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1. Robust Symbol Level Precoding for Overlay Cognitive Radio Networks

   https://doi.org/10.1109/TWC.2023.3289190  

   Liu, Lu; Masouros, Christos; Swindlehurst, A Lee (February 2024, IEEE Transactions on Wireless Communications)

   This paper focuses on designing robust symbol-level precoding (SLP) in an overlay cognitive radio (CR) network, where the primary and secondary networks transmit signals concurrently. When the primary base station (PBS) shares data and perfect channel state information (CSI) with the cognitive base station (CBS), we derive an SLP approach that minimizes the CR transmission power and satisfies symbol-wise Safety Margin (SM) constraints of both primary users (PUs) and cognitive users (CUs). The resulting optimization has a quadratic objective and linear inequality (LI) constraints, which can be solved by standard convex methods. For the case of imperfect CSI from the PBS, we propose robust SLP schemes. First, with a norm-bounded CSI error model to approximate the uncertain channels, we adopt a max-min philosophy to conservatively achieve robust SLP constraints. Second, we use the additive quantization noise model (AQNM) to describe the quantized PBS CSI and employ a stochastic constraint to formulate the problem. Both robust approaches also result in a quadratic objective with LI constraints. Simulation results show that, rather than simply trying to eliminate the network’s cross-interference, the proposed robust SLP schemes enable the primary and secondary networks to aid each other in meeting their quality of service constraints. 

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2. Massive MIMO Channel Estimation via Compressed and Quantized Feedback

   https://doi.org/10.1109/IEEECONF56349.2022.10052071  

   Shao, Mingjie; Fu, Xiao (October 2022, 2022 56th Asilomar Conference on Signals, Systems, and Computers)

   This paper focuses on downlink channel state information (CSI) acquisition. A frequency division duplex (FDD) of massive MIMO system is considered. In such systems, the base station (BS) obtains the downlink CSI from the mobile users' feedback. A key consideration is to reduce the feedback overhead while ensuring that the BS accurately recovers the downlink CSI. Existing approaches often resort to dictionary-based or tensor/matrix decomposition techniques, which either exhibit unsatisfactory accuracy or induce heavy computational load at the mobile end. To circumvent these challenges, this work formulates the limited channel feedback problem as a quantized and compressed matrix recovery problem. The formulation presents a computationally challenging maximum likelihood estimation (MLE) problem. An ADMM algorithm leveraging existing harmonic retrieval tools is proposed to effectively tackle the optimization problem. Simulations show that the proposed method attains promising channel estimation accuracy, using a much smaller amount of feedback bits relative to existing methods. 

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3. Block-Level Interference Exploitation Precoding without Symbol-by-Symbol Optimization

   https://doi.org/10.1109/WCNC55385.2023.10118875  

   Li, Ang; Shen, Chao; Liao, Xuewen; Masouros, Christos; Swindlehurst, A. Lee (March 2023, Proc. 2023 IEEE Wireless Communications and Networking Conference (WCNC))

   Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint. 

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4. Secure Intelligent Reflecting Surface-Aided Integrated Sensing and Communication

   https://doi.org/10.1109/TWC.2023.3280179  

   Hua, Meng; Wu, Qingqing; Chen, Wen; Dobre, Octavia A; Swindlehurst, A Lee (January 2024, IEEE Transactions on Wireless Communications)

   In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the potential target location of interest at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the potential target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the potential target location is uncertain in a region of interest, a robust algorithm based on the $$\cal S$$ -procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality, and also show the tremendous potential of IRS for use in sensing and improving the security of ISAC systems. 

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5. Pilot decontamination under imperfect power control

   https://doi.org/10.1109/ACSSC.2017.8335513  

   Tugnait, Jitendra K. (October 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers)

   In a time-division duplex (TDD) multiple antenna system the channel state information (CSI) can be estimated using reverse training. In multicell multiuser massive MIMO systems, pilot contamination degrades CSI estimation performance and adversely affects massive MIMO system performance. In this paper we consider a subspace-based semi-blind approach where we have training data as well as information bearing data from various users (both in-cell and neighboring cells) at the base station (BS). Existing subspace approaches assume that the interfering users from neighboring cells are always at distinctly lower power levels at the BS compared to the in-cell users. In this paper we do not make any such assumption. Unlike existing approaches, the BS estimates the channels of all users: in-cell and significant neighboring cell users, i.e., ones with comparable power levels at the BS. We exploit both subspace method using correlation as well as blind source separation using higher-order statistics. The proposed approach is illustrated via simulation examples. 

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