A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks.

With the continuous progress of science and technology, intelligent wireless sensor network (IWSN) communication has become indispensable in its role in production and life because of its convenient network settings and flexible use. However, with the widespread availability of intelligent wireless sensor networks, the use of many wireless sensor nodes constitutes a multi-node wireless communication system, which turns the accuracy and low complexity of multi-node detection in sensor networks into a problem. Although the traditional algorithm has excellent performance, it cannot give consideration to both accuracy and complexity. Therefore, a maximum logarithm message passing algorithm based on serial and threshold (S-T-Max-log-MPA) for multi-mode detection in IWSN is proposed in this paper. In this algorithm, the threshold is used to determine the necessary conditions of sensor node stability first, and then the sensor node information updating is integrated into the resource node information updating, so that the system can maintain good accuracy, performance, and change the situation of poor system accuracy at low threshold. Compared with the traditional algorithm, the proposed algorithm significantly changes the algorithm complexity reduction rate of the system multi-node detection. Simulation results show that the algorithm has a good balance between accuracy and complexity reduction rate.

A Threshold-Based Max-log-MPA Low Complexity Multiuser Detection Algorithm.

Sparse Code Multiple Access (SCMA) technology is a new multiple access scheme based on non-orthogonal spread spectrum technology, which was proposed by Huawei in 2014. In the algorithm application of this technology, the original Message Passing Algorithm (MPA) has slow convergence speed and high algorithm complexity. The threshold-based MPA has a high Bit Error Ratio (BER) when the threshold is low. In the Maximum logarithm Message Passing Algorithm (Max-log-MPA), the approximation method is used, which will cause some messages to be lost and the detection performance to be poor. Therefore, in order to solve the above problems, a Threshold-Based Max-log-MPA (T-Max-log-MPA) low complexity multiuser detection algorithm is proposed in this paper. The Maximum logarithm (Max-log) algorithm is combined with threshold setting, and the stability of user nodes is considered as a necessary condition for decision in the algorithm. Before message updating, the user information nodes are judged whether the necessary conditions for the stability of the user node have been met, and then the threshold is determined. Only users who meet the threshold condition and pass the necessary condition of user node stability can be decoded in advance. In the whole process, the logarithm domain MPA algorithm is used to convert an exp operation and a multiplication operation into a maximum value and addition operation. The simulation results show that the proposed algorithm can effectively reduce the computational complexity while ensuring the BER, and with the increase of signal-to-noise ratio, the effect of the Computational Complexity Reduction Ratio (CCRR) is more obvious.

Anti-Wiretap Spectrum-Sharing for Cooperative Cognitive Radio Communication Systems.

As wireless communication technology keeps progressing, people's requirements for wireless communication quality are getting higher and higher. Wireless communication brings convenience, but also causes some problems. On the one hand, the traditional static and fixed spectrum allocation strategy leads to high wastefulness of spectrum resources. The direction of improving the utility of spectrum resources by combining the advantages of cooperative communication and cognitive radio has attracted the attention of many scholars. On the other hand, security of communication is becoming an important issue because of the broadcasting nature and openness of wireless communication. Physical-layer security has been brought into focus due to the possibility of improving the security in wireless communication. In this paper, we propose an anti-wiretap spectrum-sharing scheme for cooperative cognitive radio communication systems which can secure the information transmission for the two transmission phases of the cooperative communication. We maximized the secondary system transmission rate by jointly optimizing power and bandwidth while ensuring the primary system achieves its secrecy transmission rate. Useful insights of the proposed anti-wiretap spectrum-sharing scheme are given in the simulation results. Moreover, several system parameters are shown to have a big impact for the simulation results.

Rateless Coded Uplink Transmission Design for Multi-User C-RAN.

