Antenna MIMO Systems with Coherent Ising Machines

Reconfigurable antenna multiple-input multiple-output (MIMO) is a promising technology for upcoming 6G communication systems. In this paper, we deal with the problem of configuration selection for reconfigurable antenna MIMO by leveraging Coherent Ising Machines (CIMs). By adopting the CIM as a heuristic solver for the Ising problem, the optimal antenna configuration that maximizes the received signal-to-noise ratio is investigated. A mathematical framework that converts the selection problem into a CIM-compatible unconstrained quadratic formulation is presented. Numerical studies show that the proposed CIM-based design outperforms classical counterparts and achieves near-optimal performance (similar to exponentially complex exhaustive searching) while ensuring polynomial complexity.

Integrated SWIPT Receiver with Memory Effects: Circuit Analysis and Information Detection

Wireless power transfer has been proposed as a key technology for the foreseen machine type networks. A main challenge in the research community lies in acquiring a simple yet accurate model to capture the energy harvesting performance. In this work, we focus on a half-wave rectifier and based on circuit analysis we provide the actual output of the circuit which accounts for the memory introduced by the capacitor. The provided expressions are also validated through circuit simulations on ADS. Then, the half-wave rectifier is used as an integrated simultaneous wireless information and power transfer receiver where the circuit's output is used for decoding information based on amplitude modulation. We investigate the bit error rate performance based on two detection schemes: (i) symbol-by-symbol maximum likelihood (ML); and (ii) ML sequence detection (MLSD). We show that the symbol period is critical due to the intersymbol interference induced by circuit. Our results reveal that MLSD is necessary towards improving the error probability and achieving higher data rates.

Hybrid RIS With Sub-Connected Active Partitions: Performance Analysis and Transmission Design

The emerging reflecting intelligent surface (RIS) technology promises to enhance the capacity of wireless communication systems via passive reflect beamforming. However, the product path loss limits its performance gains. Fully-connected (FC) active RIS, which integrates reflect-type power amplifiers into the RIS elements, has been recently introduced in response to this issue. Also, sub-connected (SC) active RIS and hybrid FC-active/passive RIS variants, which employ a limited number of reflect-type power amplifiers, have been proposed to provide energy savings. Nevertheless, their flexibility in balancing diverse capacity requirements and power consumption constraints is limited. In this direction, this study introduces novel hybrid RIS structures, wherein at least one reflecting sub-surface (RS) adopts the SC-active RIS design. The asymptotic signal-to-noise-ratio of the FC-active/passive and the proposed hybrid RIS variants is analyzed in a single-user single-input single-output setup. Furthermore, the transmit and RIS beamforming weights are jointly optimized in each scenario to maximize the energy efficiency of a hybrid RIS-aided multi-user multiple-input single-output downlink system subject to the power consumption constraints of the base station and the active RSs. Numerical simulation and analytic results highlight the performance gains of the proposed RIS designs over benchmarks, unveil non-trivial trade-offs, and provide valuable insights.

Superimposed Chirp Waveforms for SWIPT with Diplexer-based Integrated Receivers

In this paper, we present the superposition of chirp waveforms for simultaneous wireless information and power transfer (SWIPT) applications. Exploiting the chirp waveform characteristics enables us to superimpose multiple chirps, thereby allowing transmission of the same number of waveforms over less bandwidth. This enables us to perform subband selection when operating over set of orthogonal subbands. Furthermore, we consider a user equipped with a diplexer-based integrated receiver (DIR), which enables to extract radio frequency power and decode information from the same signal without splitting. Thereby, incorporating chirp superposition and subband selection, a transmission scheme is proposed to exploit both the diode's nonlinearity and frequency diversity. We derive novel closed-form analytical expressions of the average harvested energy (HE) via transmission of superimposed chirp over selected subbands based on tools from order statistics. We also analyze the downlink information rate achieved at the user. Through our analytical and numerical results, for the considered system setup, we show that superimposed chirp-based SWIPT provides an improvement of 30$\%$ in average HE performance as compared to multisine waveforms consisting of a set of fixed-frequency cosine signals, improves the minimum level of HE in a multiuser network, and extends the operating range of energy transfer as compared to fixed-frequency waveforms. Furthermore, we illustrate that the inclusion of DIR at the receiver for SWIPT enlarges the energy-information transfer region when compared to the widely considered power splitting receiver.

Continuous Fluid Antenna Systems: Modeling and Analysis

Fluid antennas (FAs) is a promising technology for introducing flexibility and reconfigurability in wireless networks. Recent research efforts have highlighted the potential gains that can be achieved in comparison to conventional antennas. These works assume that the FA has a discrete number of positions that the liquid can take. However, from a practical standpoint, the liquid moves in a continuous fashion to any point inside the FA. In this paper, we focus on a continuous FA system (CFAS) and present a general framework for its design and analytical evaluation. In particular, we derive closed-form analytical expressions for the level crossing rate (LCR) and the average fade duration of the continuous signal-to-interference ratio (SIR) process over the FA's length. Then, by leveraging the LCR expression, we characterize the system's outage performance with a bound on the cumulative distribution function of the SIR's supremum. Our results confirm that the CFAS outperforms its discrete counterpart and thus provides the performance limits of FA-based systems.

Robust IRS-Element Activation for Energy Efficiency Optimization in IRS-Assisted Communication Systems With Imperfect CSI

In this paper, we study an intelligent reflecting surface (IRS)-aided communication system with single-antenna transmitter and receiver, under imperfect channel state information (CSI). More specifically, we deal with the robust selection of binary (on/off) states of the IRS elements in order to maximize the worst-case energy efficiency (EE), given a bounded CSI uncertainty, while satisfying a minimum signal-to-noise ratio (SNR). In addition, we consider not only continuous but also discrete IRS phase shifts. First, we derive closed-form expressions of the worst-case SNRs, and then formulate the robust (discrete) optimization problems for each case. In the case of continuous phase shifts, we design a dynamic programming (DP) algorithm that is theoretically guaranteed to achieve the global maximum with polynomial complexity $O(L\,{\log L})$, where $L$ is the number of IRS elements. In the case of discrete phase shifts, we develop a convex-relaxation-based method (CRBM) to obtain a feasible (sub-optimal) solution in polynomial time $O(L^{3.5})$, with a posteriori performance guarantee. Furthermore, numerical simulations provide useful insights and confirm the theoretical results. In particular, the proposed algorithms are several orders of magnitude faster than the exhaustive search when $L$ is large, thus being highly scalable and suitable for practical applications. Moreover, both algorithms outperform a baseline scheme, namely, the activation of all IRS elements.