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.

The increasing demands for massive connectivity, low latency, and high reliability of future communication networks require new techniques. Multiple-input-multiple-output non-orthogonal multiple access (MIMO-NOMA), which incorporates the NOMA concept into MIMO, is an appealing technology to enhance system throughput and energy efficiency. However, rapidly changing channel conditions and extremely complex spatial structure degrade the system performance and hinder its application. Thus, to tackle these limitations, in this paper, we propose a deep learning-based MIMO-NOMA framework for maximizing the sum data rate and energy efficiency. To be specific, we design an effective communication deep neural network (CDNN) in which several convolutional layers and multiple hidden layers are included. Thanks to the impressive representation ability of the deep learning technique, the CDNN framework addresses the power allocation problem for achieving higher data rate and energy efficiency of MIMO-NOMA with the aid of training algorithms. Additionally, simulation results corroborate that the proposed CDNN framework is a good candidate to enhance the performance of MIMO-NOMA in term of power allocation, and extensive simulations show that it realizes larger sum data rate and energy efficiency compared with conventional strategies.

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.