Federated learning (FL) in resource-constrained wireless networks faces the challenge of long training delays. In this work, we explore delay-efficient FL by leveraging device-to-device (D2D) communications to accelerate the uploading of local models. We formulate a joint problem of upload mode selection and bandwidth allocation, which is a mixed-integer nonlinear programming (MINLP) problem and difficult to solve directly. To address this, we propose a low-complexity two-step algorithm: the first step determines the upload modes for edge devices, while the second step optimally allocates the bandwidth. Simulation results show that our algorithm outperforms baseline schemes, with delay reduction becoming more pronounced as the number of edge devices increases.

The computing power network (CPN) offers exceptional computational capabilities and reliable network services, with significant potential for future applications. To achieve ubiquitous coverage and efficient computational resource allocation, CPN can be seamlessly coordinated with low-cost autonomous aerial vehicle (AAV)-based mobile computing platforms. This article investigates an efficient low-altitude AAV-assisted computing power and resource allocation mechanism tailored for urban environments. The aim is to ensure seamless scheduling and efficient processing of computational tasks across various computing devices at different layers of the CPN, while minimizing AAV energy consumption and ensuring flight safety. First, this article proposes an Urban AAV-assisted CPN task-allocation and AAV trajectory-management decision-making problem. The AAV works until it safely lands, aiming to minimize overall task processing delay and AAV energy consumption while ensuring fairness in task allocation. Then, a novel AAV-protection-based multiagent deep deterministic policy gradient (UP-MADDPG) algorithm is introduced. It offers dynamic management of secure computing and communication flight paths when facing building blockages. Finally, we compared the proposed algorithm with baseline algorithms across various metrics. Experimental results demonstrate that the proposed algorithm achieves lower and more balanced task execution delay and AAV energy consumption while also improving fairness.

We consider the problem of reducing the learning latency of layerwise federated learning through joint device selection and bandwidth allocation. Specifically, we examine practical scenarios with heterogeneous devices with varying system parameters (e.g., CPU frequency, transmit power, etc.) and energy budgets. We formulate a long-term optimization problem, which is difficult to solve even with perfect channel state information. To address the issue, we employ Lyapunov theory to transform the problem into a series of online optimization problems, each of which can be efficiently solved using an alternating optimization-based method. Simulation results show that our scheduling scheme surpasses baseline schemes not only in terms of reducing the learning latency but also in reducing the energy deficit.

We address the minimum-delay beam scheduling problem leveraging reflections for switched beamforming systems. The objective is to efficiently disseminate a data file from a transmitter to a set of nodes via multiple predetermined beams with arbitrary overlapping patterns. The problem is formulated as a challenging mixed integer nonlinear programming (MINLP) and then decomposed into a set of subproblems. The subproblems are still difficult to solve due to their NP-hardness. We propose a heuristic algorithm for the subproblems, based on which two heuristic algorithms with different computational complexities are developed for the original problem. Simulation results high-light the significant reduction in dissemination delay achieved by the proposed algorithms compared to baseline approaches without leveraging reflections.

We investigate the minimum-delay multicast scheduling problem for millimeter wave (mmWave) networks. Salient characteristics of mmWave links, directionality and reflections, are considered under sectored antenna model. We first consider the model where the signal is received at a single Direction-of-Arrival (DoA) with the highest SNR at each node. We identify the property such that the optimal policy can be recursively partitioned into smaller sizes and propose an iterative method based on graphs which finds the optimal schedule in polynomial time. Next, we extend our model where a node leverages signals received at multiple DoAs through reflections. We introduce the concept of receiving direction diversity (RDD) which states that the availability of multiple receiving directions enables opportunistic reduction of multicast delay. We prove NP-hardness of the problem, and propose approximations with performance bounds and heuristics of reduced complexity. Next, we consider multicast scheduling with rateless codes (RCs) which reduces delay by flexible packet reception. For both cases of coded multicast with and without RDD, we formulate linear programming problems and propose greedy algorithms with nearly optimal performance and reduced complexity. By simulation we show the outperformance of our method over conventional ones, and numerically characterize the gain of RDD and RCs.

We jointly consider full-duplex operation and network coding in two-hop relay networks to enhance the throughput of the block transmission of packets over erasure channels. Two coded transmission schemes, termed Fewest Broadcast Packet First (FBPF) and Buffer Contents-based Coded Transmission (BCCT), are proposed, where random linear network coding is employed at the Base Station (BS) and the Relay Station (RS), respectively. Both schemes do not rely on users’ Channel State Information (CSI), buffer status, channel parameters, etc., and hence are practically viable. We derive closed-form upper bounds on the throughput of both schemes. We prove that both schemes achieve the optimal throughput when the BS-to-RS channel is perfect. Through extensive simulations, we demonstrate that both schemes incur substantially higher throughput than the traditional uncoded Automatic Repeat-reQuest (ARQ) scheme and perform close to a general upper bound on the system throughput. Furthermore, even with imperfect Self-Interference Cancellation (SIC) at the full-duplex RS, our schemes are shown to be superior to state-of-the-art coded transmission schemes designed for half-duplex relay networks, given that the impact of imperfect SIC on the BS-to-RS channel quality is not high.

