Aiming to circumvent the severe large-scale fading and the energy scarcity dilemma in high-altitude platform (HAP) networks, this paper investigates the benefits of the reconfigurable intelligent surface (RIS) and simultaneous wireless information and power transfer (SWIPT) on HAP communications. Specifically, we propose a concept of multi-layer refracting RIS-assisted receiver to achieve concurrent transmission of the information and energy, which is conducive to overcoming the severe fading effect induced by extreme long-distance HAP links and fully exploits RIS’s degrees-of-freedom (DoFs) for the SWIPT design. Based on the RIS-enhanced receiver, we then formulate a worst-case sum-rate maximization problem by considering the channel state information (CSI) error, the information rate requirements, and the energy harvesting constraint. To handle the intractable non-convex problem, a scalable robust optimization framework is proposed to obtain semi-closed-form solutions. Specifically, a discretization method is adopted to convert the imperfect CSI into a robust one. Then, by utilizing the LogSumExp inequality to smooth the objective and constraints, we develop a dual method to obtain the optimal solution for the HAP transmit precoder. In addition, a modified cyclic coordinate descent (M-CCD) is adopted to update the block-wise RIS coefficients. Moreover, closed-form solutions for power splitting (PS) ratios and the receive decoder are derived. Finally, the asymptotic performance of our proposed RIS-enhanced receiver is provided to reveal the substantial capacity gain for HAP communications. Numerical simulations demonstrate that the proposed architecture and optimization framework are capable of achieving superior performance with low complexity compared to state-of-the-art schemes in HAP networks.

This article proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: i) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; ii) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; iii) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.

This article integrates nonorthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omnidirectional coverage extension. To capture the impact of nonideal wireless channels on AirFL, a closed-form expression for the optimality gap (also known as the convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes, as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with a linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer nonlinear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust-region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled nonconvex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that: 1) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications; 2) our algorithms achieve a faster convergence rate on independent and identically distributed (IID) and non-IID settings compared to the existing baselines; and 3) both the spectrum efficiency and learning performance are significantly improved with the aid of the well-tuned STAR-RIS.

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a reconfigurable intelligent surface (RIS)-aided hybrid network by leveraging the RIS to flexibly adjust the decoding order of heterogeneous data. A new metric of computation rate is defined to measure the performance of AirFL users. Upon this, the objective of this work is to maximize the achievable hybrid rate by jointly optimizing the transmit power, controlling the receive scalar, and designing the reflection coefficients. Since the concurrent transmissions of all computation and communication signals are aided by the discrete phase-shifting elements at the RIS, the formulated problem (P0) is a challenging mixed-integer programming problem. To tackle this intractable issue, we decompose the original problem (P0) into a non-convex problem (P1) and a combinatorial problem (P2), which are characterized by the continuous and discrete variables, respectively. For the transceiver design problem (P1), the power allocation subproblem is first solved by difference-of-convex programming, and then the receive control subproblem is addressed by successive convex approximation, where the closed-form expressions of simplified cases are derived to obtain deep insights. For the reflection design problem (P2), a relaxation-then-quantization method is adopted to find a suboptimal solution for striking a trade-off between complexity and performance. Afterwards, an alternating optimization algorithm is developed to solve the non-linear non-convex problem (P0) iteratively. Finally, simulation results reveal that i) the proposed RIS-aided hybrid network can support on-demand communication and computation efficiently, ii) the system performance can be improved by properly selecting the location of the RIS, and iii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.

The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this article resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of the uplink nonorthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence and 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.

We propose a robust transceiver design for a covert integrated sensing and communications (ISAC) system with imperfect channel state information (CSI). Considering both bounded and probabilistic CSI error models, we formulate worst-case and outage-constrained robust optimization problems of joint transceiver beamforming and radar waveform design to balance the radar performance of multiple targets while ensuring the communications performance and covertness of the system. The optimization problems are challenging due to the non-convexity arising from the semi-infinite constraints (SICs) and the coupled transceiver variables. In an effort to tackle the former difficulty, S-procedure and Bernstein-type inequality are introduced for converting the SICs into finite convex linear matrix inequalities (LMIs) and second-order cone constraints. A robust alternating optimization framework referred to alternating double-checking is developed for decoupling the transceiver design problem into feasibility-checking transmitter- and receiver-side subproblems, transforming the rank-one constraints into a set of LMIs, and verifying the feasibility of beamforming by invoking the matrix-lifting scheme. Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm in improving the performance of covert ISAC systems.