TDMA-MAC shema težinskog grupiranja u VANET-u

In vehicle ad hoc network (VANET), the active safety messages, e.g. safety critical application (SCA) information, must be distributed timely by radio channels among vehicles to improve the driver safety. A weight clustering based TDMA-MAC scheme for VANET is presented in this paper. Considering the constrains of radio signal transmitting power in textit{Green Communication}, the vehicles (nodes) energy consumption in VANET is chosen as one important factor for cluster-head (CH) election, and entropy weight is calculated, which can reflect the subjective intention. For the clustering MAC scheme, each node in a cluster is allowed to communicate by borrowing the scheduling time slots that assigned to other nodes at an access probability. Simulation results reveal the values of access probability for which the network throughput and energy consumption under the weight clustering based MAC scheme yields the better performance compared to the region-based clustering MAC policy. Also, it has the lower average packet contention period, at the expense of a little longer average transmission time of SCA packet.U ad hoc mreži vozila (VANET), aktivne sigurnosne poruke, npr. informacije za sigurnosno kritične aplikacije, moraju biti distribuirane radijskim kanalom među vozilima na vrijeme, kako bi povećali sigurnost vozača. U ovom radu prikazana je TDMA-MAC shema VANET-a zasnovana na otežanom grupiranju. Razmatrajući ograničenja u snazi pri prijenosu radijskog signala u textit{Zelenim komunikacijama}, potrošnja energije u vozilima (čvorovi) odabrana je kao važan čimbenik za izbor voditelja grupe (CH), te se računa količina entropije, koja može odraziti subjektivnu namjeru. U svrhu MAC pristupa grupiranju, svakom čvoru grupe dozvoljena je komunikacija posudbom vremenskih intervala u rasporedu koji su pridruženi ostalim čvorovima s pridruženim vjerojatnostima pristupa.Simulacijski rezultati uz MAC shemu zasnovanu na otežanom grupiranju pokazuju bolje vrijednosti mrežne propusnosti i potrošnja energije, u odnosu na MAC sheme grupiranja zasnovane na regijama. Također, ovaj pristup ima niže prosječno vrijeme argumentacije paketa, na uštrb nešto dužeg prosječnog vremena prijenosa SCA paketa

Monte Carlo localization algorithm based on particle swarm optimization

In wireless sensor networks, Monte Carlo localization for mobile nodes has a large positioning error and slow convergence speed. To address the challenges of low sampling efficiency and particle impoverishment, a time sequence Monte Carlo localization algorithm based on particle swarm optimization (TSMCL-BPSO) is proposed in this paper. Firstly, the sampling region is constructed according to the overlap of the initial sampling region and the Monte Carlo sampling region. Then, particle swarm optimization (PSO) strategy is adopted to search the optimum position of the target node. The velocity of particle swarm is updated by adaptive step size and the particle impoverishment is improved by distributed estimation and particle replication, which avoids the local optimum caused by the premature convergence of particles. Experiment results indicate that the proposed algorithm improves the particle fitness, increases the particle searching efficiency, and meanwhile the lower positioning error can be obtained at the node\u27s maximum speed of 70 m/s

Repeated Game-Inspired Spectrum Sharing for Clustering Cognitive Ad Hoc Networks

The paper studies the cooperative spectrum sharing among multiple secondary users (SUs) in a clustering cognitive ad hoc network. The problem is formulated as a repeated game with the aim of maximizing the total transmission rate of SUs. Firstly, a clustering formation procedure is proposed to reduce the overhead and delay of game process in cognitive radio network (CRN). Then the repeated game-inspired model for SUs is introduced. With the model, the convergence condition of the proposed spectrum-sharing algorithm is conducted, and the convergence performance is investigated by considering the effects of three key factors: transmission power, discount factor, and convergence coefficient. Furthermore, the fairness of spectrum sharing is analyzed, and numerical results show a significant performance improvement of the proposed strategy when compared to other similar spectrum-sharing algorithms

An Energy-Efficient Optimization Method for High-Speed Rail Communication Systems Assisted by Intelligent Reflecting Surfaces (IRS)

