Low-complexity Iterative Algorithm Research Articles

IRS-assisted UAV wireless powered communication network for sustainable federated learning

Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are indispensable potential technologies in the future sixth-generation mobile communication technology (6 G). Utilizing the high beamforming gain of IRS and the high mobility of UAV can achieve ubiquitous network coverage and ensure a high-quality communication environment. Wireless Powered Communication Network (WPCN) is an emerging green communication technology network that converts received radio frequency (RF) signals into electrical energy to power Federated Learning (FL) users. FL users perform local computing and model transmission through the collected energy, ensuring the sustainability of FL. In order to solve the problems of complex communication environment, privacy protection, and energy constraints on terminal devices, we design an FL system based on an IRS-assisted UAV wireless power communication network, which minimizes the UAV transmission energy by jointly optimizing UAV location, IRS phase shift, and resource allocation strategies. We use a low-complexity iterative algorithm to solve this complex non-convex problem. The simulation results show that the performance of the proposed algorithm is obviously better than that of other benchmark schemes, indicating that joint optimization plays an essential role in improving the performance of the system.

Pre-filters design for weighted sum rate maximization in multiuser time reversal downlink systems

In high-speed multiuser Time Reversal (TR) downlink systems, the transmission rate is degraded due to the presence of severe inter-user and inter-symbol interference. Moreover, maximizing the weighted sum rate in such systems is a critical objective, since the weighting factors represent the priority of different users in different applications. However, it faces significant challenges as it is an NP-hard and non-convex problem. In order to suppress these interferences and maximize the weighted sum rate, in this paper we present a novel approach for the joint design of the pre-filters. The proposed method applies successive convex approximation to transform the original problem into a Second-Order Cone Programming (SOCP) problem. Then, a low-complexity iterative algorithm is provided to effectively solve the resulting SOCP problem. According to the simulation results, the proposed method reaches a local optimum within a few iterations and demonstrates superior performance in terms of weighted sum rate compared to the current algorithm.

Sampling for Remote Estimation of the Wiener Process over an Unreliable Channel

In this paper, we study a sampling problem where a source takes samples from a Wiener process and transmits them through a wireless channel to a remote estimator. Due to channel fading, interference, and potential collisions, the packet transmissions are unreliable and could take random time durations. Our objective is to devise an optimal causal sampling policy that minimizes the long-term average mean square estimation error. This optimal sampling problem is a recursive optimal stopping problem, which is generally quite difficult to solve. However, we prove that the optimal sampling strategy is, in fact, a simple threshold policy where a new sample is taken whenever the instantaneous estimation error exceeds a threshold. This threshold remains a constant value that does not vary over time. By exploring the structure properties of the recursive optimal stopping problem, a low-complexity iterative algorithm is developed to compute the optimal threshold. This work generalizes previous research by incorporating both transmission errors and random transmission times into remote estimation. Numerical simulations are provided to compare our optimal policy with the zero-wait and age-optimal policies.

Network sum-rate maximization for network-coded clustered uplink NOMA networks with SWIPT-enabled relays

This paper considers network sum-rate maximization for network-coded clustered uplink non-orthogonal multiple-access (NOMA) networks with simultaneous wireless information and power transfer (SWIPT)-enabled relays. Specifically, the aim is to perform joint power allocation, power-splitting, and relay selection (J-PA-PS-RS), subject to quality-of-service (QoS) requirements. The formulated problem is excessively computationally-intensive, as it is a non-convex optimization problem. To efficiently solve it, it is split into two sub-problems: (1) joint power allocation and power-splitting per relay, and (2) relay selection. Particularly, a solution procedure is proposed, where the joint user and relay power allocation, and power-splitting per relay sub-problem is solved via a low-complexity iterative three-layer algorithm, and then followed by optimal relay selection. Simulation results have revealed that the proposed solution procedure can efficiently be used to maximize the network sum-rate, achieve near-optimal solutions, and outperform other benchmark schemes, while satisfying QoS constraints.

