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1. DOA Estimation-Oriented Joint Array Partitioning and Beamforming Designs for ISAC Systems

   https://doi.org/10.1109/TWC.2024.3516037  

   Liu, Rang; Li, Ming; Liu, Qian; Swindlehurst, A Lee (March 2025, IEEE Transactions on Wireless Communications)

   Integrated sensing and communication has been identified as an enabling technology for forthcoming wireless networks. In an effort to achieve an improved performance trade-off between multiuser communications and radar sensing, this paper considers a dynamically-partitioned antenna array architecture for monostatic ISAC systems, in which each element of the array at the base station can function as either a transmit or receive antenna. To fully exploit the available spatial degrees of freedom for both communication and sensing functions, we jointly design the partitioning of the array between transmit and receive antennas together with the transmit beamforming in order to minimize the direction-of-arrival (DOA) estimation error, while satisfying constraints on the communication signal-to-interference-plusnoise ratio and the transmit power budget. An alternating algorithm based on Dinkelbach’s transform, the alternative direction method of multipliers, and majorization-minimization is developed to solve the resulting complicated optimization problem. To reduce the computational complexity, we also present a heuristic three-step strategy that optimizes the transmit beamforming after determining the antenna partitioning. Simulation results confirm the effectiveness of the proposed algorithms in significantly reducing the DOA estimation error. 

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2. Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

   https://doi.org/10.3390/electronics12163505  

   Jenkinson, George; Abbasi, Muhammad_Ali Babar; Molaei, Amir Masoud; Yurduseven, Okan; Fusco, Vincent (August 2023, Electronics)

   This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot. 

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3. Channel Estimation With Reconfigurable Intelligent Surfaces--A General Framework

   https://doi.org/10.1109/JPROC.2022.3170358  

   Swindlehurst, A. Lee; Zhou, Gui; Liu, Rang; Pan, Cunhua; Li, Ming (May 2022, Proceedings of the IEEE)

   Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds. 

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4. Line-of-sight and non-line-of-sight links for dispersive terahertz wireless networks

   https://doi.org/10.1063/5.0039262  

   Ghasempour, Yasaman; Amarasinghe, Yasith; Yeh, Chia-Yi; Knightly, Edward; Mittleman, Daniel_M (April 2021, APL Photonics)

   Despite the rapidly growing interest in exploiting millimeter and terahertz waves for wireless data transfer, the role of reflected non-line-of-sight (NLOS) paths in wireless networking is one of the least explored questions. In this paper, we investigate the idea of harnessing these specular NLOS paths for communication in directional networks at frequencies above 100 GHz. We explore several illustrative transmitter architectures, namely, a conventional substrate-lens dipole antenna and a leaky-wave antenna. We investigate how these high-gain directional antennas offer both new challenges and new opportunities for exploiting NLOS paths. Our results demonstrate the sensitivity to antenna alignment, power spectrum variations, and the disparity in supported bandwidth of various line-of-sight (LOS) and reflected path configurations. We show that NLOS paths can, under certain circumstances, offer even higher data rates than the conventional LOS path. This result illustrates the unique opportunities that distinguish THz wireless systems from those that operate at lower frequencies. 

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5. BEACHES: Beamspace Channel Estimation for Multi-Antenna mmWave Systems and Beyond

   https://doi.org/10.1109/SPAWC.2019.8815576  

   Ghods, Ramina; Gallyas-Sanhueza, Alexandra; Mirfarshbafan, Seyed Hadi; Studer, Christoph (July 2019, IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   Massive multi-antenna millimeter wave (mmWave) and terahertz wireless systems promise high-bandwidth communication to multiple user equipments in the same time-frequency resource. The high path loss of wave propagation at such frequencies and the fine-grained nature of beamforming with massive antenna arrays necessitates accurate channel estimation to fully exploit the advantages of such systems. In this paper, we propose BEAmspace CHannel EStimation (BEACHES), a low-complexity channel estimation algorithm for multi-antenna mmWave systems and beyond. BEACHES leverages the fact that wave propagation at high frequencies is directional, which enables us to denoise the (approximately) sparse channel state information in the beamspace domain. To avoid tedious parameter selection, BEACHES includes a computationally-efficient tuning stage that provably minimizes the mean-square error of the channel estimate in the large-antenna limit. To demonstrate the efficacy of BEACHES, we provide simulation results for line-of-sight (LoS) and non-LoS mmWave channel models. 

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