We study efficient federated learning (FL) using random pruning in resource-constrained edge intelligence networks. We propose an edge device selection strategy to identify appropriate edge devices for participating in FL at the beginning of each training iteration. We then formulate an optimization problem that jointly optimizes the pruning ratio, CPU frequency, uplink power, and bandwidth allocation for the selected edge devices. Since the optimization problem is non-convex and challenging to solve directly, we decompose it into three subproblems and propose efficient algorithms or closed-form solutions for each subproblem. Based on the solutions to the subproblems, an alternating optimization algorithm is constructed to solve the original problem. Simulation results demonstrate that our scheme outperforms baseline schemes in terms of both learning accuracy and energy consumption.

This article studies joint power, spectrum and computational resource allocation in mobile edge computing (MEC) systems. Considering that the licensed spectrum resources are not sufficient, the computing tasks can also be uploaded to the remote MEC server (MECS) via the unlicensed spectrum. To facilitate fair coexistence with Wi-Fi networks, we adopt the duty-cycle-muting mechanism with adaptive adjustment of the duty cycle on unlicensed channels. We propose a Stackelberg game formulation, where the aim is to minimize the long-term energy consumption of the noncooperative user terminals (UEs) while guaranteeing the stability of task buffers. In the game, the MECS prices the licensed spectrum to indirectly adjust the proportion of bandwidth for each UE. In particular, we develop a distributed resource management algorithm, which enables the UEs to behave independently and adaptively. Theoretical analysis and simulations demonstrate the effectiveness of our proposed algorithm with respect to energy saving under constrained signaling overheads.

In this paper, a novel decentralized mutual learning (DML) network is designed, where each mobile device can share knowledge with its neighbour devices via bidirectional device-to-device (D2D) communication. We subdivide and discuss mutual learning scenarios, and investigate the user association and resource allocation problems for the one-to-many scenario. With constraints on power, bandwidth and communication latency, we formulate a non-convex optimization problem to minimize the average communication energy consumption for sharing new knowledge. On the basis, a two-layer iterative algorithm is proposed, which consists of an outer layer algorithm based on particle swarm optimisation (PSO) for searching a suitable user association strategy and an inner layer algorithm based on sum-of-ratios optimization for achieving a globally optimal allocation of communication resource. Numerical results are presented to verify the fast convergence and the effectiveness of the proposed algorithm in terms of a trade-off between energy consumption and knowledge sharing efficiency.

We consider the minimum-delay multicast scheduling problem for switched beamforming systems. A salient characteristic of mmWave links, reflection, is considered, which enables opportunistic reduction of data dissemination delay. We formulate the problem as a mixed integer nonlinear programming, which is difficult to solve directly. Instead, we decompose the problem into a set of subproblems, by allocating a fixed path to each receiver for data reception. The optimal solution to each subproblem has a contiguous structure, and hence can be computed using a dynamic programming-based approach. We propose an optimal algorithm for the original problem based on the solutions to the subproblems. By simulation we show the outperformance of our algorithm over an optimal multicast scheduling policy without leveraging reflections and a broadcast baseline scheme.

We consider the channel-aware scheduling (CAS) problem for block transmission of packets in two-hop full-duplex relay networks with multiple users. At each time slot, the full-duplex relay station (RS) can fetch a network-coded packet from the macro base station (BS), and schedule a previously received packet for broadcasting to the users over time-varying channels. Our goal is to maximize the broadcast throughput. Since the associated Markov decision programming problem turns out to be intractable as the size of the problem increases, we propose a CAS scheme which is simple to implement and also achieves near-optimal performance. We provide a closed-form expression of the throughput of our scheme when the BS-to-RS channel is perfect, and prove that our scheme is optimal for one-user systems. Finally, numerical results demonstrate that our scheme performs close to an upper bound of the system and outperforms other transmission schemes.