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pilot overhead as well as substantial computational complexity. With benefits from the powerful detection characteristics of MIMO radar, we aim for designing a novel sensing-assisted semi-blind detection scheme in this paper, where both the inherent low-rankness of signal matrix and the essential knowledge about geometric environments are fully exploited under a designated cooperative manner. Specifically, to efficiently recover the channel factorizations via the formulated low-rank matrix completion problem, a low-complexity iterative algorithm stemming from the alternating steepest descent (ASD) method is adopted to obtain the solutions in case of unknown noise statistics. Moreover, we take one step forward by employing the denoising convolutional neural network (DnCNN) to preprocess the received signals due to its favorable performance of handling Gaussian denoising. The overall paradigm of our proposed scheme consists of three stages, namely (1) target parameter sensing, (2) communication signal denoising and (3) semi-blind detection refinement. Simulation results show that significant estimation gains can be achieved by the proposed scheme with reduced training overhead in a variety of system settings.

A low-complexity iterative algorithm for multiuser millimeter-wave systems

A low-complexity iterative algorithm for multiuser millimeter-wave systems

Joint Task Offloading and Resource Allocation for Space–Air–Ground Collaborative Network

The space–air–ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the issue of spectrum demand for the HAP drone to meet the access of a large number of users. In addition, the long propagation distance between devices and the HAP drone, and between the HAP drone and LEO satellites, will lead to high data transmission energy consumption. Motivated by these factors, we introduce a space–air–ground collaborative network that employs the non-orthogonal multiple access (NOMA) technique, enabling all ground devices to access the HAP drone. Therefore, all devices can share the same communication spectrum. Furthermore, the HAP drone can process part of the ground devices’ tasks locally, and offload the rest to satellites within the visible range for processing. Based on this system, we formulate a weighted energy consumption minimization problem considering power control, computing frequency allocation, and task-offloading decision. The problem is solved by the proposed low-complexity iterative algorithm. Specifically, the original problem is decomposed into interconnected coupled subproblems using the block coordinate descent (BCD) method. The first subproblem is to optimize power control and computing frequency allocation, which is solved by a convex algorithm after a series of transformations. The second subproblem is to make an optimal task-offloading strategy, and we solve it using the concave–convex procedure (CCP)-based algorithm after penalty-based transformation on binary variables. Simulation results verify the convergence and performance of the proposed iterative algorithm compared with the two benchmark algorithms.

Energy-Efficient Resource Allocation for Backscatter-Assisted Wireless Powered MEC

In this paper, we study the energy-efficient resource allocation for a backscatter-assisted wireless powered mobile edge computing (MEC) network. Considering the binary offloading mode and the limited computation capacity of the MEC server, we propose to minimize the MEC server's energy consumption by jointly optimizing the binary offloading decision of each IoT node, the computing frequencies and time at the MEC server and IoT nodes, the backscatter time and reflection coefficients of IoT nodes, as well as the MEC server's transmit power and time. It is formulated into a non-convex mixed integer programming problem. To solve this problem, we first obtain the optimal computing time and frequency for the MEC server and the optimal computing time of each IoT node based on the proof by contradiction and the monotonicity of the objective function, then we decouple the resulting simplified problem into two subproblems and transform each subproblem into a convex one. On this basis, we propose a low-complexity iterative algorithm to solve the simplified problem by solving the two convex subproblems alternately. Simulation results verify the fast convergence of the proposed iterative algorithm and its superiority over the baseline schemes in terms of the MEC server's energy consumption.

Federated learning via over-the-air computation in IRS-assisted UAV communications

Intelligent reflective surface (IRS) and unmanned aerial vehicle (UAV) communication are two key technologies in the sixth generation of mobile communication (6G). In this paper, IRS is equipped on UAV to form aerial IRS, which can achieve 360° panoramic full-angle reflection and flexible deployment of IRS. In order to achieve high-quality and ubiquitous network coverage under data privacy and low latency requirements, we propose an Federated learning (FL) network via Over-the-Air computation (AirComp) in IRS-assisted UAV communications. Our goal is to minimize the worst-case mean square error (MSE) by jointly optimizing the IRS phase shift, denoising factor for noise suppression, the user’s transmission power, and UAV trajectory. Optimizing and quickly adjusting the UAV position and IRS phase shift, it flexibly assists the signal transmission between users and base stations (BS). In order to solve this complex non-convex problem, we propose a low-complexity iterative algorithm, which divides the original problem into four sub-problems, respectively using the semi-definite programming (SDP) method, slack variable introduction method, successive convex approximation (SCA) method to solve each sub-problem. Through the analysis of simulation results, our proposed design scheme is obviously better than other benchmark schemes.

