The low earth orbit (LEO) satellite communication network has recently been proposed by 3GPP as a new paradigm of infrastructure to enhance the capacity and coverage of existing terrestrial wireless networks. However, the mobility of LEO satellite nodes leads to a dynamic environment, which introduces unique challenges for handover and throughput optimization in multi-user access control for LEO networks. We formulate an optimization problem of joint access control and resource allocation to maximize the long-term system throughput and avoid frequent handovers, which is non-deterministic polynomial-time hard. To overcome this challenge problem, we propose a multi-agent deep reinforcement learning algorithm and design the proximal policy optimization (PPO) network structure with the long short-term memory (LSTM) layers. In our proposed algorithm, the centralized trainer node is responsible for training the parameters of all networks, and then each ground user independently makes its own access decisions based on its local observation. We deploy a policy network on each ground user that is able to intelligently access a proper LEO satellite node to maintain high system throughput and avoid frequent handovers over a long period. The simulation results have demonstrated the effectiveness and superiority of our proposed algorithm compared to benchmark schemes in addressing the access control and resource allocation issue for the multi-user LEO satellite network.

The low earth orbit (LEO) satellite communication network has attracted extensive attention owing to its advantages of seamless coverage and low propagation delays, which provides a promising solution to realize massive access for Internet of Things (IoT) devices. In this paper, we study cooperative grant free random access (GF-RA) in LEO satellite communication systems. Specifically, we investigate the joint activity detection and channel estimation (JADCE) problem for multi-input multi-output (MIMO) based massive connectivity. First, we analyze the channel characteristics, and reveal the low-rank and row-sparsity properties of the channel impulse response (CIR) matrix. Accordingly, we transfer the JADCE problem into a low-rank matrix completion problem and a compressive sensing problem, which are solved by a two-stage algorithm efficiently. In the first stage, we design the principal component analysis with adaptive signal space detection (PCA-AASD) algorithm to perform low-rank matrix completion. In the second stage, we employ a sequential sparse Bayesian learning with multiple measurement vector (MMV) algorithm to perform active terminal detection and channel estimation. Finally, a majority voting scheme is utilized to estimate the active terminals by aggregating the estimation of multiple satellites. Simulation results validate that the proposed method achieves lower activity detection error probability and better channel estimation performance than other baseline methods in the literature.

The emerging network-softwarization technologies, such as software-defined networking and network function virtualization play important roles in 5G communication and future networks. One of the critical challenges of the practical application of the softwarized networks is to appropriately place virtual network functions (VNFs). The underlying resources and traffic requirements are often factored in the previous works of VNF placement. However, VNFs’ dynamic abilities of changing traffic are usually ignored. Resources allocated to VNFs can vary their traffic-change ratios, and the adjustment of resource volumes should be provisioned to support traffic changes. In this work, we pay attention to the joint optimization problem of VNF Placement, CPU Allocation, and flow Routing (VNFPAR) in the scenarios consisting of VNFs that can dynamically change traffic. We employ the logarithmic functions to approximate VNFs’ traffic change relations and formulate VNFPAR as a mixed-integer nonlinear programming (MINLP) problem. We demonstrate that this problem is highly nonconvex and involves highly coupled variables. For small-scale VNFPAR problems, we propose an optimal algorithm based on relaxation and programming to consume the minimum bandwidth resources. Because VNFPAR is NP-hard, to quickly find near-optimal solutions for large-scale VNFPAR problems, we present heuristic algorithms based on multistage greedy and simulated annealing, respectively. Besides, to achieve a tradeoff between solution quality and execution time, we decompose VNFPAR into subproblems and design an alternating optimization-based method. We evaluate our algorithms on real-work topologies and traffic patterns. Extensive simulations show that our proposed heuristic algorithms are convergent, stable, and effective in terms of solving VNFPARs. The proposed algorithms have small optimality gaps within 7.4%–26.3%. Meanwhile, they save 39.3%–48.4% bandwidth resources compared with relevant baseline technologies.

