In the past as well as present wireless communication systems, the wireless propagation environment is regarded as an uncontrollable black box that impairs the received signal quality, and its negative impacts are compensated for by relying on the design of various sophisticated transmission/reception schemes. However, the improvements through applying such schemes operating only at two endpoints (i.e., transmitter and receiver) are limited even after five generations of wireless systems. Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) have emerged as a new and promising technology that can configure the wireless environment in a favorable manner by properly tuning the phase shifts of a large number of quasi passive and low-cost reflecting elements, thus standing out as a promising candidate technology for the next/sixth-generation (6G) wireless system. However, to reap the performance benefits promised by RIS/IRS, efficient signal processing techniques are crucial, for a variety of purposes such as channel estimation, transmission design, radio localization, and so on. In this paper, we provide a comprehensive overview of recent advances on RIS/IRS-aided wireless systems from the signal processing perspective.We also highlight promising research directions that are worthy of investigation in the future.

The affordable hardware cost of ultra-large (XL) reconfigurable intelligent surfaces (RIS) renders them attractive solutions for the performance enhancement of localization and communication systems. However, XL-RIS results in near-field propagation channels, especially for the high-frequency terahertz (THz) communication system, which poses significant challenges for localization and channel estimation. In this paper, we focus on the spherical wavefront propagation in the near field of the THz system with the assistance of a RIS. A near-field channel estimation and localization (NF-JCEL) algorithm is proposed based on the derived second-order Fresnel approximation of the near-field channel model. To be specific, we carefully devise a down-sampled Toeplitz covariance matrix, which enables the decoupling and separate estimation of user equipment (UE) distances and angles of arrival (AoAs). Using the sub-space based method and one-dimensional search, we estimate the angles of arrival (AoAs) and user equipment (UE) distances. The channel attenuation coefficients are obtained through the least square (LS) method. To alleviate the impact of THz channel fading peaks caused by molecular absorption, estimates on multiple sub-bands are utilized for location estimation. Simulation results validate the superiority of the proposed NF-JCEL algorithm to the conventional far-field algorithm and show that higher resolution accuracy can be obtained by the proposed algorithm.

In this letter, unmanned aerial vehicles (UAVs) and intelligent reflective surface (IRS) are utilized to support terahertz (THz) communications. To this end, the joint optimization of UAV's trajectory, the phase shift of IRS, the allocation of THz sub-bands, and the power control are investigated to maximize the minimum average achievable rate of all users. An iteration algorithm based on successive Convex Approximation with the Rate constraint penalty (CAR) is developed to obtain UAV's trajectory, and the IRS phase shift is formulated as a closed-form expression with introduced pricing factors. Simulation results show that the proposed scheme significantly enhances the rate performance of the whole system.

This paper studies energy efficient resource allocation for a machine-to-machine enabled cellular network with nonlinear energy harvesting, especially focusing on two different multiple access strategies, namely nonorthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA.

Mobile edge computing (MEC) has emerged as a promising technique to enhance computing capacity of mobile devices by offloading tasks to base station for remote execution. This letter considers a MEC system exploiting the non-orthogonal multiple access (NOMA) for both task uploading and result downloading. The total energy consumption is minimized by optimizing the transmit powers, transmission time allocation, and task offloading partitions. Specifically, after full analysis of the optimal power control solutions for both uplink and downlink, an iterative algorithm implemented by successive convex approximation is proposed to obtain the task offloading partitions and time allocation. Numerical results show that the proposed NOMA-based MEC offloading scheme can significantly reduce the system energy consumption compared with traditional orthogonal multiple access scheme.

This letter investigates the power control and channel assignment problem in device-to-device (D2D) communications underlaying a non-orthogonal multiple access (NOMA) cellular network. With the successive interference cancellation decoding order constraints, our target is to maximize the sum rate of D2D pairs while guaranteeing the minimum rate requirements of NOMA-based cellular users. Specifically, the optimal conditions for power control of cellular users on each subchannel are derived first. Then, based on these results, we propose a dual-based iterative algorithm to solve the resource allocation problem. Simulation results validate the superiority of proposed resource allocation algorithm over the existing orthogonal multiple access scheme.