Secure Task Offloading for MEC-Aided-UAV System

The promise of unmanned aerial vehicles (UAVs) combined with the mobile edge computing (MEC), named MECaided-UAV extends the MEC application to offer new flexible, low-latency computing services and considerable utilities for pervasive sensing of the world. In MEC, the UAV with limited onboard energy and computation resources needs to offload its tasks to resource-rich ground base stations (GBSs) servers. Because of the broadcast nature of line-of-sight (LoS) channels, one of the key challenges in task offloading is to guarantee that confidential data is offloaded safely to the GBSs without being intercepted by eavesdroppers (Eves). In this MEC-aided-UAV system, the GBSs help the UAV compute the offloaded tasks and transmit the artificial noise (AN) to suppress the vicious Eves.We make the first attempt to study the maximum-minimum average secrecy capacity problem, including joint optimization of the trajectory and transmit power of the UAV, the transmit power of AN, the local computation ratio, and the selection of GBSs with consideration of the practical constraints of completion delay of the tasks, maximum velocity, and the power consumption. The optimization issue is confirmed as a mixed-integer nonconvex problem. Thereafter, a low-complexity iterative algorithm with the block coordinate descent method and successive convex approximation technique is put forward to get its suboptimal solution. In addition, the convergent solution can be achieved by solving the subproblems in turn. Evaluation results validate that the proposed secure offloading scheme significantly effectiveness the baselines by 17.4%-71.2% on the maximum-minimum average secrecy capacity

An Optimization Framework for General Rate Splitting for General Multicast

Immersive video, such as virtual reality (VR) and multi-view videos, is growing in popularity. Its wireless streaming is an instance of general multicast, extending conventional unicast and multicast, whose effective design is still open. This paper investigates general rate splitting for general multicast. Specifically, we consider a multi-carrier single-cell wireless network where a multi-antenna base station (BS) communicates to multiple single-antenna users via general multicast. We consider linear beamforming at the BS and joint decoding at each user in the slow fading and fast fading scenarios. In the slow fading scenario, we consider the maximization of the weighted sum average rate, which is a challenging nonconvex stochastic problem with numerous variables. To reduce computational complexity, we decouple the original nonconvex stochastic problem into multiple nonconvex deterministic problems, one for each system channel state. Then, we propose an iterative algorithm for each deterministic problem to obtain a Karush-Kuhn-Tucker (KKT) point using the concave-convex procedure (CCCP). In the fast fading scenario, we consider the maximization of the weighted sum ergodic rate. This problem is more challenging than the one for the slow fading scenario, as it is not separable. First, we propose a stochastic iterative algorithm to obtain a KKT point using stochastic successive convex approximation (SSCA) and the exact penalty method. Then, we propose two low-complexity iterative algorithms to obtain feasible points with promising performance for two cases of channel distributions using approximation and CCCP. The proposed optimization framework generalizes the existing ones for rate splitting for various types of services. Finally, we numerically show substantial gains of the proposed solutions over existing schemes in both scenarios and reveal the design insights of general rate splitting for general multicast.

