Particle swarm optimization based network selection in heterogeneous wireless environment

Deployment of heterogeneous wireless networks is spreading throughout the world as users want to be connected anytime, anywhere, and anyhow. Meanwhile, users are increasingly interested in multimedia applications such as audio, video streaming and Voice over IP (VoIP), which require strict Quality of Service (QoS) support. Provisioning of Always Best Connected (ABC) network with such constraints is a challenging task. Considering the availability of various access technologies, it is difficult for a network operator to find reliable criteria to select the best network that ensures user satisfaction while reducing multiple network selection. Designing an efficient Network selection algorithm, in this type of environment, is an important research problem. In this paper, we propose a novel network selection algorithm utilizing signal strength, available bit rate, signal to noise ratio, achievable throughput, bit error rate and outage probability metrics as criteria for network selection. The selection metrics are combined with PSO for relative dynamic weight optimization. The proposed algorithm is implemented in a typical heterogeneous environment of EDGE (2.5G) and UMTS (3G). Switching rate of the user between available networks has been used as the performance metric. Moreover, a utility function is used to maintain desired QoS during transition between networks, which is measured in terms of the throughput. It is shown here that PSO based approach yields optimal network selection in heterogeneous wireless environment.     

A spectrum handoff scheme for optimal network selection in Cognitive Radio vehicular networks: A game theoretic auction theory approach

The recent strides in vehicular networks have emerged as a convergence of multi radio access networks having different user preferences, multiple application requirements and multiple device types. In future Cognitive Radio (CR) vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. Hence, it becomes a challenge for CR vehicular node to select the optimal network for the spectrum handoff decision. A game theoretic auction theory approach is interdisciplinary effective approach to select the optimal network for spectrum handoff. The competition between different CR vehicular node and access networks can be formulated as multi-bidder bidding to provide its services to CR vehicular node. The game theory is the branch of applied mathematics which make intelligent decision to select the optimal alternative from predetermined alternatives. Hence, this paper investigates a spectrum handoff scheme for optimal network selection using game theoretic auction theory approach in CR vehicular networks. The paper has also proposed a new cost function based multiple attribute decision making method which outperforms other existing methods. Numerical results revel that the proposed scheme is effective for spectrum handoff for optimal network selection among multiple available networks.     

Outage probability and ergodic capacity analysis of satellite–terrestrial NOMA system with mixed RF/mmWave relaying

This paper investigates the performance of hybrid radio frequency/millimeter wave (RF/mmWave) satellite–terrestrial relay network with non-orthogonal multiple access (NOMA) scheme. In particular, the satellite network operates at the Ka-band while the mmWave band is adopted at decode-and-forward (DF) relay–destination link. The blockage effect in mmWave communication is considered to deduce the ordered probability density function (PDF) of the user equipment (UE) distance. However, the randomness of the UEs’ location and the random beamforming design cause the difficulty of the performance analysis. Herein, we provide a closed-form approximation for the outage probability. Moreover, the system diversity order is derived from the asymptotic expressions of the outage probability at high signal-to-noise ratio (SNR) slopes. Besides, approximate ergodic capacity performances of the multi-UEs are evaluated with numerical integration. Simulations are applied to demonstrate the accuracy of the theoretical analysis and show that the performance of the ordered selection NOMA scheme outperforms the orthogonal multiple access scheme and random selection scheme.     

Performance analysis of overlay cognitive NOMA network with imperfect SIC and imperfect CSI

In this paper, we investigate a multiple users cooperative overlay cognitive radio non-orthogonal multiple access (CR-NOMA) network in the presence of imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI). In the context of cellular network, cell-center cognitive secondary users act as relays to assist transmission from the primary user (PU) transmitter to the cell-edge PU receiver via NOMA. According to the received signals between the primary transmitter and multiple cognitive secondary center users, the best cell-center cognitive SU with the maximum signal to noise ratio (SNR) is selected to transmit the PU’s signals and its own signal to cell-edge users through NOMA principle. Then, the PU cell-edge user combine the signals received from direct transmission in the first phase and relay transmission from the best cell-center cognitive SU in the second phase by selection combining (SC). To measure the performance of the system quantitatively, we derive the end-to-end outage probability and capacity for the primary and secondary networks by taking the imperfect SIC and CSI into consideration. Finally, the performance analysis is validated by the simulations, and show that serious interference caused by imperfect SIC and (or) imperfect CSI reduce the system performance.     

