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Growing computation requirements in emerging intelligently connected vehicle (ICV) networks has posed a big challenge for vehicles to process computation-intensive tasks on-board, and thus computation partition and offloading plays a critical role. Owing to broadcast nature of wireless channels, confidential messages without proper security protection in ICVs may be easily exposed to malicious users. In this paper, a mobile crowdsourcing (MCS) based mobile crowd computing framework for ICV networks is proposed, where multiple vehicles act as workers to provide computing services for end user (EU). In particular, artificial noise (AN) assisted physical (PHY) layer security approaches are used to enhance the security in offloading links. Ergodic secrecy rates in different offloading phases in time-varying channels are derived. In addition, an incentive-driven mechanism is introduced to encourage workers to share their idle computing resources, and an optimization problem is formulated to minimize the overall price paid for computing tasks, subject to energy consumption and delay constraints. Finally, Monte Carlo simulations verify the analysis on the ergodic secrecy rates. [ABSTRACT FROM AUTHOR]

In wireless network virtualization processes, load imbalance in substrate nodes and substrate links may significantly reduce the number of virtual network requests served by a substrate network. In addition, load imbalance also impairs resource utilization and increases embedding cost of the virtual networks. To tackle these issues, this work proposes a re-embedding scheme for virtual node (RSVN). Based on the load condition of a substrate network, RSVN first determines whether to start re-embedding procedure or not, and then selects proper re-embedded virtual nodes according to their re-embedding factors. Next, RSVN re-embeds the selected virtual nodes to balance the loads of a wireless substrate network. The extensive simulation results showed that RSVN improves the acceptance ratio of virtual network requests, increases the resource utilization of substrate networks, and reduces the average embedding cost of the entire virtual networks. [ABSTRACT FROM AUTHOR]

Vehicle-to-vehicle (V2V) underlay cellular communication plays an important role in the fifth-generation (5G) and beyond communications and its security issues attract a lot of attention. Physical layer security, which explores channel characteristics to protect wireless communications, is very attractive due to its low latency. This paper focuses on secrecy capacity maximization in V2V underlay cellular communications, where the secrecy capacity of vehicular user equipments (VUEs) is optimized first, followed by sum proportional fairness function optimization for both VUEs and cellular user equipments (CUEs). The optimization problems can be solved in two sub-problems, e.g., power and subcarrier allocation problems. The power allocation can be done with the first strategy using a one-dimensional searching algorithm, whereas a genetic algorithm (GA) is used to solve the second optimization problem. In addition, as VUEs share uplink resources with CUEs, the co-channel interference exists and the subcarrier allocation sub-problem is a 3D search problem, which is solved by a two-step iterative Hungarian algorithm (IHA). Finally, simulations are performed to validate the performance of the proposed schemes. [ABSTRACT FROM AUTHOR]

In device-to-device (D2D) underlaying vehicle-to-vehicle (V2V) communications, radio resource blocks (RBs) are allocated to primary cellular users, which yields sub-optimal rates and a long access latency for secondary D2D-enabled vehicles. This work investigates a joint radio resource and power management (RRPM) problem for secure cellular underlaying V2V communications, where cellular users and vehicles have the same priority, giving vehicles more opportunities to access the RBs. Specifically, we aim to maximize secrecy rates of V2V channels under the condition that eavesdropper has an adaptive receiving detection vector to maximize the received signal-to-interference-plus-noise ratio (SINR) from vehicles. For a single pair of a vehicle and a cellular user, the closed-form optimal power allocation expressions are derived for the interference-limited scenario and the noise-limited scenario, respectively. Moreover, for multiple pairs of vehicles and cellular users, a 3-partite hypergraph based 3-dimensional matching approach is proposed to solve a mixed-integer and non-convex problem, which achieves a near-optimal result with an $O(n^4)$ time complexity. Simulations in different scenarios show that the secrecy rate of the proposed scheme can be improved by 50% if compared to existing schemes. [ABSTRACT FROM AUTHOR]

