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1. Joint Beamforming and Reflection Design for RIS-assisted ISAC Systems

   https://doi.org/10.23919/EUSIPCO55093.2022.9909807  

   Liu, Rang; Li, Ming; Swindlehurst, A. Lee (August 2022, 022 30th European Signal Processing Conference (EUSIPCO))

   In this paper, we investigate the potential of employing reconfigurable intelligent surface (RIS) in integrated sensing and communication (ISAC) systems. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communication and target detection. We aim to jointly design the transmit beamforming and receive filter of the BS, and the reflection coefficients of the RIS to maximize the sum-rate of the communication users, while satisfying a worst-case radar output signal-to-noise ratio (SNR), the transmit power constraint, and the unit modulus property of the reflecting coefficients. An efficient iterative algorithm based on fractional programming (FP), majorization-minimization (MM), and alternative direction method of multipliers (ADMM) is developed to solve the complicated non-convex problem. Simulation results verify the advantage of the proposed RIS-assisted ISAC scheme and the effectiveness of the developed algorithm. 

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2. Minimizing Pilot Overhead in Cell-Free Massive MIMO Systems via Joint Estimation and Detection

   https://doi.org/10.1109/SPAWC48557.2020.9154310  

   Song, Haochuan; You, Xiaohu; Zhang, Chuan; Tirkkonen, Olav; Studer, Christoph (May 2020, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   We propose a joint channel estimation and data detection (JED) algorithm for cell-free massive multi-user (MU) multiple-input multiple-output (MIMO) systems. Our algorithm yields improved reliability and reduced latency while minimizing the pilot overhead of coherent uplink transmission. The proposed JED method builds upon a novel non-convex optimization problem that we solve approximately and efficiently using forward- backward splitting. We use simulation results to demonstrate that our algorithm achieves robust data transmission with more than 3x reduced pilot overhead compared to orthogonal training in a 128 antenna cell-free massive MU-MIMO system in which 128 users transmit data over 128 time slots. 

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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. Mitigating Smart Jammers in Multi-User MIMO

   https://doi.org/10.1109/TSP.2023.3246226  

   Marti, Gian; Kölle, Torben; Studer, Christoph (January 2023, IEEE Transactions on Signal Processing)

   Wireless systems must be resilient to jamming attacks. Existing mitigation methods based on multi-antenna processing require knowledge of the jammer's transmit characteristics that may be difficult to acquire, especially for smart jammers that evade mitigation by transmitting only at specific instants. We propose a novel method to mitigate smart jamming attacks on the massive multi-user multiple-input multiple-output (MU-MIMO) uplink which does not require the jammer to be active at any specific instant. By formulating an optimization problem that unifies jammer estimation and mitigation, channel estimation, and data detection, we exploit that a jammer cannot change its subspace within a coherence interval. Theoretical results for our problem formulation show that its solution is guaranteed to recover the users' data symbols under certain conditions. We develop two efficient iterative algorithms for approximately solving the proposed problem formulation: MAED, a parameter-free algorithm which uses forward-backward splitting with a box symbol prior, and SO-MAED, which replaces the prior of MAED with soft-output symbol estimates that exploit the discrete transmit constellation and which uses deep unfolding to optimize algorithm parameters. We use simulations to demonstrate that the proposed algorithms effectively mitigate a wide range of smart jammers without a priori knowledge about the attack type. 

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5. Beam Alignment for the Cell-Free mmWave Massive MU-MIMO Uplink

   https://doi.org/10.1109/SiPS55645.2022.9919259  

   Brun, Jannik; Palhares, Victoria; Marti, Gian; Studer, Christoph (November 2022, IEEE Workshop on Signal Processing Systems (SiPS))

   Millimeter-wave (mmWave) cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems combine the large bandwidths available at mmWave frequencies with the improved coverage of cell-free systems. However, to combat the high path loss at mmWave frequencies, user equipments (UEs) must form beams in meaningful directions, i.e., to a nearby access point (AP). At the same time, multiple UEs should avoid transmitting to the same AP to reduce MU interference. We propose an interference-aware method for beam alignment (BA) in the cell-free mmWave massive MU-MIMO uplink. In the considered scenario, the APs perform full digital receive beamforming while the UEs perform analog transmit beamforming. We evaluate our method using realistic mmWave channels from a commercial ray-tracer, showing the superiority of the proposed method over omnidirectional transmission as well as over methods that do not take MU interference into account. 

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