Cloud radio access network (C-RAN) is a promising technology for the Internet of Things (IoT). In C-RAN, the remote radio head (RRH) and baseband unit (BBU) in the conventional base station are separated, and each BBU is backward centralized into a virtual BBU pool. In this paper, we consider the uplink transmission for the two-user C-RAN with two RRHs under a block fading channel. A novel rateless coded transmission scheme is designed. During each transmission round, each user keeps transmitting to the RRHs using Raptor code until the BBU pool feeds back an acknowledgement (ACK). With the proposed scheme, each user does not require the instant channel state information, which greatly reduces the system overhead. We also design the quantizer at the RRHs and the iterative multi-user detector and decoder at the BBU pool, based on the belief propagation (BP) algorithm. For the Raptor code applied at each user, we optimize the corresponding output node degree profile, based on extrinsic information transfer (EXIT) analysis for the decoding process at the BBU pool. The resulted degree profiles are optimal in an average sense under all possible channel states. The simulation results show that the rateless coded transmission scheme with the optimized degree profiles outperforms the benchmark degree profile in both bit error rate and average system throughput. Moreover, the achieved performance is close to the theoretical limit.

Subcarrier Allocation Based Cooperative Spectrum Sharing with Wireless Energy Harvesting in OFDM Relaying Networks.

In this paper, we propose subcarrier allocation based cooperative spectrum sharing protocol for OFDM relaying networks with wireless energy harvesting. In the proposed protocol, the cognitive relay node utilizes different subcarriers to forward the primary information to obtain the spectrum access for the cognitive system transmission. The primary system consists of two parts, a primary transmitter (PT) and primary receiver (PR), and cognitive system includes a cognitive source node (CSN), cognitive destination node (CDN) and cognitive relay node (CRN). In the first phase, CRN splits a fraction of the power received from the PT and CSN transmission to decode information, while the remaining power is used for energy harvesting. Then CRN uses disjoint subcarriers to forward the signals of PT and CSN by utilizing the harvested energy in the second phase. Three parameters which consist of power splitting ratio, power allocation and subcarriers allocation are optimized in our algorithm to maximize the cognitive transmission rate with the constraint of primary target transmission rate. Numerical and simulation results are shown to give useful insights into the proposed cooperative spectrum sharing protocol, and we also found that various system parameters have a great effect for the simulation results.

Joint Resource Optimization in Simultaneous Wireless Information and Power Transfer (SWIPT) Enabled Multi-Relay Internet of Things (IoT) System.

The internet of things (IoT) is becoming more indispensable in modern society as the further development and maturity of information technology progresses. However the exponential growth of IoT devices leads to severe energy consumption. As a technology with broad application prospects, simultaneous wireless information and power transfer (SWIPT) enables IoT devices to harvest energy from receiving radio frequency (RF) signals while ensuring information transmission. In this paper, we investigate the transmission rate optimization problem for a dual-hop multi-relay IoT system, where a decode-and-forward (DF) relay supports the SWIPT technique. We jointly optimize the resource including power and subcarrier allocation, to maximize the system transmission rate. The time-sharing strategy and Lagrange dual method are used to solve this optimization problem. Simulation results reveal that the proposed algorithm has a larger transmission rate than other benchmark algorithms when ensuring each relay has no additional energy supply. Specifically, the proposed algorithm improves the information transmission rate by 2.8%, 3.4% and 43% compared with other algorithms in the case of five relays when the source's power is equal to 0.5 W, respectively.

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks.

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay.

Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN's achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved.

Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network.

The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited due to its small size, deployment in atrocious environments and long-term working. In this paper, an energy-harvesting-based CS is described, which senses the PU together with collecting the radio frequency energy to supply data transmission. In order to improve the transmission performance of the CS, we have proposed the joint resource allocation of spectrum sensing and energy harvesting in the cases of a single energy-harvesting-based CS and an energy-harvesting-based cognitive sensor network (CSN), respectively. Based on the proposed frame structure, we have formulated the resource allocation as a class of joint optimization problems, which seek to maximize the transmission rate of the CS by jointly optimizing sensing time, harvesting time and the numbers of sensing nodes and harvesting nodes. Using the half searching method and the alternating direction optimization, we have achieved the sub-optimal solution by converting the joint optimization problem into several convex sub-optimization problems. The simulation results have indicated the predominance of the proposed energy-harvesting-based CS and CSN models.