Broadband satellite services, such as satellite- based Internet of Things and cloud services, have attracted much attention recently. To achieve the goal of anytime and anywhere high-speed satellite communications, there is a big demand for satellite spectrum. The push for improving spectrum efficiency in satellite systems has been an important issue, mainly from the perspectives of spectrum reuse and dynamic spectrum access. In this article, we investigate the feasibility of improving satellite spectrum efficiency from another viewpoint, that is, introducing a game-theoretical approach, including spectrum pricing and auction, for satellite spectrum dynamic allocation. Although spectrum pricing and auction have been suggested for terrestrial networks, this work aims to establish a market-inspired model to address distinct characteristic features of satellite communications. In particular, we propose a generic satellite spectrum pricing scheme, taking into account three factors: spectrum reuse in multi-beam satellite systems (MSS), where intercell interference should be mitigated; the heterogeneous nature of satellite transmission links in different spectrum bands, in which a differential pricing scheme is desirable; and predictable and periodic satellite switching from one non-GEO satellite link to another, where traffic-oriented pricing should be used. Finally, the challenges and future works in satellite mobile communications are identified. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is a promising technique to enhance the spectrum efficiency of a cellular network. However, one of the major challenges in D2D communications is to select appropriate communication modes for all possible D2D pairs in a D2D network. In this paper, we study the mode selection problems in a multi-mode and multi-pair D2D network, where the eNB can assign one of the three D2D communication modes (including local route mode, direct D2D mode, and relay D2D mode) to each D2D pair. To achieve the maximal cell-wise capacity, an optimal mode selection algorithm is proposed in this paper with the aim to share cellular users’ uplink channels. The optimal mode selection problem can be modeled by an integer programming problem. Particularly, in a fully loaded cell, we discover that the optimal mode selection problem can be solved using a linear programming algorithm with a significantly reduced complexity. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a promising candidate technology for 5G cellular systems. In this paper, design and optimization of scheduling and NOMA algorithms is investigated. The impact of power allocation for NOMA systems with round-robin scheduling is analyzed. A statistic model is developed for network performance analysis of joint scheduling of spectrum resource and power for NOMA algorithms. Proportional fairness scheduling for NOMA is proposed with a two-step approach, with objective of achieving high throughput and user fairness with low computational complexity. In the first step, an optimal power allocation strategy is developed with an objective of maximizing weighted sum rate. In the second step, three fast and scalable scheduling and user pairing algorithms with QoS guarantee are proposed, in which only a few user pairs are checked for NOMA multiplex. The algorithms are extended to the cases with imperfect channel state estimation and more than two users being multiplexed over one resource block. Numerical results show that the proposed algorithms are faster and more scalable than the existing algorithms, and maintain a higher throughput gain than orthogonal multiple access. [ABSTRACT FROM AUTHOR]

The fifth generation communication systems aim to achieve higher capacity and more user access. Full-duplex (FD) communication can achieve twice the spectral efficiency of the conventional half-duplex systems under ideal conditions, and FD relay is a common application of FD technology. In this paper, we propose a modified signal-to-leakage-and-noise ratio (SLNR) precoding scheme for the FD multiuser MIMO relay system, in which the self-interference (SI) of the relay is considered. In contrast to block diagonalization (BD) precoding and zero-forcing (ZF) precoding, SLNR precoding does not have an antenna dimension limitation, which enables it to support more antennas and users. In addition, SLNR precoding balances the interference and noise of a system, whereas BD precoding and ZF precoding only consider the interference. The numerical results show that the proposed SLNR precoding scheme can achieve better performance with low SI. With a reasonable sum capacity loss, the SLNR precoding scheme provides more user access despite a limited number of transmit antennas. [ABSTRACT FROM AUTHOR]

