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1. MIMO-OFDM ISAC Waveform Design for Range-Doppler Sidelobe Suppression

   https://doi.org/10.1109/TWC.2024.3503605  

   Li, Peishi; Li, Ming; Liu, Rang; Liu, Qian; Lee_Swindlehurst, A (February 2025, IEEE Transactions on Wireless Communications)

   Integrated sensing and communication (ISAC) is a key enabling technique for future wireless networks owing to its efficient hardware and spectrum utilization. In this paper, we focus on dual-functional waveform design for a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system, which is considered to be a promising solution for practical deployment. Since the dual-functional waveform carries communication information, its random nature leads to high range-Doppler sidelobes in the ambiguity function, which in turn degrades radar sensing performance. To suppress range- Doppler sidelobes, we propose a novel symbol-level precoding (SLP)-based waveform design for MIMO-OFDM ISAC systems by fully exploiting the available temporal degrees of freedom. Our goal is to minimize the range-Doppler integrated sidelobe level (ISL) while satisfying the constraints of target illumination power, multi-user communication quality of service (QoS), and constant-modulus transmission. To solve the resulting non-convex waveform design problem, we develop an efficient algorithm using the majorization-minimization (MM) and alternative direction method of multipliers (ADMM) methods. Simulation results show that the proposed waveform has significantly reduced range-Doppler sidelobes compared with signals designed only for communications and other baselines. In addition, the proposed waveform design achieves target detection and estimation performance close to that achievable by waveforms designed only for radar, which demonstrates the superiority of the proposed SLP-based ISAC approach. 

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2. Cell-Free Massive MIMO-Aided ISAC

   https://doi.org/10.1109/ICC52391.2025.11160955  

   Kulathunga, Ranga; Dassanayake, Janith Kavindu; ArumaBaduge, Gayan Amarasuriya (June 2025, IEEE)

   The performance of cell-free massive multiple-input multiple-output (MIMO)-aided integrated sensing and communication (ISAC) is investigated. Each transmit access point (AP) sends a superimposed ISAC waveform from which the users are able to decode data, while the reflected echos off a target are used at the receive APs to perform sensing functionalities. Each transmit AP adopts a local conjugate precoder, which is designed based on the locally acquired channel state information (CSI) via user pilots. This approach reduces the implementation complexity as it does not necessitate CSI exchanges. An efficient transmit power optimization is also proposed to construct the superimposed ISAC waveform. The performance is evaluated by deriving the achievable user rates and quantifying the two-dimensional MUltiple SIgnal Classification (MUSIC) spectrum function at the receive APs. Our performance analysis captures practical impairments, including erroneously estimated CSI, spatially correlated Rician fading, and clutter interference. Our analytical and numerical results demonstrate the potential of our proposed cell-free massive MIMO aided ISAC systems. 

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3. Low Range-Doppler Sidelobe ISAC Waveform Design: A Low-Complexity Approach

   https://doi.org/10.1109/TVT.2025.3570381  

   Li, Peishi; Li, Ming; Liu, Rang; Liu, Qian; Swindlehurst, A Lee (October 2025, IEEE Transactions on Vehicular Technology)

   Integrated sensing and communication (ISAC) is a pivotal enabler for next-generation wireless networks. A key challenge in ISAC systems lies in designing dual-functional waveforms that can achieve satisfactory radar sensing accuracy by effectively suppressing range-Doppler sidelobes. However, existing solutions are often computationally intensive, limiting their practicality in multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC deployments. This paper presents a novel low-complexity algorithm leveraging the augmented Lagrangian method (ALM) and Riemannian conjugate gradient (RCG) optimization techniques to address these challenges. The proposed algorithm achieves superior sidelobe suppression compared to state-of-the-art methods while dramatically reducing computational complexity, making it highly suitable for real-world MIMO-OFDM ISAC systems. Simulation results demonstrate that the proposed approach not only outperforms existing benchmarks in sidelobe reduction but also accelerates convergence, ensuring efficient performance across communication and sensing tasks. 

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4. 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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5. 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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