Accurate and efficient estimation of Channel State Information (CSI) is critical for next-generation wireless systems operating under non-stationary conditions, where user mobility, Doppler spread, and multipath dynamics rapidly alter channel statistics. Conventional pilot aided estimators incur substantial overhead, while deep learning approaches degrade under dynamic pilot patterns and time varying fading. This paper presents a pilot-aided Flash-Attention Transformer framework that unifies model-driven pilot acquisition with data driven CSI reconstruction through patch-wise self-attention and a physics aware composite loss function enforcing phase alignment, correlation consistency, and time frequency smoothness. Under a standardized 3GPP NR configuration, the proposed framework outperforms LMMSE and LSTM baselines by approximately 13 dB in phase invariant normalized mean-square error (NMSE) with markedly lower bit-error rate (BER), while reducing pilot overhead by 16 times. These results demonstrate that attention based architectures enable reliable CSI recovery and enhanced spectral efficiency without compromising link quality, addressing a fundamental bottleneck in adaptive, low-overhead channel estimation for non-stationary 5G and beyond-5G networks.

This paper proposes a novel joint channel-estimation and source-detection algorithm using successive interference cancellation (SIC)-aided generative score-based diffusion models. Prior work in this area focuses on massive MIMO scenarios, which are typically characterized by full-rank channels, and fail in low-rank channel scenarios. The proposed algorithm outperforms existing methods in joint source-channel estimation, especially in low-rank scenarios where the number of users exceeds the number of antennas at the access point (AP). The proposed score-based iterative diffusion process estimates the gradient of the prior distribution on partial channels, and recursively updates the estimated channel parts as well as the source. Extensive simulation results show that the proposed method outperforms the baseline methods in terms of normalized mean squared error (NMSE) and symbol error rate (SER) in both full-rank and low-rank channel scenarios, while having a more dominant effect in the latter, at various signal-to-noise ratios (SNR).

Currently used resource allocation methods for uplink multicarrier non-orthogonal multiple access (MC-NOMA) systems have multiple shortcomings. Current approaches either allocate the same power across all subcarriers to a user, or use heuristic-based near-far, strong channel-weak channel user grouping to assign the decoding order for successive interference cancellation (SIC). This paper proposes a novel optimal power-subcarrier allocation for uplink MC-NOMA. This new allocation achieves the optimal power-subcarrier allocation as well as the optimal SIC decoding order. Furthermore, the proposed method includes a time-sharing algorithm that dynamically alters the decoding orders of the participating users to achieve the required data rates, even in cases where any single decoding order fails to do so. Extensive experimental evaluations show that the new method achieves higher sum data rates and lower power consumption compared to current NOMA methods.

Upcoming Augmented Reality (AR) and Virtual Reality (VR) systems require high data rates ($\geq \mathbf{500 Mbps}$) and low power consumption for seamless experience. With an increasing number of subscribing users, the total number of antennas across all transmitting users far exceeds the number of antennas at the access point (AP). This results in a low rank wireless channel, presenting a bottleneck for uplink communication systems. The current uplink systems that use orthogonal multiple access (OMA) and the proposed non-orthogonal multiple access (NOMA), fail to achieve the required data rates / power consumption under predominantly low rank channel scenarios. This paper introduces an optimal power sub carrier allocation algorithm for multicarrier NOMA, named minPMAC, and an associated timesharing algorithm that adaptively changes successive interference cancellation decoding orders to maximize sum data rates in these low rank channels. This Lagrangian based optimization technique, although globally optimum, is prohibitive in terms of runtime, proving inefficient for real-time scenarios. Hence, we propose a novel near-optimal deep reinforcement learningbased energy sum optimization (DRL-minPMAC) which achieves real-time efficiency. Extensive experimental evaluations show that minPMAC achieves$\mathbf{2 8 \%}$and 39% higher data rates than NOMA and OMA baselines. Furthermore, the proposed DRL-minPMAC runs 5 times faster than minPMAC and achieves 83% of the global optimum data rates in real time.*

This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and performance of next-generation wireless networks. We delve into the design of energy-efficient passive beamforming (PBF) strategies within RIS-assisted coordinated multi-point (CoMP)-NOMA networks. Two distinct RIS configurations, namely, enhancementonly PBF (EO) and enhancement & cancellation PBF (EC), are proposed and analyzed. Our findings demonstrate that RISassisted CoMP-NOMA networks offer significant efficiency gains compared to traditional CoMP-NOMA systems. Furthermore, we formulate a PBF design problem to optimize the RIS phase shifts for maximizing energy efficiency. Our results reveal that the optimal PBF design is contingent upon several factors, including the number of cooperating base stations (BSs), the number of RIS elements deployed, and the RIS configuration. This study underscores the potential of RIS-assisted CoMP-NOMA networks as a promising solution for achieving superior energy efficiency and overall performance in future wireless networks.

This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA). Our system model incorporates a UAV-assisted RIS in coordination with a terrestrial RIS which aims for signal enhancement. We aim to maximize the sum rate for all users in the network using a custom hybrid proximal policy optimization (H-PPO) algorithm by optimizing the UAV trajectory, base station (BS) power allocation factors, active RIS amplification factor, and phase shift matrix. We integrate edge users into NOMA pairs to achieve diversity gain, further enhancing the overall experience for edge users. Exhaustive comparisons are made with passive RIS-assisted networks to demonstrate the superior efficacy of active RIS in terms of energy efficiency, outage probability, and network sum rate.

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multipoint non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks.