Dense deployment of small base stations (SBSs) within the coverage of macro base station (MBS) has been spotlighted as a promising solution to conserve grid energy in hybrid-energy heterogeneous cellular networks (HCNs), which caters to the rapidly increasing demand of mobile user (MU). However, MUs in the ultradense cellular network experience handover events more frequently than in conventional networks, which results in increased service interruption time and performance degradation due to blockages. In addition, blindly increasing the number of SBSs not only results in an increased cost for network operators but also brings serious system energy consumption and interference. In this article, we propose a joint user association and SBSs configuration scheme for maximizing energy efficiency (EE) in hybrid-energy HCNs. Specially, an association model with dual connectivity for MUs where they are connected simultaneously with SBSs and MBSs is first proposed to reduce frequent handover, which is also presented to preferentially select SBSs that can provide data transmission for MUs under the user association constraints according to the maximum system EE. And then the ratio between the number of SBSs and the number of MUs for SBSs configuration is analyzed to reduce interference and energy consumption under the tidal effect of HCNs. Furthermore, the EE utility function of joint user association and SBSs configuration is extended, and the Dinkelbach and Lagrangian algorithms are jointly optimized to solve the EE utility function. Finally, numerical simulation results are provided to demonstrate the feasibility of the proposed scheme. It is shown that the proposed scheme outperforms other schemes and can also maximize the EE in hybrid-energy HCNs.

As a novel paradigm, Vehicular Edge Computing (VEC) can effectively support computation-intensive or delay-sensitive applications in the Internet of Vehicles era. Computation offloading and resource management strategies are key technologies that directly determine the system cost in VEC networks. However, due to vehicle mobility and stochastic arrival computation tasks, designing an optimal offloading and resource allocation policy is extremely challenging. To solve this issue, a deep reinforcement learning-based intelligent offloading and power allocation scheme is proposed for minimizing the total delay cost and energy consumption in dynamic heterogeneous VEC networks. Specifically, we first construct an end-edge-cloud offloading model in a bidirectional road scenario, taking into account stochastic task arrival, time-varying channel conditions, and vehicle mobility. With the objective of minimizing the long-term total cost composed of the energy consumption and task delay, the Markov Decision Process (MDP) can be employed to solve such optimization problems. Moreover, considering the high-dimensional continuity of the action space and the dynamics of task generation, we propose a deep deterministic policy gradient-based adaptive computation offloading and power allocation (DDPG-ACOPA) algorithm to solve the formulated MDP problem. Extensive simulation results demonstrate that the proposed DDPG-ACOPA algorithm performs better in the dynamic heterogeneous VEC environment, significantly outperforming the other four baseline schemes.

The cellular vehicle-to-everything (C-V2X) networks support diverse kinds of services, such as traffic management, road safety, and sharing data. However, the safety issues cannot be ignored in the process of information transmission. In this article, a joint clustering and blockchain scheme is proposed for real-time information security transmission to prevent some vehicles from sending malicious messages to disrupt the traffic order at the crossroads in C-V2X networks. In this scheme, the dynamic stability of the cluster is maintained by updating the trust value of the vehicle nodes, which can improve the real time and accuracy of the information transmission. The modified Webster algorithm is presented to divert the traffic flow so as to reduce the traffic jams at the crossroads. Meanwhile, the blockchain technology is utilized to establish a vehicle trust management mechanism in C-V2X, which can avoid malicious tampering of vehicle information during information sharing and ensure the safety of vehicle information communication. The simulation results of the Veins simulation platform are provided to demonstrate the effectiveness of the proposed algorithm and verify that the proposed scheme can guarantee the security of real-time information transmission.

The technology of static user-centric virtual cell (VC) has been designed in the fifth-generation (5G) ultra-dense networks (UDNs) for alleviating both frequent handover and inter-cell interference. However, to provide the user-centric services, the fairness of resource management must be involved when the common vehicular-to-X (V2X) messages are multicast to the vehicle groups. In this paper, a dynamic user-centric virtual cell (DUVC) scheme is proposed for updating adaptively the VC through the mobile tracking of the vehicles. Furthermore, an approximation algorithm is proposed for solving the max-min-fair problem of resource management in V2X communication in order to better support V2X services throughout the service VC. Numerical results are provided for demonstrating that the proposed DUVC scheme is suitable for implementing in the V2X communication. Finally, the proposed algorithm is shown to outperform three other existing algorithms in terms of fairness of resource management.

In vehicle-to-everything (V2X) communication network, cellular device-to-device (D2D) communication can not only improve data rate and spectral utilization but also reduce the traffic load and power consumption. However, cellular D2D-based V2X technology has also a potential deficiency to meet various requirements of V2X communication, particularly in energy efficiency (EE). Power allocation provides an important approach to optimize the EE. In this paper, a new approach for power allocation with EE optimization (EEO) is proposed in cellular D2D-based V2X communication network. The mathematical framework of the new approach is formulated and proved and an algorithm is also proposed. Numerical simulation results are provided to demonstrate the feasibility of the proposed algorithm and the superiority over existing well-known algorithms.