This paper addresses a joint radio and computational resources allocation problem for mobile edge computing (MEC) networks. To alleviate the shortage of licensed spectrum resources, computing tasks can be offloaded to the MEC server through not only the licensed channels but also the unlicensed channels, where the adaptive duty-cycle-muting (DCM) mechanism is employed at the user terminals (UTs) to guarantee the fair coexistence with the WiFi networks. Moreover, Stackelberg game formulation is used to build up a decentralized radio and computational resources allocation framework, where the MEC server is modeled as a leader to set the price of the licensed spectrum, while UTs as followers compete for the radio and computational resources as a non-cooperative game. The objective of each UT is to minimize the long-term energy consumption with the guarantee of task buffer stability. Accordingly, we develop a distributed algorithm to achieve the equilibrium solution for the formulated Stackelberg game. Numerical results are presented to demonstrate that the proposed scheme is effective with respect to the reduction on energy consumption of UTs with limited signaling overheads.

We consider the full-duplex operation with network coding in two-hop relay networks to enhance the throughput for block transmission of packets. We propose a low-complexity transmission scheme, which does not rely on channel state information (CSI), and hence can be easily implemented in practical systems. We derive a closed-form upper bound on the asymptotic throughput of the proposed scheme, and show that the derived upper bound is tighter than a general upper bound on the throughput of any transmission scheme even with perfect CSI. Simulation results show that, the proposed scheme actually performs close to the general upper bound, and in most cases it substantially outperforms the traditional uncoded Automatic Repeat-reQuest scheme which relies heavily on the ACK/NAK feedback for packet retransmission.

The energy efficiency for data collection is one of the most important research topics in wireless sensor networks (WSNs). As a popular data collection scheme, the compressive sensing- (CS-) based data collection schemes own many advantages from the perspectives of energy efficiency and load balance. Compared to the dense sensing matrices, applications of the sparse random matrices are able to further improve the performance of CS-based data collection schemes. In this paper, we proposed a compressive data collection scheme based on random walks, which exploits the compressibility of data vectors in the network. Each measurement was collected along a random walk that is modeled as a Markov chain. The Minimum Expected Cost Data Collection (MECDC) scheme was proposed to iteratively find the optimal transition probability of the Markov chain such that the expected cost of a random walk could be minimized. In the MECDC scheme, a nonuniform sparse random matrix, which is equivalent to the optimal transition probability matrix, was adopted to accurately recover the original data vector by using the nonuniform sparse random projection (NSRP) estimator. Simulation results showed that the proposed scheme was able to reduce the energy consumption and balance the network load.

We consider the multicast scheduling problem in the heterogeneous network using a half-duplex relay station (RS). Our goal is to minimize the delay of transmitting a block of packets to users over time-varying channels using rateless codes. Due to half-duplex operation, at each time slot, the RS can choose to either multicast a packet to the users, or fetch a packet from the macro base station. We formulate a fluid relaxation for the optimal decision problem, and reveal that the optimal policy has a threshold-based structure so as to exploit the opportunism of multicast channel: the RS should multicast only when the channel quality is sufficiently “high”. We propose an online policy based on the relaxation which does not require the knowledge of channel distribution. When the channel distribution is symmetric across users, we provide a closed-form expression of the asymptotic performance of our policy. For two-user systems, we prove that our scheme is asymptotically optimal. When the users' channels are independent, we derive a performance bound based on water-filling rate allocation which approximates the optimal policy well. Simulation results show that our scheme performs close to theoretical bounds, under correlated as well as independent fading channels.

We consider the multicast scheduling problem for the block transmission of packets in a heterogeneous network using a half-duplex relay station (RS). The RS uses random linear coding to efficiently transmit packets over time-varying multicast channels. Our goal is to minimize the average decoding delay. Because of the half-duplex operation, at each time slot, the RS must decide to either: (1) fetch a new packet for encoding from the base station or (2) multicast a coded packet to wireless users. Thus, optimal scheduling hinges on exploiting multicast opportunities while persistently supplying the encoder (at the RS) with new packets. We formulate an associated fluid control problem and show that the optimal policy incorporates opportunism across multicast channels, i.e., the RS performs a multicast transmission only if the collection of channel conditions is favorable; otherwise, it performs a fetch. Based on the fluid policy, we propose an online algorithm. We prove that our algorithm asymptotically incurs no more than 4/3 and 2 times the optimal delay, for two-user and arbitrary number of user system, respectively. Simulation results show that, in fact, our algorithm's performance is very close to theoretical bounds.

We consider the problem of reducing delays in block transmissions of packets over multicast erasure channels in heterogeneous networks using relay stations. The macro base station performs random linear network coding over a block of packets which are relayed to the relay station which broadcasts the packets to the users. We propose a fluid approximation to our problem, and obtain the optimal solution for the fluid model when the users' channels are homogeneous. For the general case we propose an approximate algorithm which is simple to implement. We observe that it is crucial to explore the trade-off between the opportunity in the users' channels and moving packets out of the system. Simulation results show that our scheme achieves a decoding delay which is close to a theoretical lower bound.