Full-duplex communication for future wireless networks: Dynamic resource block allocation approach

In this paper, we discuss a full-duplex (FD) communication scenario, where multiple FD user equipments (UEs) share same spectrum resources (or resource blocks) simultaneously. The FD eNodeB deploys digital precoding and successive interference cancellation with optimal ordering algorithm, to allow coexistence of multiple UEs in downlink and uplink, respectively. The sharing of same resource blocks, results in co-channel interference (CCI), in downlink of a UE, from uplink signals of other UEs. To mitigate the interference, a smart antenna approach is adopted. The approach includes using multiple antennas at UEs to form directed beams towards eNodeB and nulls towards other UEs. However, the approach fails when the UEs due to their mobility align themselves in the same direction with respect to the eNodeB (eNB). In this paper, we propose a dynamic resource block allocation (DRBA) algorithm for avoiding CCI due to mobility of UEs, sharing the spectrum resource, in a FD communication scenario. The proposed algorithm shows significant improvement of the quality of service (QoS) of the communication links.     

Traffic resource allocation for complex networks

In this paper, an optimal resource allocation strategy is proposed to enhance traffic dynamics in complex networks. The network resources are the total node packet-delivering capacity and the total link bandwidth. An analytical method is developed to estimate the overall network capacity by using the concept of efficient betweenness (ratio of algorithmic betweenness and local processing capacity). Three network structures (scale-free, small-world, and random networks) and two typical routing protocols (shortest path protocol and efficient routing protocol) are adopted to demonstrate the performance of the proposed strategy. Our results show that the network capacity is reversely proportional to the average path length for a particular routing protocol and the shortest path protocol can achieve the largest network capacity when the proposed resource allocation strategy is adopted.     

Social-aware relay selection and energy-efficient resource allocation for relay-aided D2D communication

How to improve the flexibility of limited communication resources to meet the increasing requirements of data services has become one of the research hotspots of the modern wireless communication network. In this paper, a novel social-aware motivated relay selection method is put forward to allocate the energy efficiency (EE) resources for the device-to-device (D2D) communication. To improve system flexibility, a D2D user is selected to act as a relay named D2D-relay instead of the traditional cellular relay. The optimal relay selection strategy is formulated by searching the maximum trust value that is obtained by assessing the link stability and social connections between two users. Then, the resource allocation problem, which turns out to be a mixed-integer nonlinear fractional programming (MINLFP) problem, is solved by maximizing the total EE under physical constraint and social constraint synthetically. To improve the solution efficiency, a novel iterative algorithm is proposed by integrating the Dinkelbach theory and Lagrange dual decomposition method. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the existing social-blind and social-aware schemes, it significantly improves the probability of successful relay selection and total EE of the D2D pairs.     

Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.     

无标度网络上队列资源分配研究

研究了节点队列资源有限的条件下,无标度网络上的信息流动力学过程,发现了网络由自由流通到拥塞的相变现象,提出了一种基于节点度的队列资源分配模型.模型的核心是使节点i的队列长度与kβi成正比(ki为节点i的度,β为分配参数).仿真结果表明,在网络使用最短路径算法进行信息包传送的条件下,β近似等于1.25时队列资源分配最合理,网络容量最大,且该最佳值与队列总资源多少以及网络的规模无关.     

An overview of simultaneous wireless information and power transfer in massive MIMO networks: A resource allocation perspective

Providing a stable and perpetual source of energy to charge battery-powered wireless communication devices is viewed as a major challenge in wireless communication systems. This challenge leads to the trending research area where radio frequency signals are being exploited for energy harvesting purposes. The technique for achieving this is known as simultaneous wireless information and power transfer (SWIPT). In recent studies on SWIPT, the massive Multiple-Input-Multiple-Output (MIMO) aided energy harvesting has attracted considerable attention from the research community. This can be attributed to the high energy delivery rate of massive MIMO antenna systems due to their capacity to focus transmitted signals in the direction of the intended receivers. However, SWIPT in massive MIMO networks requires an optimal design to achieve a proper balance between different conflicting network objectives. In this article, we aim to discuss various contributions to SWIPT in massive MIMO networks in order to address critical design issues. In particular, we focus on the widely adopted approach to resolving SWIPT-related issues in massive MIMO networks, that is, the resource allocation design. We also extend our discussion to studies dedicated to solving critical design challenges. In this regard, we take into consideration the energy efficiency and security aspect of the system design. Finally, we identify potential areas that can be explored for future research work.     

