Joint optimization for secure ambient backscatter communication in NOMA-enabled IoT networks

Non-Orthogonal Multiple Access(NOMA)has emerged as a novel air interface technology for massive connectivity in Sixth-Generation(6G)era.The recent integration of NOMA in Backscatter Communication(BC)has triggered significant research interest due to its applications in low-powered Internet of Things(IoT)networks.However,the link security aspect of these networks has not been well investigated.This article provides a new optimization framework for improving the physical layer security of the NOMA ambient BC system.Our system model takes into account the simultaneous operation of NOMA IoT users and the Backscatter Node(BN)in the presence of multiple EavesDroppers(EDs).The EDs in the surrounding area can overhear the communication of Base Station(BS)and BN due to the wireless broadcast transmission.Thus,the chief aim is to enhance link security by optimizing the BN reflection coefficient and BS transmit power.To gauge the performance of the proposed scheme,we also present the suboptimal NOMA and conventional orthogonal multiple access as benchmark schemes.Monte Carlo simulation results demonstrate the superiority of the NOMA BC scheme over the pure NOMA scheme without the BC and conventional orthogonal multiple access schemes in terms of system secrecy rate.

Enhanced EM-based channel estimation for MIMO-OFDM in highly mobile channels

An enhanced expectation-maximization(EM)-based iterative channel estimator for coping with channel time variation is proposed for mobile multiple-input multi-output orthogonal frequency division multiplexing(MIMO-OFDM)systems.In the proposed scheme,the recursive least squares(RLS)algorithm is applied to track the time-varying channel impulse response(CIR)within several symbols.By using the tracked time-varying CIR,the ICI are constructed and then cancelled from the received signal,thus reducing their impactions on the channel estimation.Moreover,based on an over-sampled complex exponential basis expansion model(OCE-BEM),an improved channel predictor is derived in order to improve the initial channel estimates accuracy of the iterative estimator.Simulation results show that the proposed scheme outperforms the classic counterpart in time-varying scenarios with a smaller cost of complexity.

Energy Efficiency Analysis of Multi-Antenna Cellular Networks with Strategic Sleep Relay

Energy efficiency(EE) of cellular networks has attracted considerable attention recently. However, EE of relay-assisted cellular networks where the macro base stations(MBSs) are equipped with the multi-antenna has not been thoroughly addressed. This paper considered the downlink transmission of multi-antenna relay-assisted cellular networks, meanwhile, a strategic sleep scheme was used in relay stations(RSs), which dynamically adjusted the RS working mode according to whether the number of users serviced by the relay exceeds a given threshold. A geometric model was built to derive the coverage probability and mean achievable rate from the MBSs to user(UE), the MBS to RS, the RS to UE links and analyze the system EE. It is shown that the energy efficiency of cellular network with strategic sleep RS is slightly higher than that of cellular network with non-sleeping strategy. Furthermore, the MBS equipped with multi-antenna has better impact on energy efficiency and spectral efficiency than the MBS with single antenna.

Component Carrier Selection and Beamforming on Carrier Aggregated Channels in Heterogeneous Networks

In this paper, component carrier selection and beamforming on carrier aggregated channels in Heterogeneous Networks are proposed. The scheme jointly selects the component carrier and precoding (i.e. beamforming) vectors with the cooperation of the other cells to deal with the interference between Macro cell and Pico cell. The component carrier selection and beamforming is achieved by optimizing the multi-cell downlink throughput. This optimization results in shutting down a subset of the component carrier in order to allow for a perfect interference removal at the receive side in the dense low power node deployment scenario. Additionally, algorithm based on Branch and Bound Method is used to reduce the search complexity of the algorithm. Simulation results show that the proposed scheme can achieve high cell-average and cell-edge throughput for the Pico cell in the Heterogeneous Networks.

Energy Efficiency Optimization for Heterogeneous Cellular Networks Modeled by Matérn Hard-Core Point Process

The Poisson point process(PPP) has been widely used in wireless network modeling and performance analysis due to the independence between its nodes. Therefore, it may not be a suitable model for many of the exclusive networks between the nodes. This paper analyzes the energy efficiency(EE) and optimizes the two-tier heterogeneous cellular networks(Het Nets). Considering the mutual exclusion between macro base stations(MBSs) distribution, the deployment of MBSs is modeled by the Matérn hard-core point process(MHCPP), and the deployment of pico base stations(PBSs) is modeled by the PPP. We adopt a simple approximation method to study the signal to interference ratio(SIR) distribution in two-tier MHCPP-PPP networks and then derive the coverage probabilities, the average data rates and the energy efficiency of Het Nets. Finally, an optimization algorithm is proposed to improve the EE of Het Nets by controlling the transmit power of PBSs. The simulation results show that the EE of a system can be effectively improved by selecting the appropriate transmit power for the PBSs. In addition, two-tier MHCPP-PPP Het Nets have higher energy efficiency than two-tier PPP-PPP Het Nets.