$Q$-Learning Aided Intelligent Routing With Maximum Utility in Cognitive UAV Swarm for Emergency Communications

This article studies the routing problem in a cognitive unmanned aerial vehicle (UAV) swarm (CU-SWARM), which employs the cognitive radio into a swarm of UAVs within a three-layer hierarchical aerial-ground integrated network architecture for emergency communications. In particular, the flexibly converged architecture utilizes a UAV swarm and a high-altitude platform to support aerial sensing and access, respectively, over the disaster-affected areas. We develop a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning framework to achieve the intelligent routing to maximize the utility for CU-SWARM. To characterize the reward function, we take into account both the routing metric design and the candidate UAV selection optimization. The routing metric jointly captures the achievable rate and the residual energy of UAV. Besides, under the location, arc, and direction constraints, the circular sector is modeled by properly choosing the central angle and the acceptable signal-to-noise ratio for UAV to optimize the candidate UAV selection. With this setup, we further propose a low-complexity iterative algorithm using the dynamic learning rate to update <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -values during the training process for achieving a fast convergence speed. Simulation results are provided to assess the potential of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning framework of intelligent routing as well as to verify our overall iterative algorithm via the dynamic learning rate for training procedure. Our findings reveal that the proposed algorithm converges in a few number of iterations. Furthermore, the proposed algorithm can increase the accumulated rewards, and achieve significant performance gains, as compared to the benchmark schemes.

Edge-Assisted Multi-Layer Offloading Optimization of LEO Satellite-Terrestrial Integrated Networks

Sixth-Generation (6G) technologies will revolutionize the wireless ecosystem by enabling the delivery of futuristic services through satellite-terrestrial integrated networks (STINs). As the number of subscribers connected to STINs increases, it becomes necessary to investigate whether the edge computing paradigm may be applied to low Earth orbit satellite (LEOS) networks for supporting computation-intensive and delay-sensitive services for anyone, anywhere, and at any time. Inspired by this research dilemma, we investigate a LEOS edge-assisted multi-layer multi-access edge computing (MEC) system. In this system, the MEC philosophy will be extended to LEOS, for defining the LEOS edge, in order to enhance the coverage of the multi-layer MEC system and address the users’ computing problems both in congested and isolated areas. We then design its operating offloading framework and explore its feasible implementation methodologies. In this context, we formulate a joint optimization problem for the associated communication and computation resource allocation for minimizing the overall energy dissipation of our LEOS edge-assisted multi-layer MEC system while maintaining a low computing latency. To solve the optimization problem effectively, we adopt the classic alternating optimization (AO) method for decomposing the original problem and then solve each sub-problem using low-complexity iterative algorithms. Finally, our numerical results show that the offloading scheme conceived achieves low computing latency and energy dissipation compared to the state-of-the-art solutions, a single layer MEC supported by LEOS or base stations (BS).

Short-Packet Communications in Multihop Networks With WET: Performance Analysis and Deep Learning-Aided Optimization

In this paper, we study short-packet communications in multi-hop networks with wireless energy transfer, where relay nodes harvest energy from power beacons to transmit short packets to multiple destinations. It is proposed a novel cooperative beamforming relay selection (CRS) scheme which incorporates partial relay selection and distributed multiuser beamforming to achieve a high-reliable transmission in two consecutive hops. A closed-form expression for the average block error rate (BLER) of the CRS scheme is derived, based on which an asymptotic analysis is also carried out. To achieve optimal channel uses allocation, we formulate a fairness end-to-end throughput maximization problem which is generally NP-hard due to the non-concavity of the objective function and mixed-integer constraints. To solve this challenging problem efficiently, we first relax channel uses to be continuous and transform the relaxed problem into an equivalent non-convex one, but with a more tractable form. We then develop a low-complexity iterative algorithm relying on inner approximation framework to convexify non-convex parts that converges to at least a locally optimal solution. Towards real-time settings, we design an efficient deep convolutional neural network (CNN) with multiscale-accumulation connections to achieve the sub-optimal solution of the relaxed problem via real-time inference processes. Numerical results are presented to verify the analytical derivations and to demonstrate performance improvements of the CRS scheme over the benchmark ones in terms of BLER, reliability, latency, and throughput in various settings. Moreover, the designed CNN provides the lowest root-mean-square error compared to the state-of-the-art deep learning approaches while the CNN-aided optimization framework estimates accurately the optimal channel uses allocation with low execution time.