Power allocation for cognitive underlay networks with spectrum band selection

In this paper, we study the cooperative communication of a cognitive underlay network by utilizing the diversity of multiple spectrum bands. In particular, we assume that the transmission power of the secondary user (SU) is subject to different joint constraints, such as peak interference power of the multiple primary users (PUs), peak transmission power of the SU, outage tolerate interference, and outage probability threshold. Accordingly, two power allocation schemes are considered on the basis of the minimum interference channel from the SU to the PU and the channel state information of the primary user link. Furthermore, the SU can select one of the three transmission modes following the channel state conditions, namely as cellular, device-to-device, or switching mode, to transmit the signal to the secondary user receiver. Given this setting, two power allocation schemes over a spectrum band selection strategy are derived. In addition, closed-form expressions for the outage probability of three modes are also obtained to evaluate the performance of the secondary network. Most importantly, a closed-form expression for the peak interference power level of the PU, which is considered as one of the most important parameters to control the SU’s transmission power, is derived by investigating the relation of two considered power allocation schemes in the practise. Finally, numerical examples show that the outage performance of secondary network in the switching mode outperforms the one of the cellular and device-to-device (D2D) mode for all considered power allocation schemes.     

RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.     

A fuzzy extension of VIKOR for target network selection in heterogeneous wireless environments

In a highly integrated ubiquitous wireless environment, the selection of a network that can fulfill end-users’ service requests while keeping their overall satisfaction at a high level, is vital. The wrong selection can lead to undesirable conditions such as unsatisfied users, weak Quality of Service (QoS), network congestions, dropped and/or blocked calls, and wastage of valuable network resources. The selection of these networks is performed during the handoff process when a Mobile Station (MS) switches its current Point of Attachment (PoA) to a different network due to the degradation or complete loss of signal and/or deterioration of the provided QoS. Traditional schemes perform the handoff necessity estimation and trigger the network selection process based on a single metric such as Received Signal Strength (RSS). These schemes are not efficient enough, as they do not take into consideration the traffic characteristics, user preferences, network conditions and other important system metrics. This paper presents a novel multi-attribute vertical handoff algorithm for heterogeneous wireless networks which achieves seamless mobility while maximizing end-users’ satisfaction. Two modules are designed to estimated the necessity of handoff and to select the target network. These modules utilize parallel Fuzzy Logic Controllers (FLCs) with reduced rule-set in combination with a network ranking algorithm developed based on Fuzzy VIKOR (FVIKOR). Simulation results are provided and compared with a benchmark.     

A novel power allocation strategy for cooperative PDMA systems

Cooperative communication technology is of great importance for increasing the user reachable rate, further improving throughput and reducing the outage probability of non-orthogonal multiple access (NOMA) systems. This paper mainly studies the power allocation optimization method based on amplify-and-forward (AF) pattern division multiple access (PDMA) to obtain the maximum achievable throughput. We formulate an optimization problem of user power allocation in a downlink PDMA system with cooperative relaying, the exact expressions of system throughput and user outage probability of the AF-PDMA system are derived, and a novel power allocation optimization method based on uniform distribution and restricted constraints is proposed. The effectiveness of the restricted constraints and optimization method is verified by theoretical analysis and simulation. The studies we have performed showed that the proposed scheme with uniform distribution and restricted constraints can be significantly improved in terms of the system throughput in comparison to the case with a genetic algorithm (GA) and fixed power allocation scheme. Concerning the proposed method, the search space is reduced to 1/3 of the original feasible region, and the runtime of the algorithm accounts for only 20% of the GA runtime.     

