基于博弈资源分配的认知异构网络干扰协调算法

基于underlay频谱共享模式的认知异构网络可有效缓解频谱资源短缺问题,但同时会加剧网络中的干扰。针对该问题,提出了一种基于非合作博弈模型的动态频谱分配和功率控制算法进行干扰协调。首先,考虑频谱共享造成的干扰问题,引入认知用户优先等级,将问题构建为联合动态频谱分配与功率控制的频谱定价博弈模型;其次,通过两阶段动态博弈得到纳什均衡解,实现认知网络层频谱资源合理分配和发射功率控制。仿真表明,所提算法能够实现不同优先级用户频谱资源的合理分配和认知基站发射功率控制,有效抑制认知异构网络的跨层干扰和层内干扰。     

A network infrastructure for IP mobility support in metropolitan areas

The original design of the Internet and its underlying protocols did not anticipate users to be mobile. With the growing interest in supporting mobile users and mobile computing, a great deal of work is taking place to solve this problem. For a solution to be practical, it has to integrate easily with existing Internet infrastructure and protocols, and offer an adequate migration path toward what might represent the ultimate solution. In that respect, the solution has to be incrementally scalable to handle a large number of mobile users and wide geographical scopes, and well performing so as to support all application requirements including voice and video communications and a wide range of mobility speeds. In this paper, we present a survey of the state-of-the-art and propose a scalable infrastructure to support mobility in Internet protocol networks. In that respect, we exploit local area network (LAN) technologies to create the network infrastructure necessary to offer connectivity to mobile users across any geographical area (building, campus and metropolis). The intrinsic properties of LAN technologies and their underlying protocols, namely flat address space, transparent learning and low complexity renders this solution particularly cost effective for supporting user mobility. In particular, we propose a network topology and a set of protocols that render the infrastructure scalable to a large geographical area and many users.     

Utility-based downlink power allocation in multicell wireless packet networks

This paper introduces a utility-based radio resource management technique in multicell wireless packet networks. In terms of allocation of base station (BS) downlink transmit power and assignment of resource to users in each cell, we formulate a problem of maximizing system utility which is defined as the sum of cell utilities. The problem, however, is not solvable due to its non-convex property. Thus, we propose a heuristic algorithm based on an intuition obtained from analyzing a simple two-cell problem. Though the heuristic approach also incurs signaling overhead for power coordination between neighboring base stations, it is much less than that of the original approach. Simulation results show the performance of our proposed algorithm compared with two competitive schemes: optimal and maximum power allocation schemes. As expected, the optimal allocation scheme shows the best performance but can not be employed in a real network due to intractable complexity. Our heuristic algorithm performs reasonably well with very low complexity.     

A novel terminal-controlled handover scheme in heterogeneous wireless networks

In the next generation heterogeneous wireless networks, a mobile terminal (MT) with a multi-interface may have network access from different service providers using various technologies. In spite of this heterogeneity, seamless intersystem mobility is a mandatory requirement. One of the major challenges for seamless mobility is the creation of a vertical handover scheme, which is for users that move between different types of networks. In this article a seamless mobility handover scheme is presented. The novelty of the proposed scheme is that handover is fully controlled by the terminal. To improve the performance of the proposed handover scheme, a network discovery algorithm with fuzzy logic and a handover decision algorithm using multi criteria decision making (MCDM) based on vague sets are derived, which are both user-centric. With these algorithms, the handover scheme is power-saving, cost-aware, and performance-aware. Simulation results show that the novel handover scheme can perform network discovery in time to reduce handover dropping rate and present unnecessary activation of its interface to save the battery power, while effectively choose the optimum network through making trade-off among the user preference, network condition, and system performance.     

A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework

Due to their inherent limitations in computational and battery power, storage and available bandwidth, mobile devices have not yet been widely integrated into grid computing platforms. However, millions of laptops, PDAs and other portable devices remain unused most of the time, and this huge repository of resources can be potentially utilized, leading to what is called a mobile grid environment. In this paper, we propose a game theoretic pricing strategy for efficient job allocation in mobile grids. By drawing upon the Nash bargaining solution, we show how to derive a unified framework for addressing such issues as network efficiency, fairness, utility maximization, and pricing. In particular, we characterize a two-player, non-cooperative, alternating-offer bargaining game between the Wireless Access Point Server and the mobile devices to determine a fair pricing strategy which is then used to effectively allocate jobs to the mobile devices with a goal to maximize the revenue for the grid users. Simulation results show that the proposed job allocation strategy is comparable to other task allocation schemes in terms of the overall system response time.     