The articles in this special section provide a comprehensive overview on the recent development of the intelligent radio. The advances in wireless communications have continuously been pushing the limit of radio technologies. Nowadays, radio networks can provide extremely high data rate, ultra-low latency, and high reliability to serve communication needs of sectors that could not be imagined before. However, radio technologies have become highly complex and call for new solutions. The recent advances in artificial intelligence (AI), including machine learning (ML), data mining, and big data analysis, bring significant promise for addressing hard problems in radio networks. It has been the increasing trend to move the intelligence beyond the spectrum access, which is primarily targeted by cognitive radio, to address various challenges in radio networks, including, but not limited to, channel modeling, modulation, beamforming, radio resource allocation, and network management. Radio technologies are on the way evolving to the intelligent radio, in which AI/ML frameworks and algorithms are applied to learn from environments and explore hidden characteristics of networks for new capacity, performance, and services. We believe the intelligent radio will be the prominent feature of next generation wireless networks. It calls for interdisciplinary research to integrate the advances in AI/ML, communications, computing, and cloud technologies. Both theoretical and applied breakthroughs are expected in this new area. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communications have been proposed to improve spectrum and energy efficiency of cellular networks. One of the most critical issues in D2D networks is the selection of the communication modes for multiple D2D pairs in a cell. In this article, relay-assisted mode, in addition to basic cellular and direct D2D modes, is taken into account to improve the link performance of D2D pairs. We investigate the optimal mode selection schemes to maximize cell-wise capacity based on the reuse of cellular uplink channels in different multi-pair D2D communication scenarios. We discover that the optimal mode selection problem can be transformed into a linear programming problem with polynomial computational complexity. The exact analytical solutions for optimal mode selection are derived and can be useful to cellular operators to maximize the cellwise capacity. [ABSTRACT FROM AUTHOR]

Virtual multiple-input multiple-output (V-MIMO) is an effective way to achieve a diversity gain in uplink channels. A well-designed user pairing scheme in V-MIMO systems can exploit multiuser diversity to yield a significant performance gain. This paper investigates joint user pairing and power allocation problems in V-MIMO systems to maximize energy efficiency (EE) while guaranteeing spectrum efficiency (SE) under a total power consumption constraint. We formulate such a joint problem as a complex nonlinear optimization problem, which can be solved in two steps in $M\times K$ V-MIMO systems, i.e., power allocation with known user group and joint user pairing and power allocation. A multi-level water-filling method is used, and an iterative algorithm based on the outputs from the first step is employed in the second step. Finally, we propose a joint user pairing and power allocation scheme to find a user group that can achieve the maximum EE with power allocation. Simulation results and performance comparisons illustrate improved performance of the proposed scheme. Compared to the existing user pairing approaches, the proposed scheme achieves a significantly high EE gain while guaranteeing SE of a V-MIMO system. [ABSTRACT FROM AUTHOR]

This paper studies the issues on wireless networks virtualization in terms of two important performance metrics, i.e., energy efficiency (EE) and delay. Different from existing works on physical layer, we aim to achieve a good tradeoff between EE and delay in wireless networks virtualization using cross-layer stochastic optimization approach. In particular, we formulate a cross-layer problem using fractional programming and Lyapunov optimization method. The EE and delay tradeoff solution is given explicitly by deriving their analytical bounds that are verified by simulation results. [ABSTRACT FROM PUBLISHER]

Providing diverse and strict quality-of-service (QoS) guarantees is one of the most important requirements in machine-to-machine (M2M) communications, which is particularly need for appropriate resource allocation for a large number of M2M devices. To efficiently allocate resource blocks (RBs) for M2M devices while satisfying QoS requirements, we propose group-based M2M communications, in which M2M devices are clustered based on their wireless transmission protocols, their QoS characteristics, and their requirements. To perform joint RB and power allocation in SC-FDMA-based LTE-A networks, we formulate a sum-throughput maximization problem, while respecting all the constraints associated with SC-FDMA scheme, as well as QoS requirements in M2M devices. The constraints in uplink SC-FDMA air interface in LTE-A networks complicate the resource allocation problem. We solve the resource allocation problem by first transforming it into a binary integer programming problem and then formulate a dual problem using the Lagrange duality theory. Numerical results show that the proposed algorithm outperforms traditional Greedy algorithm in terms of throughput maximization while satisfying QoS requirements, and its performance is close to the optimal design. [ABSTRACT FROM PUBLISHER]