This paper proposes an intelligent reflecting surface (IRS)-assisted energy efficiency optimization algorithm to address the problem of energy efficiency (EE) degradation in high-speed rail communication systems caused by line-of-sight link blockages between base stations and trains. The joint optimization of base station beamforming and IRS phase shifts is formulated as a variable-coupled energy efficiency maximization problem, subject to the base station’s transmission power and the IRS unit’s modulus constraints. This is known to be an NP-hard problem, making it challenging to obtain the global optimal solution. To tackle the issue of optimization variable coupling, an alternating optimization is employed to decompose the original problem into two sub-problems: base station beamforming and IRS phase-shift optimization. The Dinkelbach method is utilized to convert the fractional objective function into a difference form; then, the successive convex approximation (SCA) algorithm is applied to transform non-convex constraints into convex ones, which are solved using CVX. The Riemann conjugate gradient (RCG) algorithm can effectively solve the difficult unit module constraint. Finally, an alternating iterative strategy is employed to converge to a suboptimal solution. Our simulation results demonstrate that the proposed algorithm significantly enhances system efficiency with low computational complexity. Specifically, when the number of IRS reflecting elements is 64, the system’s EE is improved by approximately 12.41%, 35.26%, and 37.96% compared to the semi-definite relaxation algorithm, the random phase shift approach, and no IRS scheme, respectively

Research on Joint Beamforming of High-Speed Railway Millimeter-wave MIMO Communication with Reconfigurable Intelligent Surface

The advantage of a reconfigurable intelligent surface (RIS) is that it optimizes the wireless signal transmission environment. It can ameliorate the high loss of high-speed railway (HSR) millimeter-wave (mmWave) communications and increase signal strength by coherently superimposing signals at the receiving end through an appropriate beamforming design. In this paper, an mmWave multiple-input multiple-output communication system for HSR is discussed. The channel modeling takes into account the Doppler effect and the properties of mmWave channels. Aiming at the problem of high coupling of optimization variables, the singular value decomposition method is utilized to decouple them and to achieve the optimal active beamforming design. The original optimization problem is then rewritten into a more manageable form through a sequence of transformations. In order to overcome the restrictions imposed by non-convex constraints, the optimal RIS phase shift is directly calculated using the Riemann gradient descent (RGD) method. In this paper, spectral efficiency (SE) is taken as the optimization objective. Considering the fast time-varying environment of HSR, a step size selection scheme with easier convergence is adopted in the RGD method to complete the joint beamforming design. The numerical simulations demonstrate that the proposed solution outperforms the semi-definite relaxation scheme and the RIS-free auxiliary scheme in terms of SE performance and computational complexity

Secrecy and Throughput Performance of Cooperative Cognitive Decode-and-Forward Relaying Vehicular Networks with Direct Links and Poisson Distributed Eavesdroppers

Cooperative communication and cognitive radio can effectively improve spectrum utilization, coverage range, and system throughput of vehicular networks, whereas they also incur several security issues and wiretapping attacks. Thus, security and threat detection are vitally important for such networks. This paper investigates the secrecy and throughput performance of an underlay cooperative cognitive vehicular network, where a pair of secondary vehicles communicate through a direct link and the assistance of a decode-and-forward (DF) secondary relay in the presence of Poisson-distributed colluding eavesdroppers and under an interference constraint set by the primary receiver. Considering mixed Rayleigh and double-Rayleigh fading channels, we design a realistic relaying transmission scheme and derive the closed-form expressions of secrecy and throughput performance, such as the secrecy outage probability (SOP), the connection outage probability (COP), the secrecy and connection outage probability (SCOP), and the overall secrecy throughput, for traditional and proposed schemes, respectively. An asymptotic analysis is further presented in the high signal-to-noise ratio (SNR) regime. Numerical results illustrate the impacts of network parameters on secrecy and throughput and reveal that the advantages of the proposed scheme are closely related to the channel gain of the relay link compared to the direct link

Joint Congestion Control and Resource Allocation for Delay-Aware Tasks in Mobile Edge Computing

Recently, in order to extend the computation capability of smart mobile devices (SMDs) and reduce the task execution delay, mobile edge computing (MEC) has attracted considerable attention. In this paper, a stochastic optimization problem is formulated to maximize the system utility and ensure the queue stability, which subjects to the power, subcarrier, SMDs, and MEC server computation resource constraints by jointly optimizing congestion control and resource allocation. With the help of the Lyapunov optimization method, the primal problem is transformed into five subproblems including the system utility maximization subproblem, SMD congestion control subproblem, SMD computation resource allocation subproblem, joint power and subcarrier allocation subproblem, and MEC server scheduling subproblem. Since the first three subproblems are all single variable problems, the solutions can be obtained directly. The joint power and subcarrier allocation subproblem can be efficiently solved by utilizing alternating and time-sharing methods. For the MEC server scheduling subproblem, an efficient algorithm is proposed to solve it. By solving the five subproblems at each slot, we propose a delay-aware task congestion control and resource allocation (DTCCRA) algorithm to solve the primal problem. Theoretical analysis shows that the proposed DTCCRA algorithm can achieve the system utility and execution delay trade-off. Compared with the intelligent heuristic (IH) algorithm, when the control parameter V increases from 106 to 107, the total backlogs are decreased by 5.03% and the system utility is increased by 3.9% on average for the extensive performance by using the proposed DTCCRA algorithm