Outage performance of CoMP-CNOMA networks with duplex mode selection

This paper investigates the outage performance of a downlink coordinated multipoint cooperative non-orthogonal multiple access (CoMP-CNOMA) network, where two near users cooperatively relay the information to a far user. It is assumed that the near users support both the half-duplex relaying mode and the full-duplex relaying mode. Since the full-duplex mode may be inferior to the half-duplex mode due to the residual self-interference under the full-duplex mode, we propose a duplex mode selection strategy for the near users to dynamically choose the duplex mode for relaying. Under the proposed duplex mode selection strategy, the closed form expressions for the outage probabilities of all the users are derived. The theoretical results match well with the Monte Carlo simulations. It is shown that the proposed duplex mode selection strategy can provide low outage probabilities of all the users simultaneously, while the existing duplex mode selection strategies either achieve higher outage probabilities of all the users or achieve slightly better outage performance of the far user at the sacrifice of much worse outage performance of the near users.     

Reputation-based network selection mechanism using game theory

Current and future wireless environments are based on the coexistence of multiple networks supported by various access technologies deployed by different operators. As wireless network deployments increase, their usage is also experiencing a significant growth. In this heterogeneous multi-technology multi-application multi-terminal multi-user environment users will be able to freely connect to any of the available access technologies. Network selection mechanisms will be required in order to keep mobile users “always best connected” anywhere and anytime. In such a heterogeneous environment, game theory techniques can be adopted in order to understand and model competitive or cooperative scenarios between rational decision makers. In this work we propose a theoretical framework for combining reputation-based systems, game theory and network selection mechanism. We define a network reputation factor which reflects the network’s previous behaviour in assuring service guarantees to the user. Using the repeated Prisoner’s Dilemma game, we model the user–network interaction as a cooperative game and we show that by defining incentives for cooperation and disincentives against defecting on service guarantees, repeated interaction sustains cooperation.     

虚拟时间反转镜啁啾率键控的水声多用户通信

针对频率选择性衰落严重的浅海信道多用户通信问题,提出了一种虚拟时间反转镜(VTRM,Virtual Time Reversal Mirror)啁啾率键控(CRK,Chirp-Rate Keying)的水声多用户通信方式。该算法采用啁啾信号作为载波,接收端利用啁啾信号中心频率与分数阶傅里叶变换域的关系还原出原始信息,并使用虚拟时间反转镜技术减少了多径环境对系统的影响,采用啁啾调频率分集作为多址接入方式。在10 kHz的带宽内,可支持4个用户进行可靠通信,每用户通信速率425 bps。水池及海洋实验结果表明:该算法能够有效地传输数据,并且误码率在10-3数量级以下。      

Performance Analysis and Optimization of a Cooperative Transmission Protocol in NOMA-Assisted Cognitive Radio Networks with Discrete Energy Harvesting

In this paper, we propose a spectrum-sharing protocol for a cooperative cognitive radio network based on non-orthogonal multiple access technology, where the base station (BS) transmits the superimposed signal to the primary user and secondary user with/without the assistance of a relay station (RS) by adopting the decode-and-forward technique. RS performs discrete-time energy harvesting for opportunistically cooperative transmission. If the RS harvests sufficient energy, the system performs cooperative transmission; otherwise, the system performs direct transmission. Moreover, the outage probabilities and outage capacities of both primary and secondary systems are analyzed, and the corresponding closed-form expressions are derived. In addition, one optimization problem is formulated, where our objective is to maximize the energy efficiency of the secondary system while ensuring that of the primary system exceeds or equals a threshold value. A joint optimization algorithm of power allocation at BS and RS is considered to solve the optimization problem and to realize a mutual improvement in the performance of energy efficiency for both the primary and secondary systems. The simulation results demonstrate the validity of the analysis results and prove that the proposed transmission scheme has a higher energy efficiency than the direct transmission scheme and the transmission scheme with simultaneous wireless information and power transfer technology.     

On the security of K-tier heterogeneous cellular networks

Motivated by recent developments in heterogeneous cellular networks and physical-layer security, we aim to characterize the fundamental limits of secure communication in networks. Based on a general model in which both transmitters and receivers are randomly scattered in space, we model the locations of K-tier base stations, users, and potential eavesdroppers as independent two-dimensional Poisson point processes. Using the proposed model, we analyze the achievable secrecy rates for an arbitrarily located mobile user. Assuming that the cell selection is based on achievable-secrecy-rate threshold, we obtain approximations for: (a) secrecy coverage probability and (b) average secrecy load per tier. We also investigate how the network performance is affected by secrecy rate threshold, eavesdropper density, and different access strategies are analyzed, respectively. Finally, our theoretical claims are confirmed by the numerical results.     

