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.

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 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 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.

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.

Abstract Pattern substitution (PS) method (Ho et al., Comput. Stand. Interfaces 31:787–794, 2009) is a recent reversible data hiding method for binary images. It generates one pattern pair, the patterns in which are called PM and PF, and substitutes between them to embed one bit. Two types of PS have been proposed: non-overlapping PS and overlapping PS. However, Dong et al. (ETRI J. 37:990–1000, 2015) states that the overlapping PS sometimes cannot decode correctly, which is called as miscoding problem. Moreover, to the PS-based methods (Ho et al., Comput. Stand. Interfaces 31:787–794, 2009; Dong et al., ETRI J. 37:990–1000, 2015; Dong et al., Lect. Notes Comput. Sci. 6526:3516–3522, 2011), although chosen of two patterns to form the pattern pair which relates the embedding performance much, the previous methods only use a fixed pattern pair during the whole data hiding process. In order to overcome these two shortcomings, this paper proposes an adaptive overlapping PS method. Firstly, two modes of the embedding are proposed so that the miscoding problem can be solved. Secondly, one window which contains the context of current processing pixel is generated and updated when the process proceeds to the next pixel. Thus, the optimum pair of the local context for embedding can be chosen. Thirdly, in order to get a smaller location map, a three-round embedding mechanism is also proposed. With these three improvements, our method can embed larger payloads with less distortion than the previous works.

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 energy efficiency of collecting sparse data in wireless sensor networks using compressive sensing (CS). We use a sparse random matrix as the sensing matrix, which we call Sparse Random Sampling (SRS). In SRS, only a randomly selected subset of nodes, called the source nodes, are required to report data to the sink. Given the source nodes, we intend to construct a data gathering tree such that (1) it is rooted at the sink and spans every source node and (2) the minimum residual energy of the tree nodes after the data collection is maximized. We first show that this problem is NP-complete and then develop a polynomial time algorithm to approximately solve the problem. We greedily construct a sequence of data gathering trees over multiple rounds and propose a polynomial-time algorithm to collect linearly combined measurements at each round. We show that the proposed algorithm is provably near-optimal. Simulation and experimental results show that the proposed algorithm excels not only in increasing the minimum residual energy, but also in extending the network lifetime.

In this paper, we study a data aggregation problem in wireless sensor networks. We propose a Compressive Sensing (CS) based strategy which is able to reduce energy consumption and data collection latency. We adopt a random sensing matrix with entries drawn i.i.d. according to strictly sub-Gaussian distributions. Such a matrix have property such that a fraction of its entries are equal to zero with high probability. This enables us to collect data from only a fraction of the network without affecting data recovery, which helps reduce communication overheads. Linear networks and planar networks are considered. We compare the energy consumption and latency performance of our strategy with those of Compressive Data Gathering (CDG) scheme. Analytical and simulation results show that our scheme can reduce up to 44% and 67% of the energy consumption for linear and planar networks respectively, when the number of nodes is large. A significant improvement on the latency performance is observed as well.

In this letter, we study signal reconstruction from compressed sensing measurements. We propose new sufficient conditions for stable recovery when partial support information is available. Weighted$\ell_{1}$-minimization is adopted to recover the original signal under three noise models. The proposed approach is to use Ozeki's inequality and shifting inequality in order to bound the errors in the associated weighted$\ell_{1}$-minimization. Our result offers generalized performance bounds on recovery capturing known support information. Improved sufficient conditions for recovery are derived based on our results, even for the cases where the accuracy of prior support information is arbitrarily low.