The emergence of Network Function Virtualization (NFV) and Software Defined Network (SDN) has greatly reformed the network. It is important to reduce the queuing delay spent/observed in NFV servers for the placement of Virtual Network Functions (VNFs). In this work, we mainly focus on the placement of VNFs with Poisson Arrived Traffic (VNFPPAT) to tackle the queuing delay problem. Both the Poisson distribution of traffic flows and various resource-sharing VNFs lead to the prolonged queuing delay in NFV servers with limited processing capacities. Considering the end-to-end delay as our optimization objective, we formulate this problem as a 0-1 quadratic fractional programming problem. This formulation is linearized to obtain the optimal solution for small scale networks. After proving VNFPPAT is NP-hard, we propose heuristic algorithms to obtain sub-optimal placement schemes. Through extensive simulations, we have shown that our proposed algorithms outperform the related state-of-the-art Improve Service Chaining Performance (ISCP) by 72% in terms of the end-to-end delay.

As the number of ground users with different priorities continues to grow, user resource requirements will eventually exceed the capacity limit of communications satellites. How to make full use of the flexibility of beam-hopping under the condition of limited resources to serve as many high-weight users and high-priority services as possible, will be an urgent problem to be solved. We first analyzes the technical characteristics of the beam-hopping antenna and clarifies the minimum granularity of the communication resources. A resource optimization model was established by taking the weighted revenue of service users as the objective function and taking into communication capacity constraints. Finally, an improved cuckoo algorithm is proposed for optimization, whose Levy flight is in integer space. Simulation results show that the new method proposed in this paper achieves higher user weight gain than the classic method.

Network Function Virtualization (NFV) and Software Defined Network have revolutionized data networks. For deployment of the VNFs, it is imperative to consider the queuing delay occurring in the NFV servers. The presented work suggests to incorporate the Poisson distribution of packet arrival rate and packet size along with the limited processing capacity of NFV server. The underlying phenomenon is framed as a 0-1 fractional programming problem. More precisely, the underlying problem is framed to 0-1 MILP to get the optimal solution. Since the VNF placement problem is NP-hard, therefore, we propose two heuristic algorithms to get the solution. Extensive simulations demonstrate that our algorithms effectively reduce 72.9% delay compared with the universal algorithm Improve Service Chaining Performance (ISCP).

This article focuses on two core issues of comprehensive evaluation:the index system and evaluation methods. First of all, towards the simulation platform of space information network (SIN) based on the GEO satellite system, according to the existing three-layer comprehensive evaluation index system, and, by introducing the concept of elemental layer, a comprehensive assessment of space information network with four-layer structure indicator system is proposed. Then, in view of the inconsistency caused by the contradictory evaluation in the classical comprehensive evaluation method(AHP), a method of judging matrix correction based on confidence evaluation is proposed. Finally, the strategy is applied to the group master evaluation, and an evaluation example is given. The results show that this strategy can effectively improve the consistency of judgment matrix and improve the credibility of AHP evaluation results.

In this paper, a Low Earth Orbit (LEO) satellite network capacity model is proposed to value the influence of topology and routing strategy on throughput capacity. Under the network capacity balance framework, two topology strategies and two routing algorithms are studied. The analysis solution shows that for LEO constellation, the topology strategy with opposite direction links could increase the quantity of inter satellite link (ISL), reducing the traffic on each link. Central routing algorithm presents superior traffic distribution ability in LEO network than distributed routing strategy. Simulation results prove that central routing algorithm combined with opposite direction ISL topology in LEO satellite network could reduce data packet loss rate, achieve optimization and robustness on network capacity.

