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1. Lens-Based Millimeter Wave Reconfigurable Antenna NOMA

   https://doi.org/10.1109/ICCW.2019.8756960  

   Almasi, Mojtaba Ahmadi; Amiri, Roohollah; Vaezi, Mojtaba; Mehrpouyan, Hani (May 2019, 2019 IEEE International Conference on Communications Workshops)

   This paper proposes a new multiple access technique based on the millimeter wave lens-based reconfigurable antenna systems. In particular, to support a large number of groups of users with different angles of departures (AoDs), we integrate recently proposed reconfigurable antenna multiple access (RAMA) into non-orthogonal multiple access (NOMA). The proposed technique, named reconfigurable antenna NOMA (RA-NOMA), divides the users with respect to their AoDs and channel gains. Users with different AoDs and comparable channel gains are served via RAMA while users with the same AoDs but different channel gains are served via NOMA. This technique results in the independence of the number of radio frequency chains from the number of NOMA groups. Further, we derive the feasibility conditions and show that the power allocation for RA-NOMA is a convex problem. We then derive the maximum achievable sum-rate of RA-NOMA. Simulation results show that RA-NOMA outperforms conventional orthogonal multiple access (OMA) as well as the combination of RAMA with the OMA techniques. 

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2. Scheduling Policy and Power Allocation for Federated Learning in NOMA based MEC

   https://doi.org/10.1109/GLOBECOM42002.2020.9322270  

   Ma, Xiang; Sun, Haijian; Hu, Rose Qingyang (December 2020, IEEE Globecom 2020)

   null (Ed.)

   Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications especially in wireless communications. There can be a large number of distributed edge devices connected to a central parameter server (PS) and iteratively download/upload data from/to the PS. Due to limited bandwidth, only a subset of connected devices can be scheduled in each round. There are usually millions of parameters in the state-of-art machine learning models such as deep learning, resulting in a high computation complexity as well as a high communication burden on collecting/distributing data for training. To improve communication efficiency and make the training model converge faster, we propose a new scheduling policy and power allocation scheme using non-orthogonal multiple access (NOMA) settings to maximize the weighted sum data rate under practical constraints during the entire learning process. NOMA allows multiple users to transmit on the same channel simultaneously. The user scheduling problem is transformed into a maximum-weight independent set problem that can be solved using graph theory. Simulation results show that the proposed scheduling and power allocation scheme can help achieve a higher FL testing accuracy in NOMA based wireless networks than other existing schemes within the same learning time. 

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3. Secure Communications in Tiered 5G Wireless Networks with Cooperative Jamming

   https://doi.org/10.1109/TWC.2019.2912611  

   Huo, Yan; Fan, Xin; Ma, Liran; Cheng, Xiuzhen; Tian, Zhi; Chen, Dechang (April 2019, IEEE Transactions on Wireless Communications)

   Cooperative jamming is deemed as a promising physical layer based approach to secure wireless transmissions in the presence of eavesdroppers. In this paper, we investigate cooperative jamming in a two-tier 5G heterogeneous network (HetNet), where the macro base stations (MBSs) at the macrocell tier are equipped with large-scale antenna arrays to provide space diversity and the local base stations (LBSs) at the local cell tier adopt non-orthogonal multiple access (NOMA) to accommodate dense local users. In the presence of imperfect channel state information, we propose three robust secrecy transmission algorithms that can be applied to various scenarios with different security requirements. The first algorithm employs robust beamforming (RBA) that aims to optimize the secrecy rate of a marco user (MU) in a macrocell. The second algorithm provides robust power allocation (RPA) that can optimize the secrecy rate of a local user (LU) in a local cell. The third algorithm tackles a robust joint optimization (RJO) problem across tiers that seeks the maximum secrecy sum rate of a target MU and a target LU robustly. We employ convex optimization techniques to find feasible solutions to these highly non-convex problems. Numerical results demonstrate that the proposed algorithms are highly effective in improving the secrecy performance of a two-tier HetNet. 

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4. Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning

   https://doi.org/10.1109/TWC.2023.3289929  

   Lin, Yu-Chien; Lee, Ta-Sung; Ding, Zhi (May 2023, IEEE Transactions on Wireless Communications)

   Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed learning-based DL CSI estimation that lowers uplink feedback overhead. One often overlooked problem is the limited number of DL pilots available for CSI estimation. One proposed solution leverages temporal CSI coherence by utilizing past CSI estimates and only sending CSI-reference symbols (CSIRS) for partial arrays to preserve CSI recovery performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to base stations with direct access to uplink CSI. In this work, we propose a new learning-based feedback architecture and a reconfigurable CSI-RS placement scheme to reduce DL CSI training overhead and to improve encoding efficiency of CSI feedback. Our results demonstrate superior performance in both indoor and outdoor scenarios by the proposed framework for CSI recovery at substantial reduction of computation power and storage requirements at UEs. 

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5. AI-Based Beam Management for FR3 FDD MIMO via Online Channel Synthesis

   https://doi.org/10.1109/JSAC.2026.3656162  

   Moon, Hyung-Joo; Park, Jeonghun; Chae, Chan-Byoung; Heath, Robert W (January 2026, IEEE Journal on Selected Areas in Communications)

   Efficient channel estimation and beamforming in the upper mid-band spectrum pose a fundamental challenge for 6G base stations (BSs). This requires practical beam management techniques that account for the propagation characteristics of this frequency range and the increasing number of BS antennas. To address the channel estimation bottleneck in Frequency Division Duplex (FDD) systems, this paper proposes an integrated framework combining channel synthesis and AI-based beam management. We first introduce partial statistical reciprocity (PSR) by extending the concept of partial reciprocity into the statistical domain, enabling a novel model-based online channel synthesis method. The proposed approach uses uplink channel parameters collected during BS operation to generate synthetic downlink channels that reflect the spatial distribution of active user equipment (UE), enabling the creation of high-quality, site-specific datasets for AI training. Furthermore, we propose a joint optimization framework for scalable codebook design and downlink channel estimation. A U-Net Transformer model is trained to estimate full beam-domain channel state information (CSI) using a single snapshot of uplink CSI and feedback from codebook-based beam sweeping. The model iteratively updates the codebook via backpropagation to align with current UE distributions and channel conditions, while channel estimation is performed through forward inference. The proposed AI-based framework achieves accurate channel reconstruction with minimal feedback, offering a scalable and practical solution for 6G spatial multiple access in the upper mid-band FDD system. 

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