Personal recommendation via unequal resource allocation on bipartite networks

In this paper, we present a recommendation algorithm based on the resource-allocation progresses on bipartite networks. In this model, each node is assigned an attraction that is proportional to the power of its degree, where the exponent β is an adjustable parameter that controls the configuration of attractions. In the resource-allocation process, each transmitter distributes its each neighbor a fragment of resource that is proportional to the attraction of the neighbor. Based on a benchmark database, we find that decreasing the attractions that the nodes with higher degrees are assigned can further improve the algorithmic accuracy. More significantly, numerical results show that the optimal configuration of attractions subject to accuracy can also generate more diverse and less popular recommendations.     

Multi-channel sensing and resource allocation in energy constrained cognitive radio networks

We consider a cognitive radio network in a multi-channel licensed environment. Secondary user transmits in a channel if the channel is sensed to be vacant. This results in a tradeoff between sensing time and transmission time. When secondary users are energy constrained, energy available for transmission is less if more energy is used in sensing. This gives rise to an energy tradeoff. For multiple primary channels, secondary users must decide appropriate sensing time and transmission power in each channel to maximize average aggregate-bit throughput in each frame duration while ensuring quality-of-service of primary users. Considering time and energy as limited resources, we formulate this problem as a resource allocation problem. Initially a single secondary user scenario is considered and solution is obtained using decomposition and alternating optimization techniques. Later we extend the analysis for the case of multiple secondary users. Simulation results are presented to study effect of channel occupancy, fading and energy availability on performance of proposed method.     

基于博弈论的认知无线电网络跨层资源分配

针对多跳认知无线电网络的多层资源分配问题,提出了协作去耦合方法和跨层联合方法,协作去耦合方法首先单独完成路径选择任务,随后进行信道与功率的博弈分配;跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配,两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则,引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择,设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择,设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能,从促进收敛角度,选择序贯博弈并设计了具体的博弈过程,此外还分析了博弈的纳什均衡,讨论了两种算法的复杂度,仿真结果表明,协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法。     

Structural changes in data communication in wireless sensor networks

Wireless sensor networks are an important technology for making distributed autonomous measures in hostile or inaccessible environments. Among the challenges they pose, the way data travel among them is a relevant issue since their structure is quite dynamic. The operational topology of such devices can often be described by complex networks. In this work, we assess the variation of measures commonly employed in the complex networks literature applied to wireless sensor networks. Four data communication strategies were considered: geometric, random, small-world, and scale-free models, along with the shortest path length measure. The sensitivity of this measure was analyzed with respect to the following perturbations: insertion and removal of nodes in the geometric strategy; and insertion, removal and rewiring of links in the other models. The assessment was performed using the normalized Kullback-Leibler divergence and Hellinger distance quantifiers, both deriving from the Information Theory framework. The results reveal that the shortest path length is sensitive to perturbations.     

Evolutionary public goods games on scale-free networks with unequal payoff allocation mechanism

In this paper, we bring an unequal payoff allocation mechanism into evolutionary public goods game on scale-free networks and focus on the cooperative behavior of the system. The unequal mechanism can be tuned by one parameter α: if α>0, the hub nodes can use its degree advantage to collect more payoff; if α<0, numerous non-hub nodes will obtain more payoff in a single round game. Simulation results show that the cooperation level has a non-trivial dependence on α. For the small enhancement factor r, the cooperator frequency can be promoted by both negative and positive α. For large r, there exists an optimal α that can obtain the highest cooperation level. Our results may sharpen the understanding of the emergence of cooperation induced by the unequal payoff allocation mechanism.     