To solve intra-cluster and inter-cluster transmission collision problems in vehicular ad-hoc networks, this paper proposes a novel cluster-based collision-free multichannel medium access control (CCFM-MAC) protocol. All nodes are grouped into different clusters based on the link expiration times with their neighbors. Adjacent clusters use different channels to avoid inter-cluster interference. A cluster head (CH) is in charge of assigning time slots to its members according to their state and relative location to achieve packet transmissions without collisions. In addition, a CH allocates time slots to its members based on their traffic demands in order to guarantee the access fairness while improving throughput. Finally, simulation results show that the proposed protocol outperforms EDCA MAC protocol in terms of average throughput, average end-to-end delay and successful transmission probability.

In this letter, we take the initiative in adaptively determining the optimal temporal filter (TF) length for space-time equalization (STE) schemes, where overfitting may occur under too large TF length. We specifically consider the system without employing cyclic-prefix such that the spectral efficiency could be maintained. Based on the delicately derived connection between the mean square error (MSE) of equalized training symbols and unknown data symbols, the MSE of the latter can be predicated and serves as the decision metric for determining the optimal TF length. Numerical results demonstrate that the proposed STE scheme with TF of adaptively optimized length can work properly, even in the presence of cochannel interference.

In order to prolong the lifetime of wireless sensor networks (WSNs), a low-duty-cycle mode is widely used to save the energy for sensor nodes. Under this mode, sensor nodes switch between active and dormant states, which incurs a high latency for traditional routing algorithms. To mitigate this, in this paper, the data collection problem in low-duty-cycle WSNs is formulated as a delay optimization problem of traffic flow with consideration of both congestion and collision, which is solved by a distributed algorithm based on network utility maximization. Our proposed distributed routing algorithm achieves a better tradeoff between latency and energy conservation than existing schemes, and our schemes can find a nearly global-optimal-path to achieve almost minimum average end-to-end (E2E) delay with less energy consumption. The computation complexity and energy consumption of the distributed algorithm are analyzed and evaluated in detail. The simulation results show that the proposed algorithm can achieve almost the same average E2E delay performance as the global optimal algorithm with less energy, and reduce the average E2E delay by about 30% than the shortest path algorithm when the data generation rate is high.

In this paper, we focus on the physical-layer security of a multiuser cellular downlink system. A multi-antenna base station (BS) communicates with several legitimate users, where a target user is overheard by an eavesdropper in the downlink transmission. We aim at designing linear precoders for the BS to maximize the secrecy rate of target user under the condition that a certain Quality-of- Service (QoS) is guaranteed for the other legitimate users. To solve the non-convex optimization problem, we propose an iterative algorithm to transform the original problem into a sequence of approximate convex problems, which can be solved using interior point method. Simulation results reveal that compared with two other methods dealing with the similar optimization problem in previous works, our proposed method can achieve a higher secrecy rate.

In this paper, we provide a comprehensive study of secrecy transmission in decode-and-forward (DF) relay networks subjected to slow fading. With only channel distribution information (CDI) of the wiretap channels, we aim at maximizing secrecy throughput of the two-hop transmission under a secrecy outage constraint through optimizing transmission region, rate parameters of the wiretap codes and power allocation between the source and relay. We propose fixed transmission parameter scheme (FTPS) and variable transmission parameter scheme (VTPS), which are based on the CDI and instantaneous channel state information of the main channels, respectively. In both schemes, source and relay use the same codeword, and the eavesdropper can use maximum ratio combining (MRC) reception. To improve the secrecy throughput, we further propose VTPS-D1 and VTPS-D2 schemes, where the source and relay either use independent codewords with identical code rates, or different codebooks with different code rates so that the eavesdropper can only decode the two-hop signals individually rather than using MRC. We provide explicit results on the design for all proposed schemes. Numerical results and comparisons on the secrecy throughput of these schemes are presented to reveal their respective superiorities and give some insights into the choice of design scheme.