A Dantzig-Wolfe Decomposition Based Heuristic Scheme for Bi-level Dynamic Network Design Problem

We present a heuristic to solve the NP-hard bi-level network design problem (NDP). The heuristic is developed based on the Dantzig-Wolfe decomposition principle such that it iteratively solves a master problem and a pricing problem. The master problem is the budget allocation linear program solved by CPLEX to determine the budget allocation and construct a modified cell transmission network for the pricing problem. The pricing problem is the user-optimal dynamic traffic assignment (UODTA) solved by an existing combinatorial algorithm. To facilitate the decomposition principle, we propose a backward connectivity algorithm and complementary slackness procedures to efficiently approximate the required dual variables from the UODTA solution. The dual variables are then employed to augment a new column in the master program in each iteration. The iterative process repeats until a stopping criterion is met. Numerical experiments are conducted on two test networks. Encouraging results demonstrate the applicability of the heuristic scheme on solving large-scale NDP. Though a single destination problem is considered in this paper, the proposed scheme can be extended to solve multi-destination problems as well.     

Unlocking the Advantages of Dynamic Service Selection and Pricing

Bandwidth limitations, resource greedy applications verbose mark-up languages and an increasing number of voice and data users are straining the air interface of wireless networks. Hence, novel approaches and new algorithms to manage wireless bandwidth are needed. In addition, usage based pricing is becoming increasingly prevalent (pre-paid cell phones, calling cards, non-contract minutes, etc.). This paper unlocks the potential to improving the performance of overall system behavior by allowing users to change service level and/or service provider for a (small) price. The ability to dynamically re-negotiate service gives the user the power to control QoS while minimizing usage cost. On the other hand, the ability to change service level pricing dynamically allows the service providers to manage traffic better, improve resource usage and most importantly maximize their profit. This provides a surprising win-win situation for BOTH the service providers AND the users. In this paper we present easy to implement on-line algorithms to minimize the overall usage cost to individual mobile users. This on-line algorithm continuously receives pricing information and evaluates minimum QoS requirements. The algorithm then determines appropriate service level, chooses a service provider and sets a time for re-negotiation dynamically. Our algorithm can handle many practical issues such as capacity limitations, arbitrary price fluctuations and loss/gain of service providers due to mobility. Our results do not assume any specific technologies and can be applied to any environment that can employ dynamic pricing, including wired networks. In fact, dynamic pricing is becoming increasingly desirable since service provider and capacity changes are a growing by-product of mobility. Arriving and departing users at/from a cell tower (or wireless LAN) can effectively reduce or increase the available bandwidth in a cell (or LAN transmission area) and represents a natural opportunity for a pricing change.     

减少核心网拥塞的边缘计算资源分配和卸载决策

随着移动互联网和物联网的发展,越来越多的智能终端设备投入到实际使用当中,大量计算密集型和时间敏感型应用被广泛应用,如AR/VR、智能家居、车联网等.因此,网络中的数据流量激增,使得核心网络面临的压力逐渐增大,对网络时延的控制也越来越难,此时云边协同的计算范式作为一种解决方案被提出.针对云边之间的核心网流量控制问题,文中...     

Subcarrier and power allocation for OFDMA-based multicast cellular networks using a coalitional game

The subcarrier and power allocation problem for orthogonal frequency-division multiple access (OFDMA)-based multicast cellular networks is investigated in this paper. Due to the intrinsic heterogeneity of the channel gains experienced by different multicast users, the conventional multicast schemes are highly conservative and spectrally inefficient. To address this issue, a novel multicast formulation is proposed where multiuser diversity inherent in OFDMA can be adaptively exploited by clustering users within a multicast group into smaller subgroups based on their channel gains. Subcarriers and power are then dynamically allocated to these subgroups to maximize the total multicast rate of the system. Coalitional game theory is adopted to model the group formation in which users can autonomously form coalitions with other users to compete for network resources. A low-complexity algorithm is proposed for the multicast coalitional game to reach multicoalitional equilibrium in which a sub-optimal performance can be obtained. Simulation results demonstrate that the proposed scheme outperforms the conventional unicast and multicast schemes while achieving a sub-optimal performance comparable to the exhaustive search scheme.     

