Distributed optimization of enhanced intercell interference coordination and resource allocation in heterogeneous networks

In two‐tier heterogeneous networks (HetNets), the cross‐tier interference caused by spectrum sharing between macrocell and small cells poses obstacle to ideal overall network performance. Both enhanced intercell interference coordination (eICIC) at the macrocell base station (MBS) and resource allocation (RA) at small cell base station (SBS) have been considered as efficient approaches to mitigate the cross‐tier interference. In this paper, we propose a distributed optimization framework to jointly optimize the eICIC and RA schemes at different tiers. In specific, an enhanced almost blank subframe (ABS) scheme is performed by the MBS to encourage SBSs to adopt hybrid access policy and admit the offloaded macrocell users (MUs) so as to guarantee their QoS requirements. To model the intricate interaction between two tiers, we formulate the joint optimization problem as a one‐leader multiple‐follower Stackelberg game. We prove the existence of Stackelberg equilibrium (SE) and obtain the optimal strategies for both tiers. Numerical results are presented to show that the proposed framework with hybrid access policy converges to a unique SE and utilities for both tiers is maximized, which creates a win‐win situation.     

Pricing framework for almost blank subframe scheme in two‐tier heterogeneous networks

As a part of enhanced inter‐cell interference coordination (eICIC), almost blank subframe (ABS) is an efficient technique to mitigate the cross‐tier interference of two‐tier heterogeneous networks (HetNets) and enhance overall network performance. However, in small cells with closed subscriber group (CSG) mode, how to motivate small cell base stations (SBSs) to adopt ABS schemes is still one of technical challenges due to the selfish nature of SBSs. In this paper, we propose a pricing framework with ABS scheme that benefit both the macrocell and small cell tier. Within the proposed framework, each SBS with closed access policy performs ABS scheme by muting a portion of the whole frame in time domain, and then dedicating this interference‐free subframe to macrocell users (MUs) for exclusive use. In return, the macrocell base station (MBS) announces the price for these ABSs and offers a certain amount of revenue to each SBS according to the length of its ABS. Furthermore, we extend the small cell performance analysis to the scenario of hybrid access policy, which allow neighboring MUs to access small cells. To investigate the interaction between two tiers, we formulate the proposed framework as a one‐leader multiple‐follower Stackelberg game, which regards the MBS and SBSs as leader and followers, respectively. On the basis of the theoretical analysis, we prove that a unique Stackelberg equilibrium (SE) exists and obtain the optimal strategies for both tiers. Numerical results evaluate the utility performance of both tiers when SE is achieved and verify the validity of the proposed framework.     

Power allocation in small cell networks with full-duplex self-backhauls and massive MIMO

With the dense deployment of small cell networks, low-cost backhaul schemes for small cell base stations (SBSs) have attracted great attentions. Self-backhaul using cellular communication technology is considered as a promising solution. Although some excellent works have been done on self-backhaul in small cell networks, most of them do not consider the recent advances of full-duplex (FD) and massive multiple-input and multiple-output (MIMO) technologies. In this paper, we propose a self-backhaul scheme for small cell networks by combining FD and massive MIMO technologies. In our proposed scheme, the macro base station (MBS) is equipped with massive MIMO antennas, and the SBSs have the FD communication ability. By treating the SBSs as special macro users, we can achieve the simultaneous transmissions of the access link of users and the backhaul link of SBSs in the same frequency. Furthermore, considering the existence of inter-tier and intra-tier interference, we formulate the power allocation problem of the MBS and SBSs as an optimization problem. Because the formulated power allocation problem is a non-convex problem, we transform the original problem into a difference of convex program by successive convex approximation method and variable transformation, and then solve it using a constrained concave convex procedure based iterative algorithm. Finally, extensive simulations are conducted with different system configurations to verify the effectiveness of the proposed scheme.     

Joint backhaul bandwidth and power allocation in heterogeneous cellular networks: A two‐level approach

We investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.     