Network-coded uplink clustered NOMA relay networks: Models and performance comparisons

In this paper, network sum-rate maximization (NSR-MAX) for network-coded (NC) uplink clustered non-orthogonal multiple-access (NOMA) relay networks is considered. In particular, the goal is to maximize network sum-rate via optimal power allocation, where the user clusters communicate with the base-station over an amplify-and-forward relay, subject to quality-of-service (QoS) constraints. Two NC-NOMA transmission models – namely NC-NOMA-I and NC-NOMA-II – are proposed, where in the first model, network-coding is applied at the relay only, while the second model applies network-coding for user clusters’ transmissions and at the relay to further minimize the transmission delay. The formulated NSR-MAX problem happens to be non-convex, and hence, is computationally-intensive. Thus, a low-complexity iterative two-layer algorithm (ITLA) is devised, which decouples the formulated problem and efficiently solves its over two-layers. Specifically, the inner-layer solves the NSR-MAX problem to obtain the optimal relay transmit power, while the outer-layer determines the optimal users’ transmit powers. Numerical results are presented, which illustrate that the proposed ITLA yields near-optimal solutions for all transmission models, in comparison to the formulated NSR-MAX problem (solved via global optimization package) as well as outperforming its OMA-based counterparts. Not only that, but the NC-NOMA-II is shown to be superior to the other NOMA and OMA schemes, while demonstrating resilience to timing offsets as well as successive interference cancellation (SIC) and channel state information (CSI) errors.

A Rateless 16QAM Scheme for IoT Uplink Communications

How to achieve a high transmission rate at a low cost of energy consumption in a wide range of channel variations is a critical challenge in Internet of Things (IoT) applications. In this article, a rateless 16 quadrature amplitude modulation (R-16QAM) scheme for IoT uplink communications is proposed to deal with this problem. The R-16QAM transmitter consists of an encoder, a random bit selector, and a tailored modulation mapper. Different from the sequential bits selection and Gary mapping in conventional 16QAM modulation, bits are randomly selected and mapped into a 16QAM symbol by a weighted summation of selected bits in R-16QAM. The random bits selection introduces correlation among modulated symbols, enabling the transmitter to work in a rateless manner, i.e., the transmitter keeps generating and sending R-16QAM symbols until a feedback of successful decoding is received from the receiver. It means that the number of symbols transmitted varies with channel variations. In this way, the proposed R-16QAM can automatically adjust to channel conditions and work well in a wide range of channel conditions. The weighted-summation mapping can be illustrated by a “Tanner graph,” based on which we develop a low complexity iterative demodulation algorithm to improve receiver performance. The simulations show that compared with narrow band Internet of Things (NB-IoT) standard and Turbo coded light-weight rate compatible modulation (TLRCM) for IoT uplink communications, the proposed R-16QAM scheme can consistently achieve a lower energy consumption in a signal-to-noise-ratio (SNR) range of −10–15 dB. Moreover, it can achieve a maximum transmission throughput of 3.96 bits/s/Hz at SNR=15 dB while only 0.66 and 1.7 bits/s/Hz are achieved in NB-IoT and TLRCM, respectively. These advantages enable the proposed R-16QAM scheme to be particularly suitable to IoT application scenarios where nodes work with limit energy resources under a large range of channel variations.

Age-of-Information Minimization in Healthcare IoT Using Distributionally Robust Optimization

In this article, we consider a cellular-based healthcare Internet of Things (IoT) system with imperfect channel state information (CSI), where a healthcare IoT device first receives radio frequency (RF) energy from the small cell base station (SBS) and then transmits physiological status updates to the corresponding SBS as timely as possible. A newly proposed metric, named Age of Information (AoI), will be introduced to characterize the data freshness, which is determined by the status updates generation probability and the information transmission outage probability. To minimize the average AoI, we formulate a distributionally robust optimization problem under an energy harvesting probability (chance) constraint and an information transmission probability constraint. Since the distributionally robust probability constraints are nonconvex, we use the conditional value-at-risk (CVaR)-based method to express constraint specifications related to distributional ambiguity. To tackle the NP-hard problem efficiently, we decompose the AoI minimization problem into two subproblems and propose a low-complexity iterative algorithm to obtain a suboptimal solution. Simulation results show that there exists an AoI-energy tradeoff in the considered healthcare IoT, and the CVaR-based method can achieve a better performance than the nonrobust method.

