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1. Coded Multi-User Information Retrieval with a Multi-Antenna Helper Node

   Abolpour, Milad; Salehi, MohammadJavad; Mohajer, Soheil; Shariatpanahi, Seyed Pooya; Tölli, Antti (July 2024, IEEE)

   A novel coding design is proposed to enhance information retrieval in a wireless network of users with partial access to the data, in the sense of observation, measurement, computation, or storage. Information exchange in the network is assisted by a multi-antenna base station (BS), with no direct access to the data. Accordingly, the missing parts of data are exchanged among users through an uplink (UL) step followed by a downlink (DL) step. In this paper, new coding strategies, inspired by coded caching (CC) techniques, are devised to enhance both UL and DL steps. In the UL step, users transmit encoded and properly combined parts of their accessible data to the BS. Then, during the DL step, the BS carries out the required processing on its received signals and forwards a proper combination of the resulting signal terms back to the users, enabling each user to retrieve the desired information. Using the devised coded data retrieval strategy, the data exchange in both UL and DL steps requires the same communication delay, measured by normalized delivery time (NDT). Furthermore, the NDT of the UL/DL step is shown to coincide with the optimal NDT of the original DL multi-input single-output CC scheme, in which the BS is connected to a centralized data library. 

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2. Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization

   https://doi.org/10.3390/electronics11030401  

   Hashir, Syed Muhammad; Gupta, Sabyasachi; Megson, Gavin; Aryafar, Ehsan; Camp, Joseph (February 2022, Electronics)

   In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems. 

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3. Channel Aware Greedy Algorithm for MISO Cache-Aided Communication

   https://doi.org/10.1109/SPAWC51304.2022.9834007  

   Bergel, Itsik; Mohajer, Soheil (January 2022, 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC))

   We present novel schemes for cache-aided communication over networks with a multi-antenna base station (BS) that serves multiple single-antenna users. The schemes are based on a greedy scheduling [1], which simultaneously transmits coded messages to disjoint groups of users. The proposed algorithms use the channel state information to opportunistically choose the groups to be served together and to allocate power to each coded message in order to minimize the overall communication delay. Numerical study shows that the new schemes outperform the previously known schemes. 

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4. FASURA: A Scheme for Quasi-Static Fading Unsourced Random Access Channels

   https://doi.org/10.1109/TCOMM.2023.3296593  

   Gkagkos, Michail; Narayanan, Krishna R.; Chamberland, Jean-Francois; Georghiades, Costas N. (July 2023, IEEE Transactions on Communications)

   Liva, Gianluigi (Ed.)

   Unsourced random access emerged as a novel wireless paradigm enabling massive device connectivity on the uplink. We consider quasi-static Rayleigh fading wherein the access point has multiple receive antennas and every mobile device a single transmit antenna. The objective is to construct a coding scheme that minimizes the energy-per-bit subject to a maximum probability of error given a fixed message length and a prescribed number of channel uses. Every message is partitioned into two parts: the first determines pilot values and spreading sequences; the remaining bits are encoded using a polar code. The transmitted signal contains two distinct sections. The first features pilots and the second is composed of spread modulated symbols. The receiver has three modules: an energy detector, tasked with recovering the set of active pilot sequences; a bank of Minimum Mean Square Error (MMSE) estimators acting on measurements at the receiver; and a polar list-decoder, which seeks to retrieve the coded information bits. A successive cancellation step is applied to subtract recovered codewords, before the residual signal is fed back to the decoder. Empirical evidence suggests that an appropriate combination of these ideas can outperform state-of-the-art coding techniques when the number of active users exceeds one hundred. 

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5. Distributed Graph-Based Learning for User Association and Beamforming Design in Multi-RIS Multi-Cell Networks

   https://doi.org/10.1109/TWC.2025.3551763  

   Lim, Byungju; Vu, Mai (July 2025, IEEE Transactions on Wireless Communications)

   We propose a novel graph neural network (GNN) architecture for jointly optimizing user association, base station (BS) beamforming, and reconfigurable intelligent surface (RIS) phase shift in a multi-RIS aided multi-cell network. The proposed architecture represents BSs and users as nodes in a bipartite graph where the same type of nodes shares the same neural networks for generating messages and updating its representations, allowing for distributed implementation. In addition, we utilize a composite reflected channel estimation integrated between layers of the GNN structure to significantly reduce the signaling overhead and complexity required for channel estimation in a multi-RIS network. To avoid BS overload, load balancing is regularized in the training of the GNN and we further develop a collision avoidance algorithm to ensure strict load balancing at every BS. Numerical results show that the proposed GNN architecture is significantly more efficient than existing approaches. The results further demonstrate its strong scalability with network size and achieving a throughput performance approaching that of a centralized traditional optimization algorithm, without requiring individual RIS-reflected channels estimation and without the need for re-training or fine-tuning. 

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