Optimal Power Allocation and Time Sharing in Low Rank Multi-carrier Wi-Fi Channels

The ever-evolving landscape of distributed wireless systems, e.g. multi-user AR/VR systems, demands high data rates (up to 500 Mbps per user) and low power consumption. With increasing number of participating users, uplink data transmission in the situation where the number of transmitter user antennas exceeds the number of access point (AP) antennas presents a low-rank channel problem. Current Wi-Fi standards using orthogonal multiple access (OMA) fail to address these requirements. Non-orthogonal multiple access (NOMA)-based systems, while outperforming the OMA methods, still fall short of the requirement in low-rank channel uplink transmission, because they adhere to a single decoding order for successive interference cancelation (SIC). This paper proposes and develops a novel optimal power-subcarrier allocation algorithm to maximize the achieved data rates for this low rank channel scenario. Additionally, the proposed algorithm implements a novel time-sharing algorithm for simultaneously participating users, which adaptively varies the decoding orders to achieve higher data rates than any single decoding order. Extensive experimental validations demonstrate that the proposed algorithm achieves 39%, 28%, and 16% higher sum data rates than OMA, NOMA, and multi-carrier NOMA baselines respectively, under low-rank channel conditions, under varying SNR values. We further show that the proposed algorithm significantly outperforms the baselines with varying numbers of users or AP antennas, showing the effectiveness of the optimal power allocation and time-sharing.

Scaling 6G Subscribers with Fewer BS Antennas using Multi-carrier NOMA in Fixed Wireless Access

This paper introduces a novel power allocation and subcarrier optimization algorithm tailored for fixed wireless access (FWA) networks operating under low-rank channel conditions, where the number of subscriber antennas far exceeds those at the base station (BS). As FWA networks grow to support more users, traditional approaches like orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) struggle to maintain high data rates and energy efficiency due to the limited degrees of freedom in low-rank scenarios. Our proposed solution addresses this by combining optimal power-subcarrier allocation with an adaptive time-sharing algorithm that dynamically adjusts decoding orders to optimize performance across multiple users. The algorithm leverages a generalized decision feedback equalizer (GDFE) approach to effectively manage inter-symbol interference and crosstalk, leading to superior data rates and energy savings. Simulation results demonstrate that our approach significantly outperforms existing OMA and NOMA baselines, particularly in low-rank conditions, with substantial gains in both data rate and energy efficiency. The findings highlight the potential of this method to meet the growing demand for scalable, high-performance FWA networks.

Learned Trimmed-Ridge Regression for Channel Estimation in Millimeter-Wave Massive MIMO

Channel estimation poses significant challenges in millimeter-wave massive multiple-input multiple-output systems, especially when the base station has fewer radio-frequency chains than antennas. To address this challenge, one promising solution exploits the beamspace channel sparsity to reconstruct full-dimensional channels from incomplete measurements. This paper presents a model-based deep learning method to reconstruct sparse, as well as approximately sparse, vectors fast and accurately. To implement this method, we propose a trimmed-ridge regression that transforms the sparse-reconstruction problem into a least-squares problem regularized by a nonconvex penalty term, and then derive an iterative solution. We then unfold the iterations into a deep network that can be implemented in online applications to realize real-time computations. To this end, an unfolded trimmed-ridge regression model is constructed using a structural configuration to reduce computational complexity and a model ensemble strategy to improve accuracy. Compared with other state-of-the-art deep learning models, the proposed learning scheme achieves better accuracy and supports higher downlink sum rates.

Scalable Polar Code Construction for Successive Cancellation List Decoding: A Graph Neural Network-Based Approach

While constructing polar codes for successive-cancellation decoding can be implemented efficiently by sorting the bit-channels, finding optimal polar-code constructions for the successive-cancellation list (SCL) decoding in an efficient and scalable manner still awaits investigation. This paper proposes a graph neural network (GNN)-based reinforcement learning algorithm, named the iterative message-passing (IMP) algorithm, to solve the polar-code construction problem for SCL decoding. The algorithm operates only on the local structure of the graph induced by polar-code's generator matrix. The size of the IMP model is independent of the blocklength and the code rate, making it scalable to construct polar codes with long blocklengths. Moreover, a single trained IMP model can be directly applied to a wide range of target blocklengths, code rates, and channel conditions, and corresponding polar codes can be generated without separate training. Numerical experiments show that the IMP algorithm finds polar-code constructions that significantly outperform the classical constructions under cyclic-redundancy-check-aided SCL (CA-SCL) decoding. Compared to other learning-based construction methods tailored to SCL/CA-SCL decoding, the IMP algorithm constructs polar codes with comparable or lower frame error rates, while reducing the training complexity significantly by eliminating the need of separate training at each target blocklength, code rate, and channel condition.