This paper addresses the data persistence problem in wireless sensor networks (WSNs) where static sinks are not present and the sensed data have to be temporarily but resiliently stored in the network. Based on the observation that sensor readings are correlated, we propose compressive data persistence (CDP) scheme that makes use of the compressive sensing (CS) theory. Each sensor node independently computes and stores a random projection of the sensed data, such that a mobile sink can recover the data with high probability after visiting a small and random portion of the network. As a prerequisite of distributed CS encoding, sensor readings from all nodes are disseminated within the network through random walk. Therefore, the CS measurement matrix depends heavily on how the random walk is performed. In this paper, we present an in-depth analysis on the interplay between random walk parameters and sensing data characteristics, and derive the conditions in successful CS data recovery. In addition, we discover that there is a trade-off between the number of random walk instances and steps in order to achieve the required data persistence performance. Experiments using real sensor data verify that the proposed CDP scheme achieves much lower decoding ratio than the state-of-the-art Fountain code based schemes or the decentralized erasure codes based schemes, and demonstrate that there exist energy-optimized random walk parameters for CDP.

In this paper, we investigate the security issue of a two-hop amplify-and-forward MIMO wireless relay networks in the existence of a multi-antenna eavesdropper. The optimal scheme to achieve the secrecy capacity involves a non-convex optimization and is still an open problem. Aiming to find an efficient way to enhance the secrecy rate, we propose a suboptimal joint source and relay linear precoding scheme. In the scheme, the source node adopt a generalized singular value decomposition (GSVD) based precoding to transmit the signal in the first phase, and the relay node forwards the received signal based on SVD in the null-space of the wiretap channel in the second phase. Power allocations in both phases are optimized to maximize the secrecy rate. An alternating iterative optimization algorithm is proposed to solve the problem. The algorithm is computationally efficient and guarantees to converge to a local optimum. Numerical evaluation results are provided to show the effectiveness of the iterative algorithm and the proposed secrecy scheme.

In this paper, we investigate the security issue of a two-hop amplify-and-forward multiple-input multiple-output wireless relay network in the existence of a multiantenna eavesdropper. The optimal scheme to achieve the secrecy capacity involves a nonconvex optimization and is still an open problem. Aiming to find an efficient way to enhance the secrecy rate with a tractable complexity, we propose a suboptimal joint source and relay linear precoding and power allocation scheme. In the scheme, the source node adopts a generalized singular value decomposition (SVD)-based precoding to transmit the signal in the first phase, and the relay node forwards the received signal based on the SVD precoding in the null-space of the wiretap channel in the second phase. Power allocations in both phases are optimized to maximize the secrecy rate by an alternating iterative optimization algorithm. Each iteration involves two subproblems. One has a water-filling solution and the other has a closed-form solution or a water-filling-like solution as well, both of which are computationally very efficient. The iteration converges fast and we prove that it guarantees to find a stationary optimum. Furthermore, we show that when the eavesdropper has equal or more antennas than the source does, the secrecy rate is a quasi-concave function of the source power so that allocating all the source power is generally not optimal. Numerical evaluation results are provided to show the effectiveness of the iterative algorithm and the proposed secrecy scheme.

This paper considers a large-scale wireless sensor network where sensor readings are occasionally collected by a mobile sink, and sensor nodes are responsible for temporarily storing their own readings in an energy-efficient and storage-efficient way. Existing data persistence schemes based on erasure codes do not utilize the correlation between sensor data, and their decoding ratio is always larger than one. Motivated by the emerging compressive sensing theory, we propose compressive data persistence which simultaneously achieves data compression and data persistence. In the development of compressive data persistence scheme, we design a distributed compressive sensing encoding approach based on Metropolis-Hastings random walk. When the maximum step of random walk is 400, our proposed scheme can achieve a decoding ratio of 0.36 for 10%-sparse data. We also compare our scheme with a state-of-the-art Fountain code based scheme. Simulation shows that our scheme can significantly reduce the decoding ratio by up to 63%.