Grant-Free Random Access in Pilot-Reused Multicell Massive MIMO Systems with Backoff Mechanisms

Grant-free random access(RA)is attractive for future network due to the minimized access delay.In this paper,we investigate the grantfree RA in multicell massive multiple-input multipleoutput(MIMO)systems with pilot reuse.With backoff mechanism,user equipments(UEs)in each cell are randomly activated,and active UEs randomly select orthogonal pilots from a predefined pilot pool,which results in a random pilot contamination among cells.With the help of indicators that capture the uncertainties of UE activation and pilot selection,we derive a closed-form approximation of the spectral efficiency per cell after averaging over the channel fading as well as UEs’random behaviors.Based on the analysis,the optimal backoff parameter and pilot length that maximize the spectral efficiency can be obtained.We find that the backoff mechanism is necessary for the system with large number of UEs,as it can bring significant gains on the spectral efficiency.Moreover,as UE number grows,more backoff time is needed.

Adaptive Backoff Algorithm for IEEE 802.11 MAC Protocol

A Mobile Ad Hoc Network (MANET) is a collection of mobile nodes that can communicate directly over wireless media, without the need for a preconfigured infrastructure. Several approaches have been suggested to improve Quality of Service (QoS) in IEEE 802.11-based MANETs through modifying some of the IEEE 802.11 Medium Access Control (MAC) algorithms, such as the backoff algorithm that is used to control the packets collision aftermath. In this work, an adaptive IEEE 802.11 backoff algorithm to improve QoS is de-veloped and tested in simulations as well as in testbed implementation. While the Binary Exponential Backoff (BEB) algorithm deployed by IEEE 802.11 reacts based on individual packet transmit trials, the new algo-rithm takes the history of successive packet transmit trials into account to provide a better QoS performance. The new algorithm has been tested against the legacy IEEE 802.11 through simulations using QualNet and a Linux-based testbed comprising a number of stations. The performed tests have shown significant im-provements in performance, with up to 33.51% improvement in delay and 7.36% improvement in packet delivery fraction compared to the original IEEE 802.11.

A DELAY BASED BACKOFF SCHEME FOR WLAN WITH QoS GUARANTEE AND ITS PERFORMANCE

Based on the Distributed Coordination Function (DCF) and the Enhanced DCF (EDCF) mechanisms in IEEE 802.11 and 802.11e, a novel backoff scheme for Wireless Local Area Network (WLAN) is proposed. The scheme is to solve the problem of the packet loss and the decrease of performance due to the increasing Contention Window (CW) when there are continuous collisions. In the proposed scheme, the CW of the packet will change dynamically with different delay for the different traffics. Mathematic formulas are presented to indicate the relationship between the CW and the delay character. ,The performance of the new scheme is also discussed with simulation results. The results show that it helps WLAN system handle multimedia simultaneously.

History-Based Backoff Algorithms for Mobile Ad Hoc Networks

Mobile Ad hoc Network (MANET) is a wireless network consisting of mobile devices (laptops, smart phones, etc.) that move and communicate with each other without the use of any existing network infrastructure or centralized server to avoid collisions which have negative effects on the performance of the network. Access to the shared media is controlled by a Backoff algorithm that is a part of the Media Access Protocol. In this paper, we improve the History Based Probabilistic Backoff (HPPB) algorithm by modifying the increment/decrement behavior of the Contention Window to introduce History Based Increment Backoff (HBIB) algorithm which outperforms HBPB in terms of throughput and end-to-end delay with various numbers of nodes and different traffic loads.

A utility-optimal backoff algorithm for wireless sensor networks

A novel backoff algorithm in CSMA/CA-based medium access control (MAC) protocols for clustered sensor networks was proposed. The algorithm requires that all sensor nodes have the same value of contention window (CW) in a cluster, which is revealed by formulating resource allocation as a network utility maximization problem. Then, by maximizing the total network utility with constrains of minimizing collision probability, the optimal value of CW (Wopt) can be computed according to the number of sensor nodes. The new backoff algorithm uses the common optimal value Wopt and leads to fewer collisions than binary exponential backoff algorithm. The simulation results show that the proposed algorithm outperforms standard 802.11 DCF and S-MAC in average collision times, packet delay, total energy consumption, and system throughput.

Energy Efficient Backoff Hierarchical Clustering Algorithms for Multi-Hop Wireless Sensor Networks

Compared with flat routing protocols, clustering is a fundamental performance improvement technique in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we integrate the multi-hop technique with a backoff-based clustering algorithm to organize sensors. By using an adaptive backoff strategy, the algorithm not only realizes load balance among sensor node, but also achieves fairly uniform cluster head distribution across the network. Simulation results also demonstrate our algorithm is more energy-efficient than classical ones. Our algorithm is also easily extended to generate a hierarchy of cluster heads to obtain better network management and energy-efficiency.

A Fairness-Enhanced Intelligent MAC Scheme Using Q-Learning-Based Bidirectional Backoff for Distributed Vehicular Communication Networks

With the increasing attention to front-edge vehicular communication applications,distributed resource allocation is beneficial to the direct communications between vehicle nodes.However,in highly dynamic distributed vehicular networks,quality of service(QoS)of the systems would degrade dramatically because of serious packet collisions in the absence of sufficient link knowledge.Focusing on the fairness optimization,a Q-learning-based collision avoidance(QCA)scheme,which is characterized by an ingenious bidirectional backoff reward model RQCA corresponding to arbitrary backoff stage transitions,has been proposed in an intelligent distributed media access control protocol.In QCA,an intelligent bidirectional backoff agent based on the Markov decision process model can actively motivate each vehicle agent to update itself toward an optimal backoff sub-intervel BSIopt through either positive or negative bidirectional transition individually,resulting in the distinct fair communication with a proper balance of the resource allocation.According to the reinforcement learning theory,the problem of goodness evaluation on the backoff stage self-selection policy is equal to the problem of maximizing Q function of the vehicle in the current environment.The final decision on BSI_(opt) related to an optimal contention window range was solved through maximizing the Q value or Q_(max).The ε-greedy algorithm was used to keep a reasonable convergence of the Q_(max) solution.For the fairness evaluation of QCA,four kinds of dynamic impacts on the vehicular networks were investigated:mobility,density,payload size,and data rate with a network simulator NS2.Consequently,QCA can achieve fair communication efficiently and robustly,with advantages of superior Jain’s fairness index,relatively high packet delivery ratio,and low time delay.

