Movable antenna (MA) has been recognized as a promising technology to improve communication performance in future wireless networks such as 6G. To unleash its potential, this paper proposes a novel architecture, namely extremely large-scale MA (XL-MA), which allows flexible antenna/subarray positioning over an extremely large spatial region for effectively enhancing near-field effects and spatial multiplexing performance. In particular, this paper studies an uplink XL-MA-enabled multiuser system, where single-antenna users distributed in a coverage area are served by a base station (BS) equipped with multiple movable subarrays. We begin by presenting a spatially non-stationary channel model to capture the near-field effects, including position-dependent large-scale channel gains and line-of-sight visibility. To evaluate system performance, we further derive a closed-form approximation of the expected weighted sum rate under maximum ratio combining (MRC), revealing that optimizing XL-MA placement enhances user channel power gain to increase desired signal power and reduces channel correlation to decreases multiuser interference. Building upon this, we formulate an antenna placement optimization problem to maximize the expected weighted sum rate, leveraging statistical channel conditions and user distribution. To efficiently solve this challenging non-linear binary optimization problem, we propose a polynomial-time successive replacement algorithm. Simulation results demonstrate that the proposed XL-MA placement strategy achieves near-optimal performance, significantly outperforming benchmark schemes based on conventional fixed-position antennas.

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP’s effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP’s capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency.

In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To achieve this, we assume that there is a given set of candidate sites in the target area for IRS deployment and divide the area into multiple grids of identical size. Then, we derive the average channel power gains from the BS to the IRS at each candidate site and from this IRS to any grid in the target area in terms of IRS parameters, including its size, position, height, and orientation. Thus, we are able to approximate the average cascaded channel power gain from the BS to each grid via any IRS, introducing an effective IRS reflection gain based on the large-scale channel knowledge only. Next, we formulate a multi-IRS deployment optimization problem to minimize the total deployment cost by selecting a subset of candidate sites for deploying IRSs and jointly optimizing their heights, orientations, and numbers of reflecting elements while satisfying a given coverage rate performance requirement over all grids in the target area. To solve this challenging combinatorial optimization problem, we first reformulate it as an integer linear programming problem and solve it optimally using the branchand- bound (BB) algorithm. In addition, we propose an efficient successive refinement algorithm to further reduce computational complexity. Simulation results demonstrate that the proposed lower-complexity successive refinement algorithm achieves near-optimal performance but with significantly reduced running time compared to the proposed optimal BB algorithm, as well as superior performance-cost trade-off compared to other baseline IRS deployment strategies.

In this article, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local model updates in each communication round. We formulate a total latency minimization problem with probabilistic device selection, taking into account both the communication and computation latency in the whole FL procedure. However, it is highly challenging to optimally solve this problem due to the coupling issues of model convergence and latency minimization problem caused by the heterogeneity of local model updates. Through convergence analysis, we reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting subproblems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulation results show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve up to 47.04% single-round latency reduction.

Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In this paper, we study a multi-passive/active-IRS (PIRS/AIRS) aided wireless network with a multi-antenna base station (BS) in a given region. First, we divide the region into multiple non-overlapping cells, each of which may contain one candidate location that can be deployed with a single PIRS or AIRS. Then, we show several trade-offs between minimizing the total IRS deployment cost and enhancing the signal-to-noise ratio (SNR) performance over all cells via direct/cascaded LoS transmission with the BS. To reconcile these trade-offs, we formulate a joint multi-PIRS/AIRS deployment problem to select an optimal subset of all candidate locations for deploying IRS and also optimize the number of passive/active reflecting elements deployed at each selected location to satisfy a given SNR target over all cells, such that the total deployment cost is minimized. However, due to the combinatorial optimization involved, the formulated problem is difficult to be solved optimally. To tackle this difficulty, we first optimize the reflecting element numbers with given PIRS/AIRS deployed locations via sequential refinement, followed by a partial enumeration to determine the PIRS/AIRS locations. Simulation results show that our proposed algorithm achieves better cost-performance trade-offs than other baseline deployment strategies.