Performance analysis of PDMA-based hybrid satellite–terrestrial networks with decode-and-forward relaying

As one of the most promising non-orthogonal multiple access (NOMA) technologies in 5G communication, pattern division multiple access (PDMA) has theoretically higher spectrum utilization and a larger communication capacity than conventional orthogonal multiple access (OMA) technologies. In this letter, PDMA is applied to hybrid satellite–terrestrial networks with decode-and-forward (DF) relaying in the downlink, and an in-depth study on the performance of outage probability (OP), ergodic capacity and system throughput is performed. For a more comprehensive analysis, hybrid satellite–terrestrial relay networks (HSTRNs) with conventional OMA technology are used in the comparative analysis. Analysis and simulation results show that PDMA-based hybrid satellite–terrestrial relay networks outperform the other system in terms of OP and ergodic capacity.     

An effective approach for initial access in 5G-millimeter wave-based Vehicle to Everything (V2X) communication using Improved Genetic Algorithm

The process of establishing a directional communication link between the vehicle (VE) on the road and a roadside unit (RSU) is known as initial access process in 5G-millimeter wave(mmWave)-Vehicle to Everything (V2X) communications. Initial access is a tricky problem because substantial interruption or delay can be experienced where the RSU and the VE tries to discover the suitable beam alignment for establishing a direct communication link. Thus, it is very important to resolve this initial access problem in an effective way. Moreover, with the popularity of 5G-mmWave based V2X communication many researchers are trying to address this problem. Therefore, in this paper, we will be presenting a novel beam refinement technique that uses Improved Genetic Algorithm which is quite useful when the system is comprised of large number of antenna arrays. In this work we have considered that RSU and VE are equipped with multiple input multiple output (MIMO) antenna system. Proposed improved genetic algorithm includes some key improvements in terms of selection procedure, elitism, crossover and mutation operations. The effectiveness of the proposed work is investigated in terms of Capacity achieved vs number of transmit and receive antennas at RSU and VE, codebook size, outage probability and total transmitted power. Moreover, a detail analysis has been performed with previous state of the art works in terms of key performance metrics like: capacity achieved by 5G-V2X system, outage probability, and total transmitted power utilization. The proposed work has shown improved beam refinement by solving the initial access problem effectively.     

Joint user grouping and power allocation in VLC-NOMA system

With the energy consumption of wireless networks increasing, visible light communication (VLC) has been regarded as a promising technology to realize energy conservation. Due to the massive terminals access and increased traffic demand, the implementation of non-orthogonal multiple access (NOMA) technology in VLC networks has become an inevitable trend. In this paper, we aim to maximize the energy efficiency in VLC-NOMA networks. Assuming perfect knowledge of the channel state information of user equipment, the energy efficiency maximization problem is formulated as a mixed integer nonlinear programming problem. To solve this problem, the joint user grouping and power allocation (JUGPA) is proposed including user grouping and power allocation. In user grouping phase, we utilize the average of channel gain among all user equipment and propose a dynamic user grouping algorithm with low complexity. The proposed scheme exploits the channel gain differences among users and divides them into multiple groups. In power allocation phase, we proposed a power allocation algorithm for maximizing the energy efficiency for a given NOMA group. Thanks to the objective function is fraction form and non-convex, we firstly transform it to difference form and convex function. Then, we derive the closed-form optimal power allocation expression that maximizes the energy efficiency by Dinkelbach method and Lagrange dual decomposition method. Simulation results show that the JUGPA can effectively improve energy efficiency of the VLC-NOMA networks.     

Efficient user access and lamp selection in LED-based visible light communication network

We present a network-level signaling mechanism for user access and service setup in light emitting diode(LED)-based visible light communication(VLC) networks and define the corresponding signaling messages.In this mechanism,lamp selection is an important step for realizing flexible user access and efficient resource allocation.Two basic selection schemes are proposed,and an enhanced bandwidth-based scheme is presented.Simulation results show the different advantages among these schemes.     