BiLSTM Based Reinforcement Learning for Resource Allocation and User Association in LTE-U Networks

LTE-unlicensed (LTE-U) technology is a promising innovation to extend the capacity of cellular networks. The primary challenge for LTE-U is the fair coexistence between LTE systems and the incumbent WiFi systems. In this paper, we aim to maximize the long-term average per-user LTE throughput with long-term fairness guarantee by jointly considering resource allocation and user association on the unlicensed spectrum within a prediction window. We first formulate the problem as an NP-hard combinatorial optimization problem, then reformulate it as a non-cooperative game by applying the penalty function method. To solve the game, a novel reinforcement learning approach based on Bi-directional LSTM neural network is proposed, which enables small base stations (SBSs) to predict a sequence of future actions over the next prediction window based on the historical network information. It is shown that the proposed approach can converge to a mixed-strategy Nash equilibrium of the studied game and ensure the long-term fair coexistence between different access technologies. Finally, the effectiveness of the proposed algorithm is demonstrated by numerical simulation.

     

依概率主动窃听下D2D通信的物理层安全研究

本文研究了依概率主动窃听下D2D通信的联合防窃听和抗干扰问题。由于主动窃听者可以依概率选择被动窃听或主动干扰，因此很难对抗。针对主动窃听者攻击方式的动态变化，本文采用稳健博弈学习方法来提高D2D通信的平均安全吞吐量，将一个蜂窝用户(CUE)和多个D2D用户(DUEs)之间的交互建模为一个领导者-多个追随者的斯坦博格博弈，引入了干扰代价机制描述蜂窝用户与D2D用户之间的竞争关系，设计了一个精确势能博弈描述多个D2D用户之间的协作关系。首先证明了底层子博弈的纳什均衡(NE)的存在性，并进一步证明了所提博弈的斯坦博格均衡(SE)的存在性。在此基础上，提出了基于随机学习自动机的稳健协同D2D功率控制算法，并验证其优于随机选择算法和D2D自私功率控制算法。      

Stackelberg game for backhaul resource allocation in the two-tier LTE femtocell networks

As one promising technology for indoor coverage and service offloading from the conventional cellular networks, femtocells have attracted considerable attention in recent years. However, most of previous work are focused on resource allocation during the access period, and the backhaul involved resource allocation is seriously ignored. The authors studied the backhaul resource allocation in the wireless backhaul based two-tier heterogeneous networks （HetNets）, in which cross-tier interference control during access period is jointly considered. Assuming that the macrocell base station （MBS） protects itself from interference by pricing the backhaul spectrum allocated to femtocells, a Stackelberg game is formulated to work on the joint utility maximization of the macrocell and femtocells subject to a maximum interference tolerance at the MBS. The closed-form expressions of the optimal strategies are obtained to characterize the Stackelberg equilibriums for the proposed games, and a backhaul spectrum payment selection algorithm with guaranteed convergence is proposed to implement the backhaul resource allocation for femtocell base stations （FBSs）. Simulations are presented to demonstrate the Stackelberg equilibrium （SE） is obtained by the proposed algorithm and the proposed scheme is effective in backhaul resource allocation and macrocell protection in the spectrum-sharing HetNets.     

Hierarchical game based spectrum access optimization for anti-jamming in UAV network

For the anti-jamming spectrum access optimization problem in unmanned aerial vehicle (UAV) communication networks,considering the complex and diverse malicious jamming from jammers,a Bayesian Stackelberg game was proposed to formulate the competitive relations between UAV users and jammers.Specifically,jammers acted as the leader,whereas users acted as followers of the proposed game.Based on their different utility functions,the jammer and users independently and selfishly selected their optimal strategies and obtained the optimal channels selection.Due to the NP-hard nature,it was challenging to obtain the Stackelberg Equilibrium of the proposed game.To this end,a hierarchical learning framework was formulated,and a hierarchical channel selection-learning algorithm was proposed.Simulations demonstrate that with the proposed hierarchical learning algorithm,UAV nodes can adjust their channel selection and obtain superior performance.     