A penalized inequality-constrained approach for robust beamforming with DoF limitation

A well-known challenge in beamforming is how to optimally utilize the degrees of freedom (DoF) of the array to design a robust beamformer, especially when the array DoF is limited. In this paper, we leverage the tool of constrained convex optimization and propose a penalized inequality-constrained minimum variance (P-ICMV) beamformer to address this challenge. Specifically, a well-targeted objective function and inequality constraints are proposed to achieve the design goals. By penalizing the maximum gain of the beamformer at any interfering directions, the total interference power can be efficiently mitigated with limited DoF. Multiple robust constraints on the target protection and interference suppression can be introduced to increase the robustness of the beamformer against steering vector mismatch. By integrating the noise reduction, interference suppression, and target protection, the proposed formulation can efficiently obtain a robust beamformer design while optimally trading off various design goals. To numerically solve this problem, we formulate the P-ICMV beamformer design as a convex second-order cone program (SOCP) and propose a low complexity iterative algorithm based on the alternating direction method of multipliers (ADMM). Three applications are simulated to demonstrate the effectiveness of the proposed beamformer.

Joint Optimization of Caching Placement and Trajectory for UAV-D2D Networks

With the exponential growth of data traffic in wireless networks, edge caching has been regarded as a promising solution to offload data traffic and alleviate backhaul congestion, where the contents can be cached by an unmanned aerial vehicle (UAV) and user terminal (UT) with local data storage. In this article, a cooperative caching architecture of UAV and UTs with scalable video coding (SVC) is proposed, which provides the high transmission rate content delivery and personalized video viewing qualities in hotspot areas. In the proposed cache-enabling UAV-D2D networks, we formulate a joint optimization problem of UT caching placement, UAV trajectory, and UAV caching placement to maximize the cache utility. To solve this challenging mixed integer nonlinear programming problem, the optimization problem is decomposed into three sub-problems. Specifically, we obtain UT caching placement by a many-to-many swap matching algorithm, then obtain the UAV trajectory and UAV caching placement by approximate convex optimization and dynamic programming, respectively. Finally, we propose a low complexity iterative algorithm for the formulated optimization problem to improve the system capacity, fully utilize the cache space resource, and provide diverse delivery qualities for video traffic. Simulation results reveal that: i) the proposed cooperative caching architecture of UAV and UTs obtains larger cache utility than the cache-enabling UAV networks with same data storage capacity and radio resource; ii) compared with the benchmark algorithms, the proposed algorithm improves cache utility and reduces backhaul offloading ratio effectively.

Joint Optimization Framework for Minimization of Device Energy Consumption in Transmission Rate Constrained UAV-Assisted IoT Network

Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be an alternative option for a scenario where Internet of Things (IoT) devices consume high energy to achieve the required data rate when they are far away from the terrestrial base station (BS). Therefore, this article has proposed an energy-efficient UAV-assisted IoT network where a low-altitude quad-rotor UAV provides mobile data collection service from static IoT devices. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, devices association, and respectively, transmit power allocation at every time slot while ensuring that every device should achieve a given data rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt a reinforcement learning (RL)-based solution methodology that effectively decouples it into three individual optimization subproblems. The formulated optimization problem has transformed into a Markov decision process (MDP) where the UAV learns its trajectory according to its current state and corresponding action for maximizing the generated reward under the current policy. Finally, we conceive state–action–reward–state–action, a low complexity iterative algorithm for updating the current policy of UAV, that achieves an excellent computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on optimal UAV trajectory. The proposed methodology reduces the total energy consumption of all devices by 6.91%, 8.48%, and 9.94% in 80, 100, and 120 available time slots of UAV, respectively, compared to the particle swarm optimization (PSO) algorithm.