基于移动互联网的QoS分级自适应策略研究

提出在网络融合时基于实时应急管理的QoS分级自适应策略,用分级映射表的方法建立了移动网与互联网QoS映射的关系,并提出了多业务聚合流的QoS级别选择算法：用接入互联网的网关作为自适应QoS调节的决策节点,由网关监测到的网络负载变化,根据业务流对QoS作出及时调节,使决策更加及时。仿真模拟表明,该策略解决了移动接入网与Internet提供业务的QoS匹配映射问题,可以使传输时延减少至少20％,并能抑制用户盲目扩大自己的需求,避免了用户恶意攻击加重网络负担。     

A localization algorithm for large scale mobile wireless sensor networks: a learning approach

Localization is a crucial problem in wireless sensor networks and most of the localization algorithms given in the literature are non-adaptive and designed for fixed sensor networks. In this paper, we propose a learning based localization algorithm for mobile wireless sensor networks. By this technique, mobility in the network will be discovered by two crucial methods in the beacons: position and distance checks methods. These two methods help to have accurate localization and constrain communication just when it is necessary. The proposed method localizes the nodes based on connectivity information (hop count), which doesn’t need extra hardware and is cost efficient. The experimental results show that the proposed algorithm is scalable with a small set of beacons in large scale network with a high density of nodes. The given algorithm is fast and free from a pre-deployment requirement. The simulation results show the high performance of the proposed algorithm.     

Mobility-Aware Mobile Router Selection and Address Management for IPv6 Network Mobility

Network mobility (NEMO) extends IP mobility to moving networks, which are groups of nodes that often constitute a subnet of a mobile router (MR). To realize this collective mobility, there are a number of important issues such as addressing and multihoming. With the proliferation of mobile nodes connected to the Internet, the efficient allocation/deallocation of addresses is becoming a vital requirement. We propose a collaborative address management scheme for network mobility, where the home DHCPv6 agent of a mobile network performs prefix delegation, while the mobile DHCPv6 agent (in the mobile network) allocates the IPv6 address to each mobile node. Also, network mobility with multiple MRs is taken into consideration. To provide a mobile network that has multiple MRs with robust Internet connectivity, we propose mobility-aware mobile router selection schemes. The concept of mobility awareness refers to the capability of a moving network in a vehicle (e.g., a train) to pinpoint the most stable Internet connectivity, by choosing the mobile router based on the vehicles movement pattern. The simulation shows that the proposed scheme outperforms a round-robin mobile router selection scheme in terms of the amount of carried traffic.     

异构蜂窝网络中功率和资源分配博弈算法研究

基于D2D和中继异构蜂窝网络进行资源复用可获得系统性能增益,但同时也使得网络中的干扰更加复杂。针对该问题,提出功率和资源分配博弈（PRAG）算法,通过功率控制和资源分配对D2D和中继异构蜂窝网络进行干扰协调。基于代价参数设定D2D和中继链路效用函数,确定最佳发射功率。在此基础上,将生成的效用值矩阵参与博弈,选择合适的蜂窝用户进行资源复用。仿真结果表明,与等功率分配随机（EPAR）算法相比,PRAG算法能够在消耗更少功率的基础上获得更大的系统吞吐量。     

Co-evolutionary algorithms to solve hierarchized Steiner tree problems in telecommunication networks

This study investigates a hierarchized Steiner tree problem in telecommunication networks. In such networks, users must be connected to capacitated hubs. Additionally, selected hubs must be connected to each other and to extra hubs, if necessary, by considering the latency of the resultant network. A connection between hubs can be considered to be a Steiner tree. This Steiner tree problem is modeled as a bi-level mathematical programming problem that considers two decision levels. In the upper-level, the allocation of users to hubs is performed to minimize the total network connection cost. The lower-level minimizes the user latency concerning the information that flows through the capacitated hubs. Further, two co-evolutionary schemes are developed to solve this bi-level model. The first scheme is an individual–population approach, whereas the second scheme is the traditional population–population approach. The first proposed algorithm exploits the structure of the problem by employing parallel computing in one of the populations. Numerical results depict the effectiveness of the proposed algorithms when the lower-level problem cannot be optimally solved efficiently. Furthermore, the advantages of the proposed schemes over an evolutionary one are exhibited. Finally, the hybridization of both co-evolutionary schemes is implemented to improve the semi-feasible solutions obtained by the second scheme, showing its effectiveness to solve the problem.     