D2D communication is an effective approach for traffic offloading in future cellular systems. However, only using direct D2D communication is not enough to fully exploit the advantages of D2D communications due to long distances or poor link quality between source and destination D2D UEs. This work aims to enhance the traffic offloading capacity of cellular-assisted D2D communication, taking into account D2D relays. In particular, we propose a unified model for D2D communication, in which two other D2D communication modes, that is, relay-assisted D2D and local route based D2D, are also supported by UEs in addition to direct D2D mode. We design radio protocol architectures for the three D2D modes and propose an eNB scheduling algorithm, which enables the eNB to perform joint scheduling on mode selection, radio resource allocation, and power coordination for all D2D pairs in each subframe. The effectiveness of our designs is validated by simulations. [ABSTRACT FROM PUBLISHER]

Because of random deployment patterns of femtocells, interference scenarios in a heterogeneous cellular network can be very complicated because of its changing network topology. Especially when each eNodeB occupies a fixed bandwidth, interference management becomes much more difficult. The benefit of dynamic management for local resource optimation is limited. Recently, resource virtualization has been proposed as a dynamic resource management scheme to optimize network performance. In fact, resource virtualization is viewed as a more flexible model, in which mobile network service providers can control physical resources in a global scope. This paper presents a joint resource virtualization and allocation scheme for its applications in heterogeneous macro-femto-cellular networks. The proposed scheme involves two major processes. First, it virtualizes physical resources as logical resources. Second, it carries out logical resource allocation optimization globally and aggregates logical and physical resources for resource allocation. The proposed scheme takes into account spectrum reuse and frequency domain interference jointly in order to achieve a high spectral efficiency and provide rate-on-demand services to all users. Simulation results demonstrate the effectiveness of the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This letter studies power allocation for simultaneous wireless information and power transfer in downlink coordinated multipoint (CoMP) systems. A central unit (CU) conveys data and channel information to multiple radio remote units (RRUs) via a capacity-limited backhaul. We provide a dual polarized (DP) antenna-based double side signal splitting method. Specifically, signals are split up into information decoding part [user equipment (UE) data transmitted from CU] and energy harvesting part (deterministic data created at RRUs), which are transmitted and received via vertical and horizontal polarizations of DP antennas, respectively. Normal beamformers (such as zero forcing and maximum ratio transmission) are used to reduce complexity. The problem is to maximize the sum rate satisfying per-UE received power, per-backhaul-link capacity, and per-RRU power transmission constraints. The results are provided to verify the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Due to the popularity of mobile user devices, D2D communications become a key technology to improve local services in the next generation cellular networks. However, the benefits count on efficient radio resource allocation between cellular links and D2D links, which poses critical challenges. By exploiting the network knowledge extracted from underlying mobile social networks (MSNs), this article proposes a socially aware D2D communication scheme to improve the spectrum and energy efficiency in cellular networks. First, by jointly considering the social and network knowledge, we propose a cluster formation scheme to categorize a group of UEs into multiple D2D clusters. Within each cluster, highly spectrum-efficient D2D multicasting (from the cluster head, i.e., a D2D transmitter UE to multiple cluster member UEs) is enabled. Next, a half-duplex scheme and a full-duplex scheme are proposed to coordinate the channel sharing between the cellular links and the D2D links. Simulation results show that the proposed schemes can form D2D clusters rationally among a group of UEs, and therefore largely increase the spectrum and energy efficiency of the network. [ABSTRACT FROM PUBLISHER]

