In this article, a modified mutual information maximization (InfoMax) framework, named channel capacity maximization (CapMax), is proposed and applied to learn informative representations for dynamic networks with time-varying topology and/or time-evolving node attributes. The CapMax is based on the network information theory for multiuser communication, where the representation model is treated as a multiaccess communication channel with memory and feedback. Without requirements of the backbone structure, the learning objective of our CapMax is maximizing the channel capacity, which is measured by directed information (DI) rather than mutual information. For efficient implementation, we design an estimator of the channel capacity through the combination of graph neural networks (GNNs) and recurrent neural networks (RNNs). Under some mild conditions, we theoretically prove that DI is a better measure than mutual information in capturing useful information. The experiments are conducted on multiple real-world dynamic network datasets, and the outperformance of our CapMax on different backbone models on link detection and prediction validates the effectiveness of modeling the representation model as a communication channel.

An improved CA-SCL (CRC-Aided Successive Cancellation List) decoding algorithm for Polar codes is proposed in this paper. By introducing the global cyclic redundancy check and local parity check, a lower bit error rate is obtained comparing with the known results. The new algorithm constructs a prune decoding path by the parity check in the local processing. Since the parity check can only detect an odd number of errors, the global CRC is used to increase the error detection ability and the decoding accuracy. The performance of the improved decoding algorithm is better than the known results, especially when the code length is either less than or equal to 512, and the code rate is not greater than 0.5 in case of the signal-to-noise ratio (SNR) higher than 3dB. In particular, when the code length is 256 with code rate of 0.5 and decoding list size of 32 under the condition of bit error rate 10-5on the AWGN channel, the proposed algorithm has around 0.25dB and 0.55dB performance gain comparing with that of the CA-SCL and SCL algorithms, respectively.

Consider the computations at a node in a message passing algorithm. Assume that the node has incoming and outgoing messages$\mathbf {x} = (x_{1}, x_{2}, \ldots, x_{n})$and$\mathbf {y} = (y_{1}, y_{2}, \ldots, y_{n})$, respectively. In this paper, we investigate a class of structures that can be adopted by the node for computing$\mathbf {y}$from$\mathbf {x}$, where each$y_{j}, j = 1, 2, \ldots, n$is computed via a binary tree with leaves$\mathbf {x}$excluding$x_{j}$. We make three main contributions regarding this class of structures. First, we prove that the minimum complexity of such a structure is$3n - 6$, and if a structure has such complexity, its minimum latency is$\delta + \lceil \log (n-2^{\delta }) \rceil $with$\delta = \lfloor \log (n/2) \rfloor $, where the logarithm always takes base two. Second, we prove that the minimum latency of such a structure is$\lceil \log (n-1) \rceil $, and if a structure has such latency, its minimum complexity is$n \log (n-1)$when$n-1$is a power of two. Third, given$(n, \tau)$with$\tau \geq \lceil \log (n-1) \rceil $, we propose a construction for a structure which we conjecture to have the minimum complexity among structures with latencies at most$\tau $. Our construction method runs in$O(n^{3} \log ^{2}(n))$time, and the obtained structure has complexity at most (generally much smaller than)$n \lceil \log (n) \rceil - 2$.

While packet loss rate is a crucial indicator of network reliability, abnormal nodes dropping the transit traffic lead to higher packet loss rates. One significant challenge is to efficiently distinguish the abnormal nodes from those with normal packet losses. To address this issue, this paper presents a new detection scheme based on the Likelihood Ratio Test (LRT) for cluster-tree networks - a typical architecture of wireless networks. Due to the hierarchical structure of the networks, LRT is implemented in two phases, i.e., the local detection phase at the non-root nodes and the overall detection phase at the root node. The observed forwarding behaviors of the monitored node are modeled and quantified as log-likelihood ratios where the packet loss rate in the alternative hypothesis is a default value. In this way, the log-likelihood ratios are compared with a threshold under the maximum a posteriori probability criterion to identify the anomalies. The correctness of the proposed scheme is theoretically proved if the packet loss rate in the abnormal node exceeds a critical detection point - whose expression is also formally derived. Simulation results validate the critical detection point and demonstrate the superiority of the proposed detection scheme compared to the state-of-the-art methods.