Stackelberg game based interference management for two-tier femtocell networks

Femtocell is viewed as a promising option for mobile operators to improve coverage and provide high-data-rate services in a cost-effective manner. This paper considers the uplink interference management problem in a spectrum-sharing femtocell network. Assuming that the macrocell base station (MBS) is rewarded for sharing the spectrum with femtocells by setting a reasonable interference cap (IC) for femtocell users’ (FUEs’) transmissions. Within IC, the FUEs allocate their transmission powers competitively while not introducing much interference to both the macrocell users (MUEs) and other FUEs. A Stackelberg game is formulated to jointly maximize the utility of MBS and the individual utility of FUEs. Specifically, the maximum tolerable interference at the MBS is used as the resource that the leader (MBS) and the followers (FUEs) compete for. Then, the backward induction method is applied to achieve the Stackelberg equilibrium and a distributed power update rule is developed for FUEs. In addition, the implementation protocol is presented, some issues related to the implementations and some future extensions regarding the MUEs’ uplink protection are discussed. Lastly, numerical results demonstrate the performance of our proposed power allocation in detail, and show the effects of varying the number of FBSs and changing other system parameters on the system’s performance.     

Energy-Efficient Cooperative Transmission in Heterogeneous Wireless Networks with QoS Constraint

Heterogeneous wireless network is an effective scheme to improve both the spectrum efficiency and energy efficiency of cellular system. However, interference limits the performance of such heterogeneous wireless network seriously. Hence cooperative decision making, i.e., cooperative transmission is necessary. In this paper, we develop an energy-efficient scheme for cooperative transmission in heterogeneous wireless networks with QoS constraint. Since too much signaling is needed for absolutely centralized cooperation, which is infeasible in practical system, partly cooperation is considered in this paper. We first formulate the problem as a two-stage Stackelberg game. In the first stage, an energy-efficient transmission scheme for macrocell users (MUE) is proposed based on the interference state of the MUEs. In the second stage, after obtaining the SINR and target SINR of the MUEs, the femtocell base stations update their beamformers and optimize the transmission power individually through a non-cooperative game, the existence and uniqueness of the Nash equilibrium is proved. Then an iterative algorithm is proposed to obtain the Stackelberg equilibrium. Simulation results show the performance gain of the proposed scheme.     

SDN assisted Stackelberg Game model for LTE-WiFi offloading in 5G networks

The data traffic that is accumulated at the Macro Base Station (MBS) keeps on increasing as almost all the people start using mobile phones. The MBS cannot accommodate all user’s demands, and attempts to offload some users to the nearby small cells so that the user could get the expected service. For the MBS to offload data traffic to an Access Point (AP), it should offer an optimal economic incentive in a way its utility is maximized. Similarly, the APs should choose an optimal traffic to admit load for the price that it gets from MBS. To balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading, Software Defined Networking (SDN) assisted Stackelberg Game (SaSG) model is proposed. In this model, the MBS selects the users carefully to aggregate the service with AP, so that the user experiencing least service gets aggregated first. The MBS uses the Received Signal Strength Indicator (RSSI) value of the users as the main parameter for aggregating a particular user for a contract period with LTE and WiFi. Each player involved in the game tries to maximize their payoff utilities, and thus, while incorporating those utilities in real-time scenario, we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience (QoE). Thus, the proposed SaSG model proves better when compared with other game theory models, and hence an optimal data offloading is achieved.     

Incentive edge caching in software‐defined internet of vehicles: A Stackelberg game approach

In this paper, we investigate an incentive edge caching mechanism for an internet of vehicles (IoV) system based on the paradigm of software‐defined networking (SDN). We start by proposing a distributed SDN‐based IoV architecture. Then, based on this architecture, we focus on the economic side of caching by considering competitive cache‐enablers market composed of one content provider (CP) and multiple mobile network operators (MNOs). Each MNO manages a set of cache‐enabled small base stations (SBS). The CP incites the MNOs to store its popular contents in cache‐enabled SBSs with highest access probability to enhance the satisfaction of its users. By leasing their cache‐enabled SBSs, the MNOs aim to make more monetary profit. We formulate the interaction between the CP and the MNOs, using a Stackelberg game, where the CP acts first as the leader by announcing the popular content quantity that it which to cache and fixing the caching popularity threshold, a minimum access probability under it a content cannot be cached. Then, MNOs act subsequently as followers responding by the content quantity they accept to cache and the corresponding caching price. A noncooperative subgame is formulated to model the competition between the followers on the CP's limited content quantity. We analyze the leader and the follower's optimization problems, and we prove the Stackelberg equilibrium (SE). Simulation results show that our game‐based incentive caching model achieves optimal utilities and outperforms other incentive caching mechanisms with monopoly cache‐enablers whilst enhancing 30% of the user's satisfaction and reducing the caching cost.     

