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1. A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

   Qing An; Santiago Segarra; Chris Dick; Ashutosh Sabharwal; Rahman Doost-Mohammady (September 2023, IEEE transactions on machine learning in communications and networking)

   The large number of antennas in massive MIMO systems allows the base station to communicate with multiple users at the same time and frequency resource with multi-user beamforming. However, highly correlated user channels could drastically impede the spectral efficiency that multi-user beamforming can achieve. As such, it is critical for the base station to schedule a suitable group of users in each time and frequency resource block to achieve maximum spectral efficiency while adhering to fairness constraints among the users. In this paper, we consider the resource scheduling problem for massive MIMO systems with its optimal solution known to be NP-hard. Inspired by recent achievements in deep reinforcement learning (DRL) to solve problems with large action sets, we propose SMART, a dynamic scheduler for massive MIMO based on the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest Neighbors (KNN) algorithm. Through comprehensive simulations using realistic massive MIMO channel models as well as real-world datasets from channel measurement experiments, we demonstrate the effectiveness of our proposed model in various channel conditions. Our results show that our proposed model performs very close to the optimal proportionally fair (Opt-PF) scheduler in terms of spectral efficiency and fairness with more than one order of magnitude lower computational complexity in medium network sizes where Opt-PF is computationally feasible. Our results also show the feasibility and high performance of our proposed scheduler in networks with a large number of users and resource blocks. 

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2. Energy efficient downlink massive MIMO: Is 1-bit quantization a solution ?

   https://doi.org/10.1109/ISWCS.2019.8877256  

   Marcastel, Alexandre; Fijalkow, Inbar; Swindlehurst, Lee (August 2019, Proc. 16th International Symposium on Wireless Communication Systems (ISWCS))

   Massive MIMO aims to build wireless base stations with hundreds of coherently operating antennas serving tens of single antenna users in order to improve both the transmission capacity by a factor 10-50 and the energy-efficiency trade-off by up to a thousand times. Pre-coding at the base station has been proposed to efficiently implement digital beamforming. It implies a high signal dynamic range and therefore a power backoff resulting in less energy-efficiency. One-bit quantized Zero-Forcing precoding has been proposed to efficiently handle the RF front-end when the array is implemented with so many antennas. In this paper, we analyze the energy-efficiency of the quantized Zero-Forcing precoded systems for a large number of users and a massive MIMO base station. 

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3. Design Trade-offs for Decentralized Baseband Processing in Massive MU-MIMO Systems

   https://doi.org/10.1109/IEEECONF44664.2019.9048727  

   Li, Kaipeng; McNaney, James; Tarver, Chance; Castaneda, Oscar; Jeon, Charles; Cavallaro, Joseph R.; Studer, Christoph (November 2019, 2019 IEEE 53rd Asilomar Conference on Signals, Systems and Computers)

   null (Ed.)

   Massive multi-user (MU) multiple-input multiple-output (MIMO) provides high spectral efficiency by means of spatial multiplexing and fine-grained beamforming. However, conventional base-station (BS) architectures for systems with hundreds of antennas that rely on centralized baseband processing inevitably suffer from (i) excessive interconnect data rates between radio-frequency circuitry and processing fabrics, and (ii) prohibitive complexity at the centralized baseband processor. Recently, decentralized baseband processing (DBP) architectures and algorithms have been proposed, which mitigate the interconnect bandwidth and complexity bottlenecks. This paper systematically explores the design trade-offs between error-rate performance, computational complexity, and data transfer latency of DBP architectures under different system configurations and channel conditions. Considering architecture, algorithm, and numerical precision aspects, we provide practical guidelines to select the DBP architecture and algorithm that are able to realize the full benefits of massive MU-MIMO in the uplink and downlink. 

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4. Spectral Clustering Aided User Grouping and Scheduling in Wideband MU-MIMO Systems

   Hsu, Chih-Ho; Feres, Carlos; Ding, Zhi (May 2023, IEEE International Conference on Communications)

   Multiuser MIMO (MU-MIMO) technologies can help provide rapidly growing needs for high data rates in modern wireless networks. Co-channel interference (CCI) among users in the same resource-sharing group (RSG) presents a serious user scheduling challenge to achieve high overall MU-MIMO capacity. Since CCI is closely related to correlation among spatial user channels, it would be natural to schedule co-channel user groups with low inter-user channel correlation. Yet, establishing RSGs with low co-channel correlations for large user populations is an NP-hard problem. More practically, user scheduling for wideband channels exhibiting distinct channel characteristics in each frequency band remains an open question. In this work, we proposed a novel wideband user grouping and scheduling algorithm named SC-MS. The proposed SC-MS algorithm first leverages spectral clustering to obtain a preliminary set of user groups. Next, we apply a post-processing step to identify user cliques from the preliminary groups to further mitigate CCI. Our last step groups users into RSGs for scheduling such that the sum of user clique sizes across the multiple frequency bands is maximized. Simulation results demonstrate network performance gain over benchmark methods in terms of sum rate and fairness. 

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5. Quantum Annealing for Large MIMO Downlink Vector Perturbation Precoding

   https://doi.org/10.1109/ICC42927.2021.9500557  

   Kasi, Srikar; Singh, Abhishek Kumar; Venturelli, Davide; Jamieson, Kyle (June 2021, IEEE International Conference on Communications)

   null (Ed.)

   In a multi-user system with multiple antennas at the base station, precoding techniques in the downlink broadcast channel allow users to detect their respective data in a non-cooperative manner. Vector Perturbation Precoding (VPP) is a non-linear variant of transmit-side channel inversion that perturbs user data to achieve full diversity order. While promising, finding an optimal perturbation in VPP is known to be an NP-hard problem, demanding heavy computational support at the base station and limiting the feasibility of the approach to small MIMO systems. This work proposes a radically different processing architecture for the downlink VPP problem, one based on Quantum Annealing (QA), to enable the applicability of VPP to large MIMO systems. Our design reduces VPP to a quadratic polynomial form amenable to QA, then refines the problem coefficients to mitigate the adverse effects of QA hardware noise. We evaluate our proposed QA based VPP (QAVP) technique on a real Quantum Annealing device over a variety of design and machine parameter settings. With existing hardware, QAVP can achieve a BER of 10 −4 with 100µs compute time, for a 6 × 6 MIMO system using 64 QAM modulation at 32 dB SNR. 

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