Federated learning (FL) has emerged as a promising alternative to centralized machine learning for exploiting large amounts of data generated by networks while ensuring data privacy. Unlike previous FL works that rely on terrestrial base stations, this paper studies an unmanned aerial vehicle (UAV)-assisted FL system where a UAV collects local models from distributed ground devices. By leveraging the UAV’s high altitude and mobility, it can proactively establish short-distance line-of-sight links with devices to mitigate the communication straggler effect and improve communication efficiency in FL. Specifically, we present the convergence analysis of FL without convexity assumptions, demonstrating the effect of device scheduling on the global gradients. Based on the derived convergence bound, we aim to minimize the completion time of FL training by jointly optimizing device scheduling, UAV trajectory, and time allocation. This problem explicitly incorporates the devices’ energy budgets, dynamic channel conditions, and convergence accuracy of FL constraints. Despite the non-convexity of the formulated problem, we exploit its structure to decompose it into two sub-problems and further derive the closed-form solutions via the Lagrange dual ascent method. Simulation results show that the proposed design significantly improves the tradeoff between completion time and test accuracy compared to existing benchmarks.

In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local updates in each communication round. We formulate a total latency minimization problem, taking into account both the communication and computation latency in the whole FL procedure. We reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting sub-problems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulations show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve single-round latency reduction.

In this paper, we study unmanned aerial vehicles (UAVs) assisted wireless data aggregation (WDA) in multi-cluster networks, where multiple UAVs simultaneously perform different WDA tasks via over-the-air computation (AirComp) without terrestrial base stations. This work focuses on maximizing the minimum amount of WDA tasks performed by each cluster by optimizing the UAV trajectory and transceiver design as well as cluster scheduling and association, while considering the WDA accuracy requirement. Such a joint design is critical for interference management in multi-cluster AirComp networks, via enhancing the signal quality between each UAV and its associated cluster for signal alignment while reducing the inter-cluster interference between each UAV and its non-associated clusters. Although it is generally challenging to optimally solve the formulated non-convex mixed-integer nonlinear programming, an efficient iterative algorithm as a compromise approach is developed by exploiting bisection and block coordinate descent methods, yielding an optimal transceiver solution in each iteration. The optimal binary variables and a suboptimal trajectory are obtained by using the dual method and successive convex approximation, respectively. Simulations show the considerable performance gains of the proposed design over benchmarks and the superiority of deploying multiple UAVs in increasing the number of performed tasks while reducing access delays.

Pliable index coding (PICOD) has recently been regarded as a promising solution that exploits the coding advantage to improve communication efficiency of content-type systems (e.g., recommendation system), where clients are pliable and are interested in receiving any new message that they do not have. PICOD aims to find an effective coding strategy that satisfies the demands of all clients with the minimum number of transmissions. However, most of the previous works mainly provided theoretical understanding on PICOD in special instances based on greedy algorithms. In contrast, in this paper, we present a flexible sparse and low-rank matrix modeling approach to minimize the number of transmissions for the general PICOD problems. This is achieved by establishing generalized pliable alignment conditions to guarantee the requirements of all clients. As the resulting non-convex problem is highly intractable, we further develop an alternating pursuit framework to detect the rank of the matrix to be recovered by using the rank-increasing strategy. To address the feasibility-detection issues in the existing methods, we propose an alternating projection algorithm, which admits closed-form expressions and avoids excessive sparsity inducing. Moreover, we establish the global convergence of the alternating projection algorithm with random initial points. Simulation results demonstrate that the proposed alternating pursuit algorithm significantly reduces the number of transmissions compared to the state-of-the-art methods.

Different from the existing works that focus on transceiver design of over-the-air computation (AirComp) over static networks, we in this paper consider an unmanned aerial vehicle (UAV) aided AirComp system, where the UAV as a flying base station aggregates data from mobile sensors. The trajectory design of the UAV provides an additional degree of freedom to improve the performance of AirComp. We aim to minimize the time-averaged mean-squared error (MSE) of AirComp by jointly optimizing the UAV trajectory, receive normalizing factors, and sensors’ transmit power. To this end, we first propose a novel and equivalent problem transformation by introducing intermediate variables. This reformulation leads to a convex subproblem when fixing any other two blocks of variables, thereby enabling efficient algorithm design based on the principle of block coordinate descent and alternating direction method of multipliers (ADMM) techniques. In particular, we derive the optimal closed-form solutions for normalizing factors and intermediate variables optimization subproblems. We also recast the convex trajectory design subproblem into an ADMM form and obtain the closed-form expressions for each variable updating. Simulation results show that the proposed algorithm achieves a smaller time-averaged MSE while reducing the simulation time by orders of magnitude compared to state-of-the-art algorithms.