Both clustering and cluster-head vehicles (CHVs) cooperative communication have been employed for reducing traffic congestion to improve road traffic efficiency in cooperative vehicular networks. In this paper, an iterative optimization k-means clustering algorithm with lower complexity than previous algorithms is proposed. It can automatically generate multiple clusters according to the number of vehicles and quickly find the CHVs by avoiding delays caused by complex calculations. Moreover, a new optimization power allocation strategy with bidirectional incremental hybrid decode-amplify-forward protocol focusing on reducing the total power consumption of CHVs is proposed. This strategy can set the signal to noise ratio threshold as the critical point for selecting dynamically the bidirectional incremental amplify-and-forward or decoding and forwarding protocol with a lower outage probability to transmit information. Thus, the proposed power allocation strategy is capable of minimizing the total transmission power while ensuring a lower outage probability than previous approaches. Finally, the numerical results are provided for corroborating the theoretical results and demonstrate the efficiency of the proposed approaches. Note that through the numerical simulations we can find the critical point of the outage probability for the aforementioned protocols under different relay locations. This assists vehicles to select “relays” with the optimal cooperative position for vehicular cooperative communication system.

The device-to-device (D2D) communications mode has been regarded as an effective technique for solving/relieving the contradiction between the exponentially increased data traffic requirements of mobile customers and the essence of scarcity of radio resources in wireless communication networks. In the presence of unfavorable direct communication links between D2D peers, cooperative relays may play an important role in enhancing both reliability and flexibility of D2D communications. Aiming at maximizing the sum throughout of the network with a low computation complexity, a new scheme, which jointly considers a number of critical aspects, such as power control, interference limit based on the location information, optimal relay selection based on delineated area, and optimal link selection based range division, etc., is proposed in this paper. Numerical results reveal that the proposed scheme is capable of substantially improving the system's performance compared with either the existing brute-force technique or the area-division scheme. Furthermore, the proposed scheme also exhibits its advantages over the existing techniques in terms of computational complexity.

This paper provides symbol-error-rate (SER) performance analysis and minimum power allocation for multi-source multi-destination cooperative vehicular networks using the hybrid decode-amplify-forward (HDAF) cooperative relaying protocol. Previous studies of power allocation minimize the outage probability subject to a total power constraint. Our approach aims to minimize the power allocation in order to maintain the SER below a specific threshold and thus it achieves lower power consumption. Numerical tests show that HDAF has significantly reduced SER compared with the forward strategies of amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, the power consumption in our proposed approach is much less than that in AF and DF.

In this paper, a novel adequate and concise information extraction approach is explored to provide a promising alternative for manifesting the intrinsic structure of the cyclostationary signals, such as communication signals. A novel graph-based signal representation is proposed to interpret the spectral correlation function into a graph and its adjacency matrix. This graph can represent the proposed adequate and concise information about the communication signals in practice. A typical application, namely modulation classification, can be implemented using our proposed new graph-based approach. According to Monte Carlo simulation results, the proposed graph-based modulation classification method leads to the promising performance in both additive noise channels and difficult multipath fading channels, compared to other existing techniques also using the spectral correlation functions.

In wireless distributed networks, multisource multirelay cooperative techniques can be used to exploit the spatial and temporal diversity gains to increase the performance or reduce the transmission energy consumption, which is very useful for intelligent transport system (ITS) networks. In this paper, we propose a power allocation method to optimize the hybrid decode-amplify-forward cooperative transmission for multisource multirelay ITS networks as a means to reduce the total power consumption while minimizing outage probability. Specifically, we derive closed-form outage probability expressions and present an energy-efficient relay selection method to form an optimal relay set. It is proven that the proposed methods can solve the joint power allocation and relay selection problem under outage probability constraint. Our performance analysis is supplemented by numerical simulation results to illustrate the significant energy savings of the proposed optimal power allocation and the relay selection methods.

In this paper, a novel adaptive coded-modulation scheme, namely M-ary coded modulation, which is spectrally-efficient and can facilitate variable data-rates, is proposed to meet different quality-of-service (QoS) requirements for wireless multimedia applications. An efficient constellation subset selection algorithm incorporated with a novel M-ary coding technique is proposed to generate convolutional codes for different constellation subsets. The studies on the theoretical and practical aspects of our proposed adaptive coded-modulation scheme are presented. Compared with other existing adaptive coded-modulation methods, our scheme offers more flexibilities. According to Monte Carlo simulation results, our new M-ary coded-modulation scheme not only can satisfy the predetermined symbol-error-rate (SER) requirement, but also can significantly enhance the communication quality in terms of bit error rate and throughput.

In this paper, we propose a game-theoretic power control algorithm to minimize the total power consumption in a cooperative communication network that transmits information from multiple sources to a destination via multiple relays to save energy and improve communication performance. Under the amplify-and-forward relaying scheme, the proposed approach not only selects the best source-relay pair but also obtains the optimal transmission power, while satisfying the communication quality-of-service requirements. Using a game-theoretic model, the impacts of the number of relays on the total power consumption are also analyzed. Finally, numerical results are provided to corroborate our theoretical results and demonstrate the performance of the proposed approach.

As a promising theory to recover sparse signal from data samples acquired below the Nyquist rate, compressive sensing (CS) has been drawing pervasive interest in the past decade. In this paper, we explore the compressive sensing potentials for the near-field multiple acoustic-source localization. A novel localization scheme is designed by introducing the optimization of the measurement matrix to enforce the restricted isometry property (RIP) and maximize the signal-to-noise ratio (SNR). Monte Carlos simulations have been carried out to demonstrate the effectiveness of our proposed new scheme. Compared to other existing localization techniques, our scheme exhibits superior performances.

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well.