Emerging millimeter-wave (mmWave) MIMO radars combine the benefits of large bandwidth available at mmWave frequencies with the spatial diversity provided by MIMO architectures, significantly enhancing radar capabilities for automotive, surveillance, and imaging applications. However, deploying large numbers of antennas and transceivers at these high frequencies substantially increases chip complexity and hardware costs. In this paper, we address the design of sparse two-dimensional (2D) antenna arrays that retain the desirable beampattern characteristics of fully populated arrays – namely, narrow mainlobes and low sidelobes – while significantly reducing the required number of antenna elements. We formulate the sparse array design problem as a beampattern matching optimization, which selects optimal subsets of transmit and receive antenna positions from an initial dense grid. To efficiently solve this challenging nonconvex optimization problem, we introduce an iterative algorithm combining Majorization–Minimization (MM) and Alternating Optimization (AO) techniques. We provide theoretical guarantees for convergence to at least a local optimum. Additionally, we propose a weighting vector optimization step to further enhance sidelobe suppression. Numerical simulations confirm that the proposed method maintains angular resolution and Sidelobe Levels (SLLs) comparable to those of full arrays, while substantially reducing hardware complexity and cost. Performance comparisons against existing methods demonstrate notable improvements in sidelobe suppression and computational efficiency without compromising processing gain. 

Reconfigurable intelligent surfaces (RISs) have emerged as a spectrum- and energy-efficient technology to enhance the coverage of wireless communications within the upcoming 6G networks. Recently, novel extensions of this technology, referred to as multi-sector beyond diagonal RIS (BD-RIS), have been proposed, where the configurable elements are divided into L sectors (L ≥ 2) and arranged as a polygon prism, with each sector covering 1/L space. This paper presents a performance analysis of a communication system that is assisted by a multi-sector BD-RIS operating in time-switching (TS) mode. Specifically, we derive closed-form expressions for the moment-generating function (MGF), probability density function (PDF), and cumulative density function (CDF) of the signal-to-noise ratio (SNR) per user. Furthermore, exact closed-form expressions for the outage probability, achievable spectral and energy efficiency, symbol error probability, and diversity order for the proposed system model are derived. To evaluate the performance of multi-sector BD-RISs, we compare them with the simultaneously transmitting and reflecting (STAR)-RISs, which can be viewed as a special case of multi-sector BD-RIS with only two sectors. Interestingly, our analysis reveals that, for a fixed number of configurable elements, increasing the number of sectors improves outage performance while reducing the diversity order compared to the STAR-RIS configuration. This trade-off is influenced by the Rician factors of the cascaded channel and the number of configurable elements per sector. However, this superiority in slope is observed at outage probability values below 10-5, which remains below practical operating ranges of communication systems. Additionally, simulation results are provided to validate the accuracy of our theoretical analyses. These results indicate that increasing the number of sectors in multi-sector BD-RIS-assisted systems significantly enhances performance, particularly in both spectral and energy efficiency gains. For instance, our numerical results show an average increase of 184% in spectral efficiency and 128% in maximum energy efficiency when transitioning from a 2-sector to a 6-sector configuration.

A cognitive fully adaptive radar (CoFAR) adapts its behavior on its own within a short period of time in response to changes in the target environment. For the CoFAR to function properly, it is critical to understand its operating environment through estimation of the clutter channel impulse response (CCIR). In general, CCIR is sparse but prior works either ignore it or estimate the CCIR by imposing sparsity as an explicit constraint in their optimization problem. In this article, contrary to these studies, we develop covariance-free Bayesian learning (CoFBL) techniques for estimating sparse CCIR in a CoFAR system. In particular, we consider a multiple measurement vector scenario and estimate a simultaneously sparse (row sparse) CCIR matrix. Our CoFBL framework reduces the complexity of conventional sparse Bayesian learning through the use of the diagonal element estimation rule and conjugate gradient descent algorithm. We show that the framework is applicable to various forms of CCIR sparsity models: group, joint, and joint-cum-group. We evaluate our method through numerical experiments on a dataset generated using RFView, a high-fidelity modeling and simulation tool. We derive Bayesian Cramér–Rao bounds for the various considered scenarios to benchmark the performance of our algorithms. Our results demonstrate that the proposed CoFBL-based approaches perform better than the existing popular approaches, such as multiple focal underdetermined system solver and simultaneous orthogonal matching pursuit.

To improve the transmission performance of low Earth orbit (LEO) satellites limited by practical constraints of transmit power, antenna array size, and antenna gain in single satellites, multiple LEO satellites can be leveraged to cooperatively serve terrestrial user terminals (UTs). This paper investigates cooperative downlink (DL) transmission from multiple LEO satellites by using distributed beamforming, considering the inevitable delay and Doppler compensation errors that impact coherent processing. Firstly, we establish the DL transmission signal model for multiple LEO satellites with delay and Doppler compensation errors. On this basis, we design the transmitters and receivers to maximize the average signal-to-leakage-plus-noise ratio. Then, we analyze the DL transmission performance via deriving lower bounds and closed-form expressions for both the user rate and the average rate gain of cooperative transmission compared to single LEO satellite transmission. We prove that as the number of receiving antennas at the UT increases, the impact of imperfect compensation on the user rate decreases, and the average rate gain improves. In addition, we prove that the UT can achieve the optimal average rate gain when its array response vectors corresponding to different LEO satellites are orthogonal. Simulations are performed and compared to the theoretical analysis, demonstrating the performance gains brought by distributed beamforming and validating our analysis.