Cognitive radio network with secrecy and interference constraints

In this paper, we investigate the physical-layer security of a secure communication in single-input multiple-output (SIMO) cognitive radio networks (CRNs) in the presence of two eavesdroppers. In particular, both primary user (PU) and secondary user (SU) share the same spectrum, but they face with different eavesdroppers who are equipped with multiple antennas. In order to protect the PU communication from the interference of the SU and the risks of eavesdropping, the SU must have a reasonable adaptive transmission power which is set on the basis of channel state information, interference and security constraints of the PU. Accordingly, an upper bound and lower bound for the SU transmission power are derived. Furthermore, a power allocation policy, which is calculated on the convex combination of the upper and lower bound of the SU transmission power, is proposed. On this basis, we investigate the impact of the PU transmission power and channel mean gains on the security and system performance of the SU. Closed-form expressions for the outage probability, probability of non-zero secrecy capacity, and secrecy outage probability are obtained. Interestingly, our results show that the strong channel mean gain of the PU transmitter to the PU’s eavesdropper in the primary network can enhance the SU performance.     

Network sum-rate maximization via joint power allocation and antenna selection for clustered downlink/uplink NOMA networks

This paper considers the problem of joint power allocation and antenna selection (J-PA-AS) for downlink (DL) and uplink (UL) clustered non-orthogonal multiple-access (NOMA) networks. In particular, the goal is to perform antenna selection for each user cluster and allocate transmit power to its users so as to maximize the network sum-rate in the DL and UL directions, while satisfying quality-of-service (QoS) requirements. The formulated problem happens to be non-convex and NP-hard, and thus, there is no systematic or computationally-efficient approach to solve it directly. In turn, a low-complexity two-stage algorithm is proposed. Specifically, the first stage optimally solves the sum-rate maximizing power allocation for each (antenna, user cluster) pair. After that, antenna selection is optimally solved in polynomial-time complexity via the Kuhn–Munkres with backtracking (KMB) algorithm. Extensive simulation results are provided to validate the proposed algorithm, which is shown to efficiently yield the optimal network sum-rate in each link direction, in comparison to the optimal J-PA-AS scheme (solved via a global optimization package), and superior to other benchmark schemes. Light is also shed on the impact of spatial-diversity on the network sum-rate, where it is shown that the greater the number of base-station antennas is, the higher the network sum-rate, and the lower the outage events. Additionally, the significance of decoupling antenna selection in each link direction on the network sum-rate is highlighted. Lastly, the cases of imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) have been investigated, where it is demonstrated that spatial-diversity gains reduce the adverse effects of imperfect CSI and SIC on the network sum-rate.     

Relay selection scheme based on deep reinforcement learning in wireless sensor networks

Cooperative communication technology has realized the enhancement in the wireless communication system’s spectrum utilization rate without resorting to any additional equipment; additionally, it ensures system reliability in transmission, increasingly becoming a research focus within the sphere of wireless sensor networks (WSNs). Since the selection of relay is crucial to cooperative communication technology, this paper proposes two different relay selection schemes subject to deep reinforcement learning (DRL), in response to the issues in WSNs with relay selection in cooperative communications, which can be summarized as the Deep-Q-Network Based Relay Selection Scheme (DQN-RSS), as well as the Proximal Policy Optimization Based Relay Selection Scheme (PPO-RSS); it further compared the commonly used Q-learning relay selection scheme (Q-RSS) with random relay selection scheme. First, the cooperative communication process in WSNs is modeled as a Markov decision process, and DRL algorithm is trained in accordance with the outage probability, as well as mutual information (MI). Under the condition of unknown instantaneous channel state information (CSI), the best relay is adaptively selected from multiple candidate relays. Thereafter, in view of the slow convergence speed of Q-RSS in high-dimensional state space, the DRL algorithm is used to accelerate the convergence. In particular, we employ DRL algorithm to deal with high-dimensional state space while speeding up learning. The experimental results reveal that under the same conditions, the random relay selection scheme always has the worst performance. And compared to Q-RSS, the two relay selection schemes designed in this paper greatly reduce the number of iterations and speed up the convergence speed, thereby reducing the computational complexity and overhead of the source node selecting the best relay strategy. In addition, the two relay selection schemes designed and raised in this paper are featured by lower-level outage probability with lower-level energy consumption and larger system capacity. In particular, PPO-RSS has higher reliability and practicability.