A game theoretic learning solution for distributed relay selection on throughput optimization

In this paper, we study the problem of distributed relay selection in wireless networks using a game theoretic approach. Specifically, we consider a system model where one relay node can be shared by multiple source-destination pairs. Our objective is to find the relay selections of source nodes to optimize the total capacity. The relay selection problem is formulated as a congestion game with player-specific payoff functions and the existence of Nash equilibrium (NE) is demonstrated. Then we propose a stochastic learning automata (SLA) based distributed relay selection approach to obtain the NE without information exchange among source nodes. Simulation results show that the proposed distributed relay selection approach achieves satisfactory performance, when compared with other solutions.     

Opportunistic multichannel access with decentralized channel state information

This paper considers multiaccess control for the uplink in orthogonal frequency division multiple access wireless networks. To avoid the extensive information exchange associated with centralized approaches, we formulate the decentralized access control problem with the contention power constraint as a Bayesian game, mapping time‐varying channel state information into contention strategies. By exploiting the problem structure, a strategy where users access the channels with probability one if the observed channel gain is above a predetermined threshold is shown to be optimal. It is also shown that the energy consumption of the threshold strategy will not exceed that of randomized strategies. The game is then equivalently reformulated as one of finding the threshold value in a distributed manner, and the existence and uniqueness of Bayesian Nash equilibria is established. A distributed algorithm based on Lagrange duality is proposed to approach the unique equilibrium, and the algorithm is shown to be globally stable. In a homogeneous system, the performance loss of the proposed scheme is proved to be bounded compared with a centralized channel allocation scheme. Contrary to other proposals, our method allows for heterogeneous channel state information and achieves a comparable throughput with reduced power. Copyright © 2013 John Wiley & Sons, Ltd.     

Distributed Relay Selection and Power Control for Multiuser Cooperative Communication Networks Using Stackelberg Game

The performance in cooperative communication depends on careful resource allocation such as relay selection and power control, but the traditional centralized resource allocation requires precise measurements of channel state information (CSI). In this paper, we propose a distributed game-theoretical framework over multiuser cooperative communication networks to achieve optimal relay selection and power allocation without knowledge of CSI. A two-level Stackelberg game is employed to jointly consider the benefits of the source node and the relay nodes in which the source node is modeled as a buyer and the relay nodes are modeled as sellers, respectively. The proposed approach not only helps the source find the relays at relatively better locations and "buy” an optimal amount of power from the relays, but also helps the competing relays maximize their own utilities by asking the optimal prices. The game is proved to converge to a unique optimal equilibrium. Moreover, the proposed resource allocation scheme with the distributed game can achieve comparable performance to that employing centralized schemes.     

基于能量效率的双层非正交多址系统资源优化算法

该文针对双层非正交多址系统(NOMA)中基于能量效率的资源优化问题,该文提出基于双边匹配的子信道匹配方法和基于斯坦科尔伯格(Stackelberg)博弈的功率分配算法。首先将资源优化问题分解成子信道匹配与功率分配两个子问题,在功率分配问题中,将宏基站与小型基站层视作斯坦科尔伯格博弈中的领导者与追随者。然后将非凸优化问题转换成易于求解的方式,分别得到宏基站和小型基站层的功率分配。最后通过斯坦科尔伯格博弈,得到系统的全局功率分配方案。仿真结果表明,该资源优化算法能有效地提升双层NOMA系统的能量效率。     

面向软件定义无线接入网的高效协作内容缓存算法

为了缓解海量的移动业务数据与容量受限的无线接入网回传链路之间的矛盾,本文提出一种面向软件定义无线接入网（SD-RAN）的协作内容缓存网络架构.在宏蜂窝基站（MBS）的控制管理下,小蜂窝基站（SBS）可以在存储单元有序存储一些高流行度的内容.针对SBS存储单元空间受限问题,进一步提出SD-RAN网络架构下的协作内容缓存算法.该算法中,每个SBS缓存空间被分割成两部分：（1）用于存储全网流行度最高的公共内容以保证各小蜂窝小区本地命中率.（2）用于存储流行度较高的差异化的内容以促进MBS内SBS之间的协作.在此基础上,解析推导具有最优平均内容获取开销的分割参数闭合表达式.仿真结果表明该算法在不同系统参数条件下能显著降低SD-RAN的平均内容获取开销.     