In this work, we consider a typical scenario in a harsh urban propagation environment which is typical for a smart city scenario where multiple reconfigurable intelligent surfaces (RISs) are deployed in different hotspot areas to overcome signal blockage between the base station and users. Our goal is to ensure uninterrupted service availability to users in different hotspot areas regardless of their location. Consistent service availability can be achieved by guaranteeing that each RIS deployed in a hotspot area can support a certain number of users. This plays a critical role in smart city applications in the context of emergency communications and ubiquitous connectivity since the design ensures service availability to as many users as possible in all relevant locations. Taking into consideration the challenges in obtaining channel state information (CSI) given the passive nature of RIS and dynamic environments, we formulate a robust fairness problem to maximize the minimum expected number of served users in proximity to each RIS while considering the available transmit power and the worst-case quality of service (QoS) constraints within the bounded CSI error model framework. The resulting problem is a mixed integer non-convex program which is highly coupled and challenging to solve in polynomial time. Thus, we resort to binary variable relaxation, convex approximation techniques, and alternating optimization to tackle the problem. Additionally, we handle the semi-infinite uncertainty constraints by employing the S-procedure and general sign-definiteness. Simulation results demonstrate the effectiveness of the proposed design in obtaining consistent and reliable service in different hotspot areas compared to the relevant benchmark schemes. In addition, the proposed design shows flexibility in serving users with their target QoS given different channel uncertainty levels.

This paper investigates joint device identification, channel estimation, and symbol detection for LEO satellite-enabled grant-free random access systems, specifically targeting scenarios where remote Internet-of-Things (IoT) devices operate without global navigation satellite system (GNSS) assistance. Considering the constrained power consumption of these devices, the large differential delay and Doppler shift are handled at the satellite receiver. We firstly propose a spreading-based multi-frame transmission scheme with orthogonal time-frequency space (OTFS) modulation to mitigate the doubly dispersive effect in time and frequency, and then analyze the input-output relationship of the system. Next, we propose a receiver structure based on three modules: a linear module for identifying active devices that leverages the generalized approximate message passing algorithm to eliminate inter-user and inter-carrier interference; a non-linear module that employs the message passing algorithm to jointly estimate the channel and detect the transmitted symbols; and a third module that aims to exploit the three dimensional block channel sparsity in the delay-Doppler-angle domain. Soft information is exchanged among the three modules by careful message scheduling. Furthermore, the expectation-maximization algorithm is integrated to adjust phase rotation caused by the fractional Doppler and to learn the hyperparameters in the priors. Finally, the convolutional neural network is incorporated to enhance the symbol detection. Simulation results demonstrate that the proposed transmission scheme boosts the system performance, and the designed algorithms outperform the conventional methods significantly in terms of the device identification, channel estimation, and symbol detection.

This paper investigates the spatio-temporal predictive learning problem, which is crucial in diverse applications such as MIMO channel prediction, mobile traffic analysis, and network slicing. To address this problem, the attention mechanism has been adopted by many existing models to predict future outputs. However, most of these models use a single-domain attention which captures input dependency structures only in the temporal domain. This limitation reduces their prediction accuracy in spatio-temporal predictive learning, where understanding both spatial and temporal dependencies is essential. To tackle this issue and enhance the prediction performance, we propose a novel crossover attention mechanism in this paper. The crossover attention can be understood as a learnable regression kernel which prioritizes the input sequence with both spatial and temporal similarities and extracts relevant information for generating the output of future time slots. Simulation results and ablation studies based on synthetic and realistic datasets show that the proposed crossover attention achieves considerable prediction accuracy improvement compared to the conventional attention layers.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enhance the flexibility and performance of RIS by extending traditional 180∘ half-space coverage to a full 360∘. Motivated by this, we explore a STAR-RIS-assisted overlay cognitive satellite-terrestrial network (OCSTN) with multiple users. In this setup, a primary satellite source communicates with terrestrial primary receivers (PRs) using the non-orthogonal multiple access (NOMA) scheme, while a decode-and-forward-based secondary transmitter (ST) facilitates primary communications in exchange for spectrum access. A STAR-RIS further aids the ST by simultaneously transmitting and reflecting superposed signals to enhance both primary and secondary communications. The analysis incorporates practical considerations, including imperfect successive interference cancellation (ipSIC) in the NOMA and overlay system, as well as quantization errors introduced by the discrete phase shifts of STAR-RIS elements. For terrestrial Nakagami-m fading and satellite shadowed-Rician fading, we derive exact and asymptotic outage probability expressions and ergodic rate bounds for primary and secondary networks. The numerical and simulation results demonstrate that the STAR-RIS-assisted OCSTN consistently achieves superior performance compared to standalone OCSTN benchmarks across key performance metrics.

Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency spectrum. Such a joint radar-communications (JRC) model has advantages of low-cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mm-wave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical in implementation of mmWave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade-off between communications and radar functionalities. Novel multiple-input-multiple-output (MIMO) signal processing techniques are required because mmWave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mmWave JRC systems with an emphasis on waveform design.

Synchronizing the local oscillators in multibeam satellites with the objective of coherent communications is still an open challenge. It has to be addressed to implement full-frequency reuse approaches, such as precoding techniques using the already deployed multibeam satellites. This article addresses the required phase synchronization to enable precoding techniques in multibeam satellite systems. It contains the detailed design of a frequency and phase compensation loop based on the proportional-integral controller, which deals with the phase drift introduced by the hardware components. Specifically, the phase noise of the local oscillators used for up and down conversion at each system element (gateway, satellite, and user terminals). The implementation of the two-state phase noise model used to emulate this phase drift is included in the article. Besides, a comparative analysis of several methods to combine the frequency and phase measurements obtained from the user terminals is also included. Finally, the performance of the proposed closed-loop synchronization method is validated through simulations using our in-house developed MIMO end-to-end satellite emulator based on SDR platforms.