IRS-Enabled Backscattering in a Downlink Non-Orthogonal Multiple Access System

Abstract

Intelligent reflecting surface (IRS)-enabled backscatter communications can be enabled by an access point (AP) that splits its transmit signal into modulated and unmodulated parts. This letter integrates non-orthogonal multiple access (NOMA) with this method to create a twouser primary system and a secondary system of IRS data. Considering the decoding order, we maximize the rate of the strongest primary user by jointly optimizing the IRS phase shifts, power splitting (PS) factor at the AP, and NOMA power coefficients while guaranteeing the quality of service (QoS) for both weak user and IRS data in the primary and secondary systems, respectively. The resulting optimization problem is non-convex. Thus, we split it into three parts and develop an alternating optimization (AO) algorithm. The advantage is that we derive closed-form solutions for the PS factor and NOMA power coefficients in the first two parts. In the third part, we optimize the phase shifts by exploiting semi-definite relaxation (SDR) and penalty techniques to handle the unit-modulus constraints. This algorithm achieves substantial gains (e.g., 40-68%) compared to relevant baseline schemes.

Double-Faced Active Intelligent Reflecting Surfaces-Assisted Symbiotic Radio Communications

“…In addition to the above, IRS-assisted SR has been adopted in non-orthogonal multiple access (NOMA) systems in [20], [21]. In [20], the IRS is deployed to relay the modulated part of the signal of an access point (AP) in a two-user primary NOMA system and send its own information over the unmodulated part of the AP signal.…”

“…In addition to the above, IRS-assisted SR has been adopted in non-orthogonal multiple access (NOMA) systems in [20], [21]. In [20], the IRS is deployed to relay the modulated part of the signal of an access point (AP) in a two-user primary NOMA system and send its own information over the unmodulated part of the AP signal. In this work, the achieved rate of the strongest primary user is maximized through the joint optimization of the power splitting factors at the AP, the NOMA power coefficients, and the IRS's phase shifts under the quality-of-service constraints of the weak user and the IRS data.…”

“…The optimal solutions for ( 20) are the eigenvectors that correspond to the largest eigenvalues of V r and V t , which for simplicity of the notation will be denoted in what follows as the largest eigenvectors. The constraints in (20) are still non-convex and thus will be converted into convex constraints through a two-layer iterative approximation. Assume there is a feasible solution in the r th outer iteration and in the i th inner iteration, i.e., V (r,i) r and V (r,i) t…”

Signal Detection in Ambient Backscatter Systems: Fundamentals, Methods, and Trends

“…2) IRS-enabled BackCom: The IRS plays the role of a transmitter in this concept. It also passively reflects signals for ongoing links as well as transmits its own data [165]. Embedded sensors can provide this type of data, [159], [166].…”

Deep-Q Reinforcement Learning based Resource Allocation in Wireless Communication Networks

“…CONVENTIONAL OPTIMIZATION FRAMEWORK P 1 is non-convex due to the involved products of the optimization variables in the objective and constraints. We thus resort to alternative optimization (AO) techniques, splitting P 1 into three sub-problems, i.e., transmit beamforming, receiver beamforming, and tags' reflection coefficient optimization [25], [27]. This approach iteratively optimizes a variable or set of variables while keeping the rest of the variables fixed until convergence is achieved [28], [29].…”