Intelligent Reflecting Surface (IRS) is a promising technology that is able to manipulate the wireless propagation channels via smartly adjusting the signal reflection. With continuous phase shifts, IRS has been shown to be effective in enlarging the achievable rate region. In this paper, we investigate the achievable rate region of a IRS-aided multi-user interference channel, where the phase shifts at the IRS can only take a finite number of discrete values. We formulate a multi-objective optimization problem (MOOP) to characterize the achievable rate region. The commonly adopted approaches such as the rate profile method fail to solve MOOP with optimization variables. Although the exhaustive search method can obtain the Pareto-optimal solutions, it suffers from high computational complexity. To this end, we propose a computationally efficient active learning algorithm via Gaussian process (GP). By modeling the objectives of MOOP as a draw from a GP distribution with only a few randomly computed rate-tuples, the active learning algorithm can quickly dominate the non-optimal points and find Pareto-optimal points without calculating rate-tuples. Numerical simulations demonstrate that the achievable rate region of IRS-aided interference channel is much larger than that without IRS and the proposed active learning framework obtains near-optimal Pareto solutions with a much lower computational complexity than the traditional exhaustive search algorithm.

Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance transmission due to the distortion induced by the channel fading and noise. To unleash the full potential of AirComp, this paper proposes to use a low-cost unmanned aerial vehicle (UAV) acting as a mobile base station to assist AirComp systems. Specifically, due to its controllable high-mobility and high-altitude, the UAV can move sufficiently close to the sensors to enable line-of-sight transmission and adaptively adjust all the links' distances, thereby enhancing the signal magnitude alignment and noise suppression. Our goal is to minimize the time-averaging mean-square error for AirComp by jointly optimizing the UAV trajectory, the scaling factor at the UAV, and the transmit power at the sensors, under constraints on the UAV’s predetermined locations and flying speed, sensors’ average and peak power limits. However, due to the highly coupled optimization variables and time-dependent constraints, the resulting problem is non-convex and challenging. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques. Simulation results verify the convergence of the proposed algorithm and demonstrate the performance gains and robustness of the proposed design compared with benchmarks.

Power-domain non-orthogonal multiple access (NOMA) has become a promising technology to exploit the new dimension of the power domain to enhance the spectral efficiency of wireless networks. However, most existing NOMA schemes rely on the strong assumption that users’ channel gains are quite different, which may be invalid in practice. To unleash the potential of power-domain NOMA, we propose a reconfigurable intelligent surface (RIS)-empowered NOMA scheme to introduce desirable channel gain differences among the users by adjusting the phase shifts at the RIS. Our goal is to minimize the total transmit power by jointly optimizing the beamforming vectors at the base station, the phase-shift matrix at the RIS, and user ordering. To address challenge due to the highly coupled optimization variables, we present an alternating optimization framework to decompose the non-convex bi-quadratically constrained quadratic problem under a specific user ordering into two rank-one constrained matrices optimization problems via matrix lifting. To accurately detect the feasibility of the non-convex rank-one constraints and improve performance by avoiding early stopping in the alternating optimization procedure, we equivalently represent the rank-one constraint as the difference between nuclear norm and spectral norm. A difference-of-convex (DC) algorithm is further developed to solve the resulting DC programs via successive convex relaxation, followed by establishing the convergence of the proposed DC-based alternating optimization method. We further propose an efficient user ordering scheme with closed-form expressions, considering both the channel conditions and users’ target data rates. Simulation results validate the ability of an RIS in enlarging the channel-gain difference when the users’ original channel conditions are similar and the superiority of the proposed DC-based alternating optimization method in reducing the total transmit power.