Precise and full extraction of the coupling-of-modeparameters with periodic Green's function

Precise extraction of coupling-of-mode (COM) parameters plays a key role in the design for modern high performance surface acoustic wave (SAW) filters. An accurate and efficient analysis of char- acteristics for SAW propagating under periodic metal gratings has been developed based on the concept of harmonic admittance and periodic Green抯 function. Some further improvement is made on the nu- merical algorithm, such as isolation of the logarithmic singularity, asymptotic simplification of periodic Green抯 function, and utilization of Chebyshev polynomials as basis functions of the charge distribution. The most important point is extraction of the phase of coupling reflection coefficient by the characteris- tics of standing wave on the edges of stopband. This approach leads to a fast, precise and full extraction of COM parameters. The results and discussions for several materials have been presented.

Passive location using CDMA mobile communication system based on repeater equipment

To overcome the drawback of a short operationrange and low-resolution of a passive location system using acivil communication signal, the new idea that utilizes codedivision multiple access (CDMA) signal and repeater is disposedoff. First, the CDMA passive location model and observationfunction are given, and the error source and error range areanalyzed. Subsequently, the CDMA passive location algorithmin a repeater environment is described and simulated. Thesimulation result shows that the algorithm can provide thelocation value with high accuracy.

Joint spatial-frequency blind multiuser detection based on LCCMA

Multicarrier code division multiple access (MC-CDMA) has the ability to combat with frequency selective fading and antenna array can enhance the performance of system. The paper proposes a novel joint spatial-frequency blind multiuser detection for antenna array MC-CDMA based on linear constraint constant modulation algorithm (LCCMA), which has robust performance and can ensue the weight vectors to converge to that of the desired user. Simulation indicates the proposed algorithm has better bit error ratio (BER) performance than that of the traditional beamforming-based two-step algorithm.

Byzantine Fault-Tolerant Routing for Large-Scale Wireless Sensor Networks Based on Fast ECDSA

Wireless sensor networks are a favorite target of Byzantine malicious attackers because of their limited energy, low calculation capability, and dynamic topology, and other important characteristics. The Byzantine Generals Problem is one of the classical problems in the area of fault tolerance, and has wide application, especially in distributed databases and systems. There is a lot of research in agreement and replication techniques that tolerate Byzantine faults. However, most of this work is not suited to large-scale wireless sensor networks, due to its high computational complexity. By introducing Fast ECDSA(Elliptic Curve Digital Signature Algorithm), which can resist timing and energy attacks, and reduce the proportion of verifying signature algorithm to generating signature algorithm to 1.2 times, we propose a new Byzantine fault-tolerant routing algorithm for large-scale wireless sensor networks with double-level hierarchical architecture. In different levels, the algorithm runs different BFT protocols.Theory and simulation results have proved that this algorithm has high security and the number of communication rounds between clusters is reduced by 1/3, which balances the network load. At the same time, the application of Fast ECDSA improves the security level of the network without burdening it.

IDRMS: an improved dynamic replication management scheme for cloud storage

Cloud storage is getting more and more popular as a new trend of data management. Data replication has been widely used to increase the data availability in cloud storage systems. However,most data replication schemes do not fully consider cost and latency issues when users need large amounts of remote replicas. We present an improved dynamic replication management scheme( IDRMS). By adding a prediction model,the optimal allocation of replicas among the cloud storage nodes is determined that the total communication cost and network delay are minimal. When the local data block is frequently requested,the data replicas can be moved to a closer or cheaper node for cost reduction and increased efficiency. Moreover,we replace the B+tree with the B*tree to speed up the search and reduce workload with the lowest blocking probability. We define the value of popularity to adjust the placement of replicas dynamically. We divide the data nodes in the network into hot nodes and cool nodes. By changing to visit cool nodes instead of hot nodes,we can balance the workload in the network. Finally,we implement IDRMS in Matlab simulation platform and simulation results demonstrate that IDRMS outperforms other replication management schemes in terms of communication cost and load balancing for large-scale cloud storage.

Distributed data storage solution under sink failures in wireless sensor networks

In challenging environment, sensory data must be stored inside the network in case of sink failures, we need to redistribute overflowing data items from the depleted storage source nodes to sensor nodes with available storage space and residual energy. We design a distributed energy efficient data storage algorithm named distributed data preservation with priority(D^2P^2). This algorithm takes both data redistribution costs and data retrieval costs into account and combines these two problems into a single problem. D^2P^2 can effectively realize data redistribution by using cooperative communication among sensor nodes. In order to solve the redistribution contention problem, we introduce the concept of data priority, which can avoid contention consultations between source nodes and reduce energy consumption. Finally, we verify the performance of the proposed algorithm by both theory and simulations. We demonstrate that D^2P^2's performance is close to the optimal centralized algorithm in terms of energy consumption and shows superiority in terms of data preservation time.

Fast adaptive principal component extraction based on a generalized energy function

By introducing an arbitrary diagonal matrix, a generalized energy function (GEF) is proposed for searching for the optimum weights of a two layer linear neural network. From the GEF, we derive a recur- sive least squares (RLS) algorithm to extract in parallel multiple principal components of the input covari- ance matrix without designing an asymmetrical circuit. The local stability of the GEF algorithm at the equilibrium is analytically verified. Simulation results show that the GEF algorithm for parallel multiple principal components extraction exhibits the fast convergence and has the improved robustness resis- tance to the eigenvalue spread of the input covariance matrix as compared to the well-known lateral inhi- bition model (APEX) and least mean square error reconstruction (LMSER) algorithms.