Accurate origin-destination traffic prediction (ODTP) is a persistent problem in network management. Due to the structural nature of networks, the spatial correlations are critical for effective perdition. In this paper, the ODTP problem is built onto the graph domain by mapping OD traffic into graph-structured data involving the topology information. Moreover, due to the flow characteristics of OD traffic, the complex spatial-temporal (ST) correlations should not be limited at one-hop neighbors or consecutive time steps. To benefit from this observation, we propose a novel graph learning based method, called multi-scale spatial-temporal graph neural network (MSTNN). In MSTNN, spatial and temporal extractors are designed to capture multi-scale spatial and temporal correlations. Specifically, the spatial extractor employs the graph attention mechanism to capture the time-varying spatial correlations of nodes and their multi-hop neighbors. In the temporal extractor, we design gated dilated convolution layers with different dilation factors, each of which represents a different granularity of time, to exploit the multi-scale temporal correlations in both adjacent and non-adjacent time steps. After cascading the spatial and temporal extractors, the multi-scale ST correlations are weighted fused. Simulations on two real-world datasets show that MSTNN outperforms existing approaches that work well in various prediction tasks.

In literatures, there are various constructions of frequency hopping sequence (FHS for short) sets with good Hamming correlations. Some papers employed only multiplicative groups of finite fields to construct FHS sets, while other papers implicitly used only additive groups of finite fields for construction of FHS sets. In this paper, we make use of both multiplicative and additive groups of finite fields simultaneously to present a construction of optimal FHS sets. The construction provides a new family of optimal (qm- 1, qm-t-1/r , rqt; qm-t-1/r + 1) frequency hopping sequence sets archiving the Peng-Fan bound. Thus, some FHS sets constructed in literatures using either multiplicative groups or additive groups of finite fields are included in our family. In addition, some other FHS sets can be obtained via the well-known recursive construction through one-coincidence sequence set.

In frequency-hopping sequence (FHS) design, the average Hamming correlation is an important performance indicator. In this paper, the average Hamming correlation of low-hit-zone (LHZ) FHS sets, which has not yet been reported previously, is studied. A lower bound on the average Hamming auto-correlation and the average Hamming cross-correlation of LHZ FHS sets is firstly derived. The new lower bound includes the bound for conventional FHS sets derived by Peng et al. as special case. Then a sufficient and necessary condition for an LHZ FHS set with optimal average Hamming correlation is given. Further, a construction of LHZ FHS sets with optimal average Hamming correlation is presented, whose parameters meet the lower bound on the average Hamming correlation of LHZ FHS sets.

In this paper, we consider safety message transmission in a dense vehicular network. With increasing vehicular network density, the collision rate increases when multiple vehicles transmit safety messages simultaneously. To address this issue, we propose a request-transmission split time division multiple access (TDMA) scheme, referred to as RTS-TDMA. In our scheme, we divide a frame into three phases, i.e., a contention access phase, a broadcast feedback phase, and a contention-free transmission phase. Each vehicle selects a repetition rate according to a given probability distribution and repeats the transmission of its request packet to improve the reliability of the request. In addition, a roadside unit acts as the coordinator and uses a successive interference cancellation technique to resolve request collisions. RTS-TDMA also reduces the request time percentage by containing only the vehicle identity in each request packet. Both theoretical analysis and numerical results verify that the RTS-TDMA scheme can provide higher throughput than the coded slotted ALOHA scheme.

This paper is concerned with a machine learning approach to cancel the interference for cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model, where the C-IoT system is noncooperative and has very limited knowledge on the interference. Our transceiver design uses an iterative processing structure, which consists of a linear estimator, a demodulation-and-decoding module, and a clustering module. In the clustering module, we employ modified expectation-maximization (EM)-based algorithms to estimate the interference under the knowledge of the modulation constraint (MC) of the interference. We show that this modified EM algorithm-based receiver outperforms the original EM-based receiver, since the former is able to generate a more accurate clustering result by reducing the dimension of the parameter space. We further improve the performance of the iterative receiver by introducing the extrinsic information technique, with the resulting algorithm referred to as the extrinsic modulation constrained EM (Ext-MC-EM) algorithm. We show that the Ext-MC-EM algorithm-based receiver considerably outperforms the counterpart iterative receivers, including the MC-EM algorithm.

