The use of full frequency reuse (FFR) for low-earth orbit (LEO) satellite multiple-input multiple-output (MIMO) communication systems can further improve spectral efficiency, but it also leads to serious co-channel interference (CCI). Compared with land systems, LEO satellites’ high dynamism and long transmission delay pose challenges in eliminating CCI due to the difficulty in obtaining accurate channel state information (CSI). This paper adopts a blind source separation (BSS) algorithm that does not require accurate CSI and avoids the extra occupation of channel resources to eliminate CCI. LEO satellite MIMO communication system with FFR has convolutional mixing characteristics. The inherent sorting ambiguity of the BSS algorithm leads to the difficulty of signal reconstruction after converting frequency-domain de-mixing signals to time-domain. This paper proposes a Fast Decoupled-Independent Vector Analysis (FD-IVA) algorithm. Firstly, FD-IVA algorithm uses IVA algorithm to solve the sorting ambiguity problem in the demixing process. Then, the iteration step size of each row of the separation matrix is optimized separately by decoupling to reduce the number of iterations of the de-mixing algorithm. Finally, the optimization of the initial matrix is achieved through joint approximative diagonalization of the eigenmatrix (JADE) algorithm to further improve the convergence speed of the de-mixing algorithm. Simulation analysis shows that the proposed FD-IVA algorithm has the best performance of CCI elimination, and the optimized initial separation matrix can obtain a better initial iteration point, significantly reducing the number of iterations.

The multi-satellite multiple-input multiple-output (MIMO) communication system with full frequency reuse (FFR) has high spectral efficiency. However, satellite communications are highly susceptible to interference, and the communication quality of multi-satellite MIMO communication systems with FFR will be seriously affected when suffering from close-range interference from mobile unmanned aerial vehicle (UAV) Cluster. Therefore, we utilize an online blind source separation (BSS) algorithm without the aid of a priori information for the anti-jamming of mobile UAV clusters, which can avoid the additional occupation of channel resources, as well as process the mixed signals of time-varying systems and output separated signals in real-time. In this paper, the R-V-M-EASI algorithm is proposed, which adjusts the adaptive step size and momentum term factor using the crosstalk index as a separation performance index. The mutation point of the time-varying mixing matrix can be determined by detecting the change of the error function and then using a better converged separation matrix before the mutation to separate the observation signal retrospectively. Simulation results show that the R-V-M-EASI algorithm can effectively separate the mixed signals in both stationary and non-stationary environments, and it has faster convergence speed and lower steady error. For scenarios where the mixed matrix mutates, the proposed algorithm is effective in improving the separation accuracy of the separation matrix at the initial stage.

In order to achieve the long-term work of the wireless sensor network in the border area,a network combining UAV and NOMA(Non-Orthogonal Multiple Access)technology is used to collect data from sensors distributed along the narrow border,so as to effectively reduce the energy consumption of WSNs(Wireless Sensor Networks).The sensor user group uploads data to UAV in a multi-carrier NOMA mode to minimize the total energy consumption of all sensors by optimizing sensor user scheduling and grouping,transmit power and UAV trajectory under the constraints of maximum transmit power,user QoS(Quality of Service)and UAV mobility.The optimization problem is a mixed integer nonlinear optimization problem,which is decomposed into three subproblems to solve.By using exhaustive search,Lagrange duality decomposition and SCA(Successive Convex Approximation),the subproblems are solved successively,and the minimum energy consumption is achieved.Simulation results indicate that the proposed scheme can effectively reduce the total energy consumption of the WSNs.

SummaryMultiple‐input multiple‐output (MIMO) technology can boost the capacity of conventional satellite communications, and full frequency reuse (FFR) can further improve the capacity by making full use of frequency resources. However, FFR will cause severe co‐channel interference and lead to a degradation of channel capacity. Blind source separation (BSS) algorithm can be used to eliminate co‐channel interference and does not require prior information, which avoids the extra occupation of channel frequency resources. While the multi‐satellite MIMO communication system have delay mixing characteristic, the separation performance will be degraded if the delay is ignored. In this paper, DMBSS algorithm for delay mixing is proposed. Firstly, the DMBSS algorithm utilizes Taylor expansion to achieve dimension expansion of observed signals and so as to transform the delay mixing into instantaneous mixing with extended dimension. Secondly, complex non‐orthogonal joint diagonalization algorithm is used to solve the separation matrix equation of extended observation signals and extended source signals and thus to eliminate the co‐channel interference. Simulation results show that for multi‐satellite MIMO communication scenarios with FFR, the proposed algorithm can effectively improve the performance of co‐channel interference cancellation and reduce the value of BER. Furthermore, the influence of order of Taylor expansion on the separation performance is also analyzed and simulated in this paper to show the cost performance of the proposed algorithm.

The Service trigger algorithm is the core of the entire IMS system platform, which can affect the work efficiency and service quality of the entire system. In the early days, the service trigger algorithm officially defined by 3GPP can no longer meet the overall requirements of current big data and big user information services. In order to meet the Service needs of the multimedia network platform and the huge user group in the future, this paper conducts an in-depth study on the official Service trigger algorithm, and proposes an IMS packet Service trigger algorithm based on the double filter criteria. This algorithm achieves the goal of quickly processing multiple services by combining the packet Service trigger mechanism with the iFC+sFC dual service filter criteria. The simulation results show that the new service triggering algorithm can effectively reduce the overall session setup delay, improve the system throughput, and greatly improve the IMS system service triggering efficiency.

