Both intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) are the cutting-edge technologies for future wireless communications. In this work, we examine the effectiveness of IRS in NOMA system with respect to transmit power consumption. Towards this end, the power minimization problem for IRS-aided downlink NOMA system is formulated, taking into consideration the constraint of each user’s minimum signal-to-interference-ratio (SINR). To solve the joint power control and IRS phase shifts optimization problem, we first explore the relationship between individual user’s transmit power and the variables of phase shifts. Then, we solve the phase shift determination problem via the sequential rotation algorithm. Numerical results show that our designed joint optimization algorithm can significantly reduce the required transmit power when compared with various benchmarking schemes.

To accommodate the stringent requirements of massive connectivity and ultra-high throughput, both reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) have been perceived as the key techniques for future communication networks. In this paper, a versatile framework is conceived to boost transmit power efficiency for RIS-enabled multi-group NOMA networks in the presence of coordinated multi-point (CoMP) reception and imperfect successive interference cancellation (SIC). Particularly, a group-level SIC (GSIC) method is proposed to eliminate the decoded group’s interference together with the well-designed transceivers to mitigate the aggregated interference, including the intra-group interference, the residual interference caused by imperfect SIC, and the interference of NOMA users decoded later. According to the novel framework, a power minimization problem is formulated by collaboratively optimizing the transmit power and the phase shifts. To render the problem tractable, an alternating scheme is developed to optimize the transmit power and the phase shifts iteratively. Specifically, the transmit powers for the users in the same group are devised by a parallel iteration algorithm, whilst the phase shifts are optimized by a sequential rotation method. In simulations, it is shown that the proposed scheme requires less transmit power than various benchmark methods under the constraint of each user’s quality of service.

Reconfigurable intelligent surface (RIS) is an attractive solution to smart radio environment, which has a promising potential to mitigate Doppler effect for mobile channels. In this letter, we present a Doppler mitigation method assisted by RIS for high-speed scenarios, including the design of transmission protocol and the phase control of RIS pattern. The transmission protocol is an enhanced version of a two-stage protocol. A two-phase training sequence is introduced in the first stage, to decouple the estimates of direct and cascaded channels and simplify the training matrix for LS estimation. Training overheads are reduced in the second stage to improve the data rate. In addition, the real-time control matrix of the RIS pattern is provided for Doppler mitigation. Numerical simulations show that the presented method can effectively mitigate Doppler shift, and improve the achievable rate to be 1.82-fold.

Multi-access edge computing (MEC), which brings computing capability close to user equipment (UE) within a radio access network (RAN), is a promising technique to meet the challenges of the explosive growth in edge data traffic and the number of connected devices. Nevertheless, MEC offloading failures, i.e., interruption of task offloading due to UE mobility and limited battery capacity, is still an important issue. In this paper, we aim to minimize the total energy consumption in dual connectivity (DC)-and non-orthogonal multiple access (NOMA)-assisted computation offloading systems. This problem is formulated as a mixed-integer non-linear programming (MINLP) problem with non-convex constraints, which is a non-deterministic polynomialtime (NP)-hard and perplexing problem. To address this problem, we propose an iterative optimization algorithm to obtain a suboptimal time scheduling and task allocation solution. Specifically, we solve the non-convex constrained MINLP problems by exploiting the successive convex approximation (SCA) algorithm and the non-linear optimization algorithm with the mesh adaptive direct search (MADS) (NOMAD). Furthermore, a deep learning-based intelligent offloading scheme is introduced, called a cyclic branch network, to fully utilize data traffic and the computing capability on the network edge. Numerical results show that the proposed intelligent offloading scheme has comparable performance and dramatically reduces the inference time compared with the traditional optimization algorithm.

Multi-access edge computing (MEC) is a promising computing paradigm that incorporates computing capability into a radio access network (RAN) nearby user equipment (UE) to improve the quality of experience. However, MEC also encounters many challenges inherent in RANs, such as mobility management. In this letter, we develop a novel computation offloading scheme utilizing non-orthogonal multiple access (NOMA) and dual connectivity (DC) and focus on jointly optimizing task segmentation and power allocation to minimize the total energy consumption. Specifically, the joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem that is non-deterministic polynomial hard (NP-hard). To solve this problem, we propose a new heuristic algorithm to obtain a sub-optimal solution. We then propose an online learning algorithm based on a twin delayed deep deterministic policy gradient (TD3) to meet user mobility requirements. The numerical results show that the proposed scheme outperforms other schemes, and the TD3-based algorithm has comparable energy consumption performance and dramatically reduces the execution time compared with the heuristic algorithm.

The deep integration between non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (MIMO) technology is a promising way to achieve massive connectivity and high capacity in B5G/6G wireless communication system. In this letter, a novel signal processing framework for massive MIMO-NOMA data transmission is proposed based on group-level successive interference cancellation (GLSIC), in which the users are grouped according to the distances to the base station (BS) and NOMA is applied among different groups by employing GLSIC to reduce the inter-group interference. The uplink sum rate is derived for the proposed GLSIC-based massive MIMO-NOMA system with minimum mean square error channel estimator and maximal ratio combiner at BS, and the effects of channel estimation error, inter-group pilot contamination, and imperfect GLSIC are analytically quantified in the derivation. Theoretical analysis and simulation results show that, under the same conditions, the proposed GLSIC-based system achieves higher uplink rate than the existing cluster-based MIMO-NOMA system with user-level SIC.