In order to evaluate the goodness of frequency hopping (FH) sequence design, the periodic Hamming correlation function is used as an important measure. Usually, the length of correlation window is shorter than the period of the chosen FH sequence, so the study of the partial Hamming correlation of FH sequence is particularly important. If an FH sequence or an FH sequence set has an optimal partial Hamming correlation with respect to the partial Hamming correlation bound for all length of correlation window, then the FH sequence or the FH sequence set is said to be strictly optimal. In this paper, we first prove that there is no strictly optimal FH sequence set of family size M and sequence length N over a frequency slot set of size q with respect to the partial Hamming correlation bound derived by Niu et al. when N > q2/M and q ≥ 2, and that by Cai et al. when N > q2/M and q ≥ 2N/(N - 2). Furthermore, we define a special partition-type difference packing (DP) called [N, ∇, Hal] PDP and give several classes of [N, ∇, Hal] PDPs. Then, we present a new construction of strictly optimal FH sequences. By choosing different PDPs, the FH sequences constructed can give new and flexible parameters. By utilizing this construction method recursively, we can obtain new [N, ∇, Hal] PDPs, which lead to infinitely many classes of strictly optimal FH sequences with new parameters. Moreover, based upon an [N, ∇, Hal] PDP, we present a construction of strictly optimal FH sequence sets. By preceding construction method and recursive construction, we can also obtain infinite classes of strictly optimal FH sequence sets which can give new and flexible parameters.

The progressive edge-growth (PEG) algorithm constructs an edge in each stage to maximize the variable node (VN) of interest's local girth in real time. Thus, this VN's local girths, after more than one edge is added to the current tanner graph (TG) setting, may not be maximized relative to that TG setting. To address this problem, we define the multi-edge local girth and edge-trial, and based on these definitions, propose a new multi-edge metric-constrained PEG algorithm (MM-PEGA) to improve the design at each VN. The MM-PEGA constructs an edge in each stage that, relative to the current TG setting, can potentially maximize the VN of interest's local girth after a certain number (up to the edge-trial) of edges are added to the TG setting. We first analyze the properties of the multi-edge local girth, and then propose an algorithm for calculating the multi-edge local girth. We also propose a method for accelerating the MM-PEGA. Moreover, we generalize the MM-PEGA for improving different PEG-like designs. According to the theoretical analysis, increasing the edge-trial of the MM-PEGA is expected to positively affect the cycle-structure and the error performance of resulting low-density parity-check (LDPC) code. This expectation is verified by simulations.

In this paper, we propose a new multi-edge metric-constrained quasi-cyclic progressive edge-growth algorithm (MM-QC-PEGA), which is suitable for constructing both single- and multi-weighted (binary) QC low-density parity-check (LDPC) codes with arbitrary lengths, rates, circulant sizes, and variable node (VN)-degree distributions. The MM-QC-PEGA is able to detect all cyclic-edge-set-minimum-virtual cycles (CMVCs), as it accurately computes the metric value of each CMVC with an a posteriori test. In addition, we propose a new greatest-common-divisor (GCD)-approximation for time efficiently approximating the metric value of a CMVC without a posteriori tests, and propose a new GCD-approximated MM-QC-PEGA (G-MM-QC-PEGA) by using the GCD-approximation to replace the a posteriori test involved in the MM-QC-PEGA. As a result, the G-MM-QC-PEGA is faster than the MM-QC-PEGA, and the CMVCs undetectable to the G-MM-QC-PEGA under multi- and single-weighted QC-LDPC code graphs have minimum lengths of eight and ten, respectively. Moreover, we propose a new masking technique that is efficient in masking the parity-check matrix consisting of an array of arbitrary circulants of the same size. Compared with the several existing works, our proposed algorithms could perform better or comparably in terms of avoiding generating small cycles and error performances. Our proposed algorithms somewhat perfect the works of QC-LDPC code construction.

We consider low-density parity-check (LDPC) code design by considering the unequal error protection property in high order modulated bit-interleaved coded modulation (BICM) schemes. The existing work mainly considered the effect of variable node edge assignments on the decoding performance. In this paper, we consider both variable node and check node edge assignments to further optimize the LDPC codes for BICM schemes. To achieve this, we derive new extrinsic information transfer (EXIT) functions for both regular and irregular LDPC code ensembles. Then we employ differential evolution to optimize the code ensembles in terms of the lowest decoding threshold. Finally, we propose a modified progressive edge growth algorithm to design regular and irregular LDPC codes based on the optimized code ensembles. The numerical results show that our designed LDPC codes have better bit error rate performance compared to the codes designed in the existing work.