Over-the-air computation (AirComp) is a promising technology that is capable of achieving fast data aggregation in Internet of Things (IoT) networks. The mean-squared error (MSE) performance of AirComp is bottlenecked by the unfavorable channel conditions. This limitation can be mitigated by deploying a reconfigurable intelligent surface (RIS), which reconfigures the propagation environment to facilitate the receiving power equalization. The achievable performance of RIS relies on the availability of accurate channel state information (CSI), which however is generally difficult to be obtained. In this paper, we consider an RIS-aided AirComp IoT network, where an access point (AP) aggregates sensing data from distributed devices. Without assuming any prior knowledge on the underlying channel distribution, we formulate a stochastic optimization problem to maximize the probability that the MSE is below a certain threshold. The formulated problem turns out to be non-convex and highly intractable. To this end, we propose a data-driven approach to jointly optimize the receive beamforming vector at the AP and the phase-shift vector at the RIS based on historical channel realizations. After smoothing the objective function by adopting the sigmoid function, we develop an alternating stochastic variance reduced gradient (SVRG) algorithm with a fast convergence rate to solve the problem. Simulation results demonstrate the effectiveness of the proposed algorithm and the importance of deploying an RIS in reducing the MSE outage probability.

In this paper, we consider the convolutional phase retrieval problem, which is a crucial and challenging problem in signal processing and wireless communication. Our goal is to recover a signal from phaseless measurements. To address the challenge of phaseless measurements, we recast the recovery problem into a low-rank matrix optimization problem via matrix lifting. To further exactly detect the feasibility of resulting fixed-rank constraint, we propose a novel difference of convex functions (DC) representation for the rank function by exploiting the difference between trace norm and spectral norm, followed by presenting an efficient algorithm to solve the resulting DC programming. The simulation results demonstrate that our proposed algorithm outperforms the existing convex relaxation methods in terms of signal recovery sample complexity and the noise robustness.

The adoption of reconfigurable intelligent surface (RIS) in wireless networks can enhance the spectrum- and energy-efficiency by controlling the propagation environment. Although the RIS does not consume any transmit power, the circuit power of the RIS cannot be ignored, especially when the number of reflecting elements is large. In this paper, we propose the joint design of beamforming vectors at the base station, active RIS set, and phase-shift matrices at the active RISs to minimize the network power consumption, including the RIS circuit power consumption, while taking into account each user's target data rate requirement and each reflecting element's constant modulus constraint. However, the formulated problem is a mixed-integer quadratic programming (MIQP) problem, which is NP-hard. To this end, we present an alternating optimization method, which alternately solves second order cone programming (SOCP) and MIQP problems to update the optimization variables. Specifically, the MIQP problem is further transformed into a semidefinite programming problem by applying binary relaxation and semidefinite relaxation. Finally, an efficient algorithm is developed to solve the problem. Simulation results show that the proposed algorithm significantly reduces the network power consumption and reveal the importance of taking into account the RIS circuit power consumption.

Massive connectivity has become a critical requirement for Internet-of-Things (IoT) networks, where a large number of devices need to connect to an access-point sporadically. Moreover, low-latency communication and sporadic device traffic are essential to support intelligent services in IoT networks. In this paper, to support low-latency communication for massive devices with sporadic traffic, we present a sparse blind demixing to simultaneously detect the active devices and decode multiple source signals without a priori channel state information in multi- in-multi-out (MIMO) networks. To address the unique challenges of bilinear measurements and sporadic device activity detection, we recast the estimation problem as a sparse and low-rank optimization problem via matrix lifting. We further propose a difference-of-convex-functions (DC) representation for the rank function to guarantee the exact rank constraint, followed by ignoring the non-convex group sparse function. This is achieved by exploiting the difference between nuclear norm and the convex Ky Fan k- norm for a rank function representation. We then develop an efficient DC algorithm to solve the resulting non-convex DC program without regularization parameter. Numerical results demonstrate that the proposed DC approach is able to exactly recover the ground truth signals with reduced sample sizes, as well as achieve better performance against noise compared with the existing convex methods.

Optical wireless communication becomes a key enabling technology for achieving ultra-high data rate requirements in beyond 5G systems. In this paper, to reduce both channel signaling overhead and hardware cost in optical wireless communications, we present a blind deconvolutional phase retrieval approach to recover the source signals from phaseless measurements without a priori channel information. To deal with the coupled challenges of phaseless measurements and bilinear signaling model, we recast the signal recovery problem into a rank-one matrices recovery problem via matrix lifting, followed by relaxing each phaseless matrix measurement into its convex hull. We further propose a difference-of-convex-functions (DC) programming algorithm to solve the low-rank matrix optimization problem. This is achieved by proposing the DC representation for the rank function based on the convex Ky Fan k-norm, thereby exactly detecting the fixed-rank constraints. The numerical results demonstrate that the proposed DC approach outperforms the state-of-the-art methods in terms of signal recovery performance and the robustness to the noise.