This letter investigates the problem of combating the intelligent reactive jammer through a deep reinforcement learning (DRL) based hidden strategy. Existing anti-jamming researches always assume a capacity-limited adversary which is non-reactive or only launches attacks while detecting activities of the transmitter. In this letter, we consider an intelligent reactive jammer that releases track jamming once activities of the transmitter are detected, otherwise it launches indiscriminate jamming (e.g., sweep or comb jamming). A DRL-based hidden strategy is proposed to resist this powerful jamming via adjusting power and accessing idle channels simultaneously. Moreover, the parallel policy network and individual reward function are introduced to enhance the anti-jamming performance. Simulation results confirm that the proposed algorithm can significantly elude the jammer’s detection and achieve dynamic spectrum access in a dynamic and unknown environment.

An excellent routing protocol is important for wireless sensor network (WSN) construction and efficient data transmission. With the continuous expansion of the application scenarios and scopes of the Internet of Things, the existing WSN routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this article proposes an energy-saving clustering protocol based on adaptive Voronoi dividing, named energy-saving clustering by Voronoi adaptive dividing (ESCVAD) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as low energy adaptive clustering hierarchy (LEACH) protocol and stable energy protocol (SEP), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.

In this paper, the blind signal separation problem of complex baseband signal is addressed. A widely linear complex autoregressive process of order one is employed to represent the temporal structure of complex sources. We formulate a new contrast function by a convex combination of generalized autocorrelations and the statistics of the innovation. And the proposed contrast function is optimized by gradient method. Simulation results show that the proposed algorithm is better than the comparison algorithm in convergence speed and convergence accuracy.

Aiming at the poor separation performance of traditional blind source separation (BSS) algorithms at low signal-to-noise ratios (SNR), a new BSS algorithm under low SNR is proposed. The algorithm designs the structure of “pre-denoising + blind source separation + post-denoising”, which uses ensemble empirical mode decomposition technology to complete the pre-denoising before separation, and the stochastic resonance technology to complete the post-denoising after separation. The simulation results show that the proposed algorithm can effectively separate communication signals and jamming from the observed mixed signals under the condition of low SNR.

This paper investigates the impact of partial relay selection scheme (PRSS) on cooperative underlay cognitive radio (CR) non-orthogonal multiple access (NOMA) networks. Decode-and-forward (DF) protocol is used at the selected relay to assist simultaneous transmission from the source node to destination users. Based on the above assumptions, we obtain the closed-form expressions of the outage probability (OP) for the secondary users. Moreover, the analytical results of this paper verify the impacts of relay node's number and power allocation (PA) on the system performance. Monte Carlo (MC) simulations validate the theoretical results.

We investigate the energy efficiency (EE) channel selection problem in energy harvesting cognitive radio sensor network (EH-CRSN). The current channel allocation studies usually only consider a couple of transmitter-receiver pairs or multiple sensor nodes (SNs) with single sink. We study the network scenario with multiple sinks and multiple SNs. In view of the property that energy resource constraints in energy harvesting network, we seek the EE optimization method mechanism. The main idea of the EE-centric optimization is each SN selects channel according to the local EE to make the overall system EE improved and get the convergence at last. In this paper, we firstly introduce a RF-powered EN-CRSN and the EE formulation. Then, we proposed a game model and analyze its properties. Next, we design a SLA based channel select algorithm. Finally, simulation results show that the proposed framework can effectively improve system EE compared with the throughput-centric optimization scheme.

Transmission delay and energy efficiency (EE) are important performances of 5G. Applying wireless energy harvesting (WEH) technology to cognitive radio network (CRN) can effectively solve battery power problems, and it is expected to have higher EE for wireless energy harvesting cognitive radio network (WEH-CRN). Collision will occur easily while multiple secondary transmitters (STs) of CRN simultaneously accesses the idle licensed channel, thus the secondary network transmission delay will increase and EE will decrease. By using the p-persistent CSMA mechanism for transmission, the collision probability can be effectively reduced. Based on WEH-CRN model and p-persistent CSMA protocol, this paper focus on analyzing the average packet delay and the EE of ST, including theoretical analysis and numerical simulation. These results will provide useful reference for the parameter design of the WEH-CRN.

In this paper, we consider an energy harvesting cognitive radio sensor network (EH-CRSN), which is composed of multiple secondary users (sensor nodes) opportunistically access licensed channels. We first propose a novel single-sink EH-CRSN and derive its network throughput. Then, we extend the EH-CRSN to the multi-sink case which will cause interference among secondary communications. To deal with this problem, we optimize channel access schedule, so as to maximize the network throughput in many actual large-scale scenarios. Specifically, we formulate a mixed interaction game and demonstrated the existence of Nash equilibrium. A stochastic learning automata (SLA)-based channel selecting an algorithm is further proposed to achieve the Nash equilibrium. Finally, the simulation results verify the validity of network throughput function in single-sink EH-CRSN and show that the proposed solution can get the maximum or near-maximum system throughput.