Deep Q-learning based optimal resource allocation method for energy harvested cognitive radio networks

In this article, we propose a deep Q-learning based algorithm for optimal resource allocation in energy harvested cognitive radio networks (EH-CRN). In EH-CRN, channel resources of primary users (PU) networks are shared with secondary users (SU) and energy harvesting allows nodes of the CRN to acquire energy from the environment for operation sustainability. However, amount of energy harvested from the environment is not fixed and requires dynamic allocation of resources for obtaining optimum network and throughput capacity. In this work, we overcome the limitations of existing Q-learning based resource allocation schemes which are constrained by large state-space systems and have slow convergence. Proposed deep Q-learning based algorithm improves the resource allocation in EH-CRN, while considering quality of service (QoS), energy and interference constraints. Simulation results show that proposed algorithm provide improved convergence and better resource utilization compared to other techniques in literature.     

Bandwidth allocation in cooperative wireless networks: Buffer load analysis and fairness evaluation

In modern cooperative wireless networks, the resource allocation is an issue of major significance. The cooperation of source and relay nodes in wireless networks towards improved performance and robustness requires the application of an efficient bandwidth sharing policy. Moreover, user requirements for multimedia content over wireless links necessitate the support of advanced Quality of Service (QoS) features. In this paper, a novel bandwidth allocation technique for cooperative wireless networks is proposed, which is able to satisfy the increased QoS requirements of network users taking into account both traffic priority and packet buffer load. The performance of the proposed scheme is examined by analyzing the impact of buffer load on bandwidth allocation. Moreover, fairness performance in resource sharing is also studied. The results obtained for the cooperative network scenario employed, are validated by simulations. Evidently, the improved performance achieved by the proposed technique indicates that it can be employed for efficient traffic differentiation. The flexible design architecture of the proposed technique indicates its capability to be integrated into Medium Access Control (MAC) protocols for cooperative wireless networks.     

A multi-timescale resource allocation algorithm based on self-learning for distributed fog radio access networks

Faced with limited network resources, diverse service requirements and complex network structures, how to efficiently allocate resources and improve network performances is an important issue that needs to be addressed in 5G or future 6G networks. In this paper, we propose a multi-timescale collaboration resource allocation algorithm for distributed fog radio access networks (F-RANs) based on self-learning. This algorithm uses a distributed computing architecture for parallel optimization and each optimization model includes large time-scale resource allocation and small time-scale resource scheduling. First, we establish a large time-scale resource allocation model based on long-term average information such as historical bandwidth requirements for each network slice in F-RAN by long short-term memory network (LSTM) to obtain its next period required bandwidth. Then, based on the allocated bandwidth, we establish a resource scheduling model based on short-term instantaneous information such as channel gain by reinforcement learning (RL) which can interact with the environment to realize adaptive resource scheduling. And the cumulative effects of small time-scale resource scheduling will trigger another round large time-scale resource reallocation. Thus, they constitute a self-learning resource allocation closed loop optimization. Simulation results show that compared with other algorithms, the proposed algorithm can significantly improve resource utilization.     

Game theory-based radio resource allocation in NOMA vehicular communication networks supported by UAV