We propose a new low-density parity-check (LDPC) coded binary physical-layer network coding (PNC) scheme for Gaussian two-way relay channels. In this scheme, we introduce a bit mapper between the LDPC encoder and the modulator, which considers the unequal error protections brought by the high order PSK modulations. We add a new bipartite sub-channel graph consisting of sub-channels and variable nodes (VNs) to the Tanner graph and propose a progressive edge growth (PEG) algorithm to design the bit mapper. The design paradigm is to search for the bit mapping distribution with the lowest decoding threshold by using the extrinsic information transfer (EXIT) chart, and then establish the edges progressively between VNs and sub-channels according to the distribution. The proposed PEG algorithm is employed to design the bit mappers of the schemes with 8-PSK, 16-PSK, 64-PSK and 256-PSK. Simulation results show that the proposed schemes can considerably improve the bit error performance of the PNC XOR messages, compared to the schemes without bit mappers.

In this paper, we consider the design of the bit mapping in low-density parity-check (LDPC) coded bit-interleaved coded modulation (BICM) schemes. We introduce a two-layer bipartite graph to represent the LDPC coded BICM scheme where a new bit mapping graph linking sub-channels to variable nodes (VNs) is added to the conventional Tanner graph. We propose a progressive edge growth (PEG) algorithm to design the bit mapping for the BICM scheme. The design paradigm is to provide more protections to the VNs that are allocated to the sub-channels with the lowest mutual information. We define a novel concept of unreliable depth profile to classify the reliability of the VNs. By connecting more reliable edges to the least reliable VNs, we can significantly improve the reliable edge distribution for the unreliable VNs, thus improve the extrinsic information in the iterative decoding. The proposed bit mapping algorithm is employed for the design of the LDPC coded BICM with 64-QAM and 256-QAM. Simulation results show that the proposed design can considerably improve the error performance, compared to the conventional consecutive bit mapping strategy.

Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.

To improve the performance further for the bit- interleaved coded modulation with iterative decoding (BICM-ID) system, a modified extrinsic information transfer (EXIT) model is presented, in which the decoding and the demodulation are modeled as two iterative processing units. By using the binary erasure channel (BEC) approximation for the a priori AWGN channel, the analytical formulation of mutual information for sub-channels in M-QAM are obtained. Besides, the grid depth-first searching algorithm (GDFSA) is modified for designing the optimal bit mapping distribution with minimum convergence rate. Using the DVB-S2 rate-0.5 64800-bit low-density parity-check (LDPC) code as demonstration, the simulations show that the BER performances achieve 0.225/0.650dB gains at 10-5for 16-QAM/32-QAM under AWGN channel, compared to the consecutive bit mapping.

This paper proposes two new cycle-optimized algorithms, called the multi-step progressive edge-growth (PEG) algorithm and the multi-step approximate cycle extrinsic message degree (ACE) constrained PEG algorithm, to design an LDPC code with larger girth and better cycle-connectivity. In addition, an efficient method is developed to calculate the distances between the variable nodes (VNs) and the check nodes (CNs) in the proposed algorithms. Simulation results show that compared to the conventional cycle-optimized algorithms, the proposed algorithms can achieve both better cycle-connectivity and foreseeable lower error floors.

In this paper, we propose an encoded packet-assisted retransmission, namely EAR, approach that use opportunistic network coding (ONC) to improve the bandwidth efficiency for unicast sections in wireless networks. In existing ONC schemes, network coding opportunity are not always fully exploited due to the lack of understanding of the use of the encoded packets receivers cannot decode. To overcome this limitation, EAR develops an improved opportunistic listening mechanism that allows receivers to store all heard undecodable ONC packets and to periodically report the reception status to the sender. The sender then can directly XOR these encoded packets with other packets to exploit the potential coding opportunities of them. To further maximize the utilization of coding opportunity in the network, EAR introduces a greedy coding algorithm that constructs a simple scheduler. The scheduler prioritizes packets according to their contribution to the overall transmission performance. Simulation results show that, EAR can significantly reduce the total number of retransmissions and provide more efficient bandwidth utilization, compared with existing schemes.