The use of cognitive-radio(CR)-enabled femtocell is regarded as a promising technique in wireless communications, and many studies have been reported on its resource allocation and interference management. However, fairness and spectrum sensing errors were ignored in most of the existing studies. In this paper, we propose a resource allocation scheme for orthogonal frequency division multiple access (OFDMA)-based cognitive femtocells. The target is to maximize the total capacity of all femtocell users (FUs) under given quality-of-service (QoS) and cotier/cross-tier interference constraints with imperfect channel sensing. To achieve the fairness among FUs, the minimum and maximum numbers of subchannels occupied by each user are considered. First, the subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing and applying cotier interference constraints, which is finally solved using a dual decomposition method. Based on the obtained solution, an iterative subchannel and power allocation algorithm is proposed. The effectiveness of the proposed algorithm in terms of capacity and fairness compared with the existing schemes is verified by simulations. [ABSTRACT FROM AUTHOR]

Cognitive radio technology can significantly improve spectrum utilization efficiency via enabling secondary users to access licensed spectrum dynamically without harmful interference to primary users. Most existing works on cognitive radio networks were focused on enhancing spectrum efficiency using spectrum sensing, spectrum sharing, and network deployment schemes. However, the energy efficiency issue was largely ignored. Thus, the dilemma between high energy consumption by fast-growing wireless communications and fewer global energy resources has motivated research on energy-efficient cognitive radio networks for enhancing spectrum efficiency and energy efficiency simultaneously. In this article, we overview energyefficient non-cooperative cognitive radio networks from the micro, meso, and macro perspectives, where the micro view means how to design energy-efficient spectrum sensing algorithms for each individual secondary user, the meso view means how to coordinate non-cooperative secondary users to share spectrum efficiently, and the macro view means how to deploy cognitive radio networks in an energy-efficient approach. [ABSTRACT FROM AUTHOR]

Traditional \alpha-fairness resource allocation in wireless multimedia communications assumes that the quality of experience (QoE) model (or utility function) of each user is available to the base station (BS), which may not be valid in many practical cases. In this paper, we consider a blind scenario where the BS has no knowledge of the underlying QoE model. Generally, this consideration raises two fundamental questions. Is it possible to set the fairness parameter \alpha in a precisely mathematical manner? If so, is it possible to implement a specific \alpha-fairness resource allocation scheme online? In this work, we will give positive answers to both questions. First, we characterize the tradeoff between the performance and fairness by providing an upper bound of the performance loss resulting from employing \alpha-fairness scheme. Then, we decompose the \alpha-fairness problem into two subproblems that describe the behaviors of the users and BS and design a bidding game for the reconciliation between the two subproblems. We demonstrate that, although all users behave selfishly, the equilibrium point of the game can realize the \alpha-fairness efficiently, and the convergence time is reasonably short. Furthermore, we present numerical simulation results that confirm the validity of the analytical results. [ABSTRACT FROM PUBLISHER]

Device-to-device (D2D) communications help improve the performance of wireless multicast services in cellular networks via cooperative retransmissions among multicast recipients within a cluster. Resource utilization efficiency should be taken into account in the design of D2D communication systems. To maximize resource efficiency of D2D retransmissions, there is a tradeoff between multichannel diversity and multicast gain. In this paper, by analyzing the relationship between the number of relays and minimal time-frequency resource cost on retransmissions, we derive a closed-form probability density function (pdf) for an optimal number of D2D relays. Motivated by the analysis, we then propose an intracluster D2D retransmission scheme with optimized resource utilization, which can adaptively select the number of cooperative relays performing multicast retransmissions and give an iterative subcluster partition algorithm to enhance retransmission throughput. Exploiting both multichannel diversity and multicast gain, the proposed scheme achieves a significant gain in terms of resource utilization if compared with its counterparts with a fixed number of relays. [ABSTRACT FROM PUBLISHER]