Vehicular communication networks are emerging as a promising technology to provide high-quality internet service such as entertainment for road users via infrastructure-to-vehicle (I2V) communication, and to guarantee road users’ safety via vehicle-to-vehicle (V2V) communication. Some technical issues that impact the performance of these networks are the lack of or poor communication paths between vehicles, and the limitation of radio resources. Unmanned aerial vehicles (UAVs) as promising solutions for supporting vehicular networks could provide communication coverage in hazardous environments and areas with no capacities for installation or maintenance of ground base stations (BSs). Also, non-orthogonal multiple access (NOMA) methods can improve spectral and energy efficiency and thereby allow more users to be connected to the desired network. In this paper, exploring the NOMA, we develop a scheme for optimum resource allocation in presence of a UAV that supports vehicular communications. Resource allocation for this scenario is formulated as a mixed-integer non-linear programming (MINLP) problem. Due to the high complexity of such problems, we propose two low-complexity near-optimal methods. First, we apply difference-of-concave-functions (DC) approximations to solve the problem in an iterative process. Next, we use Stackelberg game-based method for efficient solving, and then, closed-form expressions of optimal power allocations using KKT-conditions are derived. Simulations illustrate the effectiveness of the proposed scheme along with the Stackelberg game-based method.     

Fairness-aware resource allocation for D2D-enabled IoT in NOMA-based cellular networks with mutual successive interference cancellation

Device-to-device (D2D) communications and non-orthogonal multiple access (NOMA) are promising technologies to meet the growing demand for IoT-connected devices. However, they bring about new challenges including the co-channel interference, that can limit the performance improvement. To manage the co-channel interference, we address the problem of joint power allocation and sub-channel assignment for D2D-enabled IoT devices (IoTDs) underlaying a NOMA-based cellular network, in which the successive interference cancellation (SIC) decoding is enabled at the level of IoTDs and cellular user equipment (CUE)to increase the number of connected devices and the capacity. This problem is modeled as a mixed-integer nonconvex optimization problem which includes the concept of fairness with respect to the data rates of IoTDs. To solve the problem, a semi-distributed algorithm is developed, which is of polynomial time complexity. The proposed algorithm leverages the successive convex approximation and a heuristic approach. Evaluation results demonstrate the efficiency of the proposed scheme with respect to the sum rate, fairness, access rate and computational complexity.     

A matching-theoretic approach to resource allocation in D2D-enabled downlink NOMA cellular networks

This paper investigates the resource allocation problem in non-orthogonal multiple-access (NOMA) cellular networks underlaid with OMA-based device-to-device (D2D) communication. This network architecture enjoys the intrinsic features of NOMA and D2D communications; namely, spectral efficiency, massive connectivity, and low-latency. Despite these indispensable features, the combination of NOMA and D2D communications exacerbates the resource allocation problem in cellular networks due to the tight coupling among their constraints and conflict over access to shared resources. The aim of our work is to maximize the downlink network sum-rate, while meeting the minimum rate requirements of the cellular tier and underlay D2D communication, and incorporating interference management as well as other practical constraints. To this end, many-to-many matching and difference-of-convex programming are employed to develop a holistic sub-channels and power allocation algorithmic solution. In addition to analyzing the properties of the proposed solution, its performance is benchmarked against an existing solution and the traditional OMA-based algorithm. The proposed solution demonstrates superiority in terms of network sum-rate, users’ connectivity, minimum rate satisfaction, fairness, and interference management, while maintaining acceptable computational complexity.     

Joint utility-based uplink power and rate allocation in wireless networks: A non-cooperative game theoretic framework

In this paper a novel utility-based game theoretic framework is proposed to address the problem of joint transmission power and rate allocation in the uplink of a cellular wireless network. Initially, each user is associated with a generic utility function, capable of properly expressing and representing mobile user’s degree of satisfaction, in relation to the allocated system’s resources for heterogeneous services with various transmission rates. Then, a Joint Utility-based uplink Power and Rate Allocation (JUPRA) game is formulated, where each user aims selfishly at maximizing his utility-based performance under the imposed physical limitations, and its unique Nash equilibrium is determined with respect to both variables, i.e. uplink transmission power and rate. The JUPRA game’s convergence to its unique Nash equilibrium is proven and a distributed, iterative and low complexity algorithm for computing JUPRA game’s equilibrium is introduced. The performance of the proposed approach is evaluated in detail and its superiority compared to various state of the art approaches is illustrated, while the contribution of each component of the proposed framework in its performance is quantified and analyzed.