支持多速率多播的Ad hoc网络资源分配算法

多播流是无线Ad hoc网络中的一种常见和有效的通信形式,尤其是对多媒体业务而言。提出了一种支持多速率多播传输的Ad hoc网络资源分配算法,它通过引入基于价格的流量分配方案来解决多速率多播传输问题,从而能够自适应地分配网络流量,并且最大化网络流的总效用。仿真结果表明,该算法不仅具有良好的收敛性,而且它的多速率多播传输特性使得不同信道条件的用户能获得最佳的网络性能,从而提高了网络吞吐量。     

A study of power allocation in multi-MRN aided multiuser networks

It is promising that mobile relay nodes (MRNs), i.e., relays mounted on top of transportation vehicles, are deployed in future mobile communication systems in order to improve system performance. In this paper a multi-MRN aided multiuser system is studied. The system performance under direct transmission mode and MRN assisted transmission mode are compared with variable parameter of vehicle penetration loss (VPL). The mobile users are separated into two categories in which the direct transmission mode and the MRN assisted transmission mode are selected to transmit in downlink, respectively. A novel power allocation algorithm is proposed to increase the average system capacity under the constraint of total transmit power. The different power allocation schemes are applied for users in two categories to improve the system performance. It is demonstrated by simulation results that the proposed algorithm outperforms the average power allocation algorithm.     

基于DQN的超密集网络能效资源管理

小基站的密集随机部署会产生严重干扰和较高能耗问题,为降低网络干扰、保证用户网络服务质量（QoS）并提高网络能效,构建一种基于深度强化学习（DRL）的资源分配和功率控制联合优化框架。综合考虑超密集异构网络中的同层干扰和跨层干扰,提出对频谱与功率资源联合控制能效以及用户QoS的联合优化问题。针对该联合优化问题的NP-Hard特性,提出基于DRL框架的资源分配和功率控制联合优化算法,并定义联合频谱和功率分配的状态、动作以及回报函数。利用强化学习、在线学习和深度神经网络线下训练对网络资源进行控制,从而找到最佳资源和功率控制策略。仿真结果表明,与枚举算法、Q-学习算法和两阶段算法相比,该算法可在保证用户QoS的同时有效提升网络能效。     

Minimum disruption service composition and recovery in mobile ad hoc networks

The dynamic nature of mobile ad hoc networks poses fundamental challenges to the design of service composition schemes that can satisfy the end-to-end quality of service requirements and minimize the effect of service disruptions caused by dynamic link and node failures. Although existing research on mobile ad hoc networks has focused on improving reliability, little existing work has considered service deliveries spanning multiple components. Moreover, service composition strategies proposed for wireline networks (such as the Internet) are poorly suited for highly dynamic wireless ad hoc networks.This paper proposes a new service composition and recovery framework designed to achieve minimum service disruptions for mobile ad hoc networks. The framework consists of two tiers: service routing, which selects the service components that support the service path, and network routing, which finds the optimal network path that connects these service components. Our framework is based on the disruption index, which is a novel concept that characterizes different service disruption aspects, such as frequency and duration, that are not captured adequately by conventional metrics, such as reliability and availability.Using the definition of disruption index, we formulate the problem of minimum-disruption service composition and recovery (MDSCR) as a dynamic programming problem and analyze the properties of its optimal solution for ad hoc networks with known mobility plan. Based on the derived analytical insights, we present our MDSCR heuristic algorithm for ad hoc networks with uncertain node mobility. This heuristic algorithm approximates the optimal solution with one-step lookahead prediction, where service link lifetime is predicted based on node location and velocity using linear regression. We use simulations to evaluate the results of our algorithm in various network environments. The results validate that our algorithm can achieve better performance than conventional methods.     

A novel resource optimization scheme for multi-cell OFDMA relay network

In cellular networks, users communicate with each other through their respective base stations (BSs). Conventionally, users are assumed to be in different cells. BSs serve as decode-and-forward (DF) relay nodes to users. In addition to this type of conventional user, we recognize that there are scenarios users who want to communicate with each other are located in the same cell. This gives rise to the scenario of intra-cell communication. In this case, a BS can behave as a two-way relay to achieve information exchange instead of using conventional DF relay. We consider a multi-cell orthogonal frequency division multiple access (OFDMA) network that comprises these two types of users. We are interested in resource allocation between them. Specifically, we jointly optimize subcarrier assignment, subcarrier pairing, and power allocation to maximize the weighted sum rate. We consider the resource allocation problem at BSs when the end users’ power is fixed. We solve the problem approximately through Lagrange dual decomposition. Simulation results show that the proposed schemes outperform other existing schemes.