Priority Aware and Spectrum Efficient Scheduling of Co-existing Wireless Body Area Networks

Real-time continuous and remote health monitoring has become widespread due to the developments in Wireless body area networks (WBANs). Based on the criticality of health data to be transmitted, regular healthcare data and critical emergency health data must be provided differential service. In this paper, we consider the beyond WBAN communication in a system comprising multiple WBANs with different quality of service (QoS) requirements and multiple access points (APs), and propose two hybrid approaches for resource allocation. In the first approach, the AP association to the WBANs and channel allocation to the APs are done jointly and is modelled as an optimization problem, which is computationally complex and it also requires global network information. In order to reduce the involvement of APs in making decisions for resource allocations of WBANs, the problem is reformulated as a Stackelberg game with price update, which guarantees QoS of the critical users. A learning based algorithm, namely distributed learning for Pareto optimality, is used by the normal users, in this second approach. The performance of both the proposed approaches are evaluated and compared, in terms of the throughput of the critical and normal users as well as the QoS guarantee of the critical users.

     

Toward green 5G heterogeneous small-cell networks: power optimization using load balancing technique

The overwhelming demand for data by an ever-increasing number of users is a great challenge wireless cellular networks are faced with. One potential solution to this issue is deploying a massive number of small cells (SCs) in the existing macro network. As SC overlay has a big role in the future wireless networks that can overcome the data traffic upsurge at little power cost, heterogeneous network (HetNet) has been viewed as a promising technology for 5G networks that extends cell coverage, improves network capacity and offloads the network traffic from the macro cell (MC) to the SCs. However, the hyper-dense SCs and their uncorrelated operation raise an important question about the joint power consumption of the macro base station (MBS) and the small base stations (SBSs) in the HetNet since the aggregate power consumption of the dense SBSs cannot be ignored. Recently, the SC sleeping technique has become a hot topic for saving energy in HetNets. To minimize power consumption in HetNets, we propose three algorithms to dynamically adapt the operation of the SBSs to active/sleep (on/off) for non-uniform user distribution in the HetNet. We investigate the general optimal power minimization problem for HetNet that requires relatively high computational complexity. Taking into account the additional increase of the traffic load brought to the MBS, a key design principle of the proposed algorithms is to switch off the SBSs gradually based on their locations, user densities in their coverage areas or the highest power that can be saved by switching some of them off, respectively. Then, we enhance the mathematical framework to make the analysis more realistic by considering the offloading between the SCs and the MBS that occurs when the traffic load exceeds SCs’ capacity. In this paper, based on the fact that user densities of SCs and MC change with time, we model the traffic on the European traffic profile and portray the power consumption of the HetNet throughout the day. Simulation results show that by applying SC sleeping and our proposed algorithms, the HetNet can save about 20% power daily. The performances of our proposed algorithms are close to that of the optimal algorithm and their computational complexities are remarkably lower.     

Distributed self-optimizing interference management in ultra-dense networks with non-orthogonal multiple access

Both non-orthogonal multiple access (NOMA) and ultra-dense network (UDN) are promising technologies in future wireless networks. However, considering the overlapped coverage of small base stations (SBSs) and the spectrum sharing with NOMA, interference management (IM) becomes a more complex and fundamental problem. Moreover, considering the massive SBSs and dynamic network conditions in UDN, more efficient mechanisms need to be designed to deal with the IM issue. Thus, we propose a distributed self-optimizing interference management approach to address both the intra-cell interference caused by NOMA and the inter-cell interference among dense deployed SBSs. Aiming to minimize the interference and guarantee the users’ requirements, we mathematically formulate the joint resource allocation and user selection problem with consideration of the diverse user requirements, complicated interference topology, and limited resources. Furthermore, we consider the imperfections of successive interference cancellation at receivers for separating and decoding superimposed signals and analyze the impacts of residual interference and outage probability in NOMA-based UDNs. For tractability purpose, we introduce interference graph and satisfaction game theory and propose distributed algorithms to solve the problem. Simulation results show that interference can be reduced significantly in UDNs with NOMA compared with the traditional IM approaches.