RWBO（pd, w）： A Novel Backoff Algorithm for IEEE 802.11 DCF

The Probability Distribution of. Slot Selection (PDoSS) of IEEE 802.11 DCF is extremely uneven, which makes the packet collision probability very high. In this paper, the authors explore how to make the stations select:the slots uniformly, and give an RWBO(P-d,w) algorithm for 802.11 DCF to make the PDoSS even and decrease the packet collision probability. A Markov model is given to analyze the PDoSS of RWBO(p(d), w). The performance of RWBO(p(d), w) is evaluated, by. simulation in terms of saturation throughput and packet collision probability. The simulation results indicate that RWBO(p(d), w) can decrease the packet contention,probability to a large extent, and utilize the channel more efficiently as compared to the 802.11 DCF. Moreover, the relation between saturation throughput, and walking probability (p(d)),the relation between saturation throughput and contention windows (w), the relation between packet collision probability and walking probability (p(d)), and the relation between packet collision probability and contention windows (w) are analyzed. The analysis indicates that RWBO(p(d), w) has some remarkable features: its saturation throughout keeps high and packet collision probability keeps very low (under 0.1) in a large range of p(d) and w, which allow users to configure p(d) and w more flexibly.

DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

面向水下移动汇聚节点的媒体接入控制协议研究

针对水下移动传感网络(underwater mobile sensor network,UWMSN)中使用自主移动节点进行数据收集存在的拓扑结构不断变化、传输不可靠的问题,提出一种动态退避发送数据帧的自适应媒体接入控制(media access control,MAC)协议,通过将协议划分为控制阶段与数据阶段周期性地更新可建立通信的节点信息,根据当前周期获得的节点距离信息调度各节点数据传输顺序,利用水下环境的大传播时延计算各节点的回退发送数据帧的时间,使用数据重传机制提高传输的可靠性。采用OPNET网络仿真模拟器对提出的协议进行仿真和对比分析。仿真结果显示,协议在密度较低的小范围水下传感网络中可保证较高的投递成功率和较低的端到端时延,增大了网络吞吐量,提高了网络性能。

基于NB-IOT随机接入的评估建模与仿真

由于NB-IoT传输大部分发生在上行链路,随机接入信道容易成为系统瓶颈。针对此问题,对NB-IoT随机接入信道进行建模,推导出RACH成功概率的精确表达式,量化评估每个覆盖等级中网络配置对系统性能的影响。仿真分析结果表明,利用评估模型对最大前导重传次数、退避窗口大小、子载波数量等参数进行周期性优化,能够有效提高设备接入成功率,降低接入时延。

基于冲突分类模型的冲突解析算法

IEEE 802.11网络中的传统退避算法利用竞争窗口机制进行冲突解析,冲突节点被重新分布在一个更大的窗口范围内以避免冲突.然而,只要这些分布窗口之间存在着交集,就仍有可能引发冲突.为了解决一个问题,提出了一种冲突分类模型,将网络中的分组冲突分成交叉冲突和同级冲突,并且提出针对这两种不同类型的冲突应该采取不同的策略进行解析.利用顺序离散窗口分布机制(sequential discrete window distribution,简称SDWD)解析交叉冲突,通过节点分布窗口的离散化避免交叉冲突;同时,通过设置合适的分布窗口大小在同级冲突概率和分组延迟之间取得折衷.在此基础上,实现了两种冲突解析算法:冲突分类解析算法(cross collision resolution,简称CR)和无冲突分类解析算法(collision-free CCR,简称CF-CCR).仿真结果表明,同IEEE 802.11DCF协议相比,CCR和CF-CCR在冲突率、吞吐量、分组延迟、公平性以及延迟抖动方面均有提高.并且,这两个算法在不同场合表现出不同的优越性.

Ad hoc网络信道接入技术研究

Ad hoc网络是一种特殊的对等式网络。传统的基于固定的或有中心的网络的协议不能满足 Ad hoc网络的需要。首先研究了 Ad hoc网络信道接入协议要考虑的隐终端问题和暴露终端问题 ,并给出了一种可能的双信道解决方案。在分析了现有的两种退避算法后 ,提出使用退避计数器广播加信道争用估计技术来消除不公平现象。在这些结果的基础上 ,提出了一种新的信道接入协议。

无线传感器网络贪婪转发策略中的路由空洞问题

贪婪转发策略是无线传感器网络中基于地理位置的路由算法中非常重要的一种,但是该算法会遭遇到路由空洞问题。该文从理论上分析了路由空洞在规则部署和随机部署情况下的存在概率,并得出了随机部署情况下随机建立的路径会遭遇到路由空洞的概率。通过分析可以得出,当网络中节点的平均邻居数大于10时,利用带有退避改进方法的贪婪转发策略足以满足无线传感器网络的需要。

一种时效性约束的二进制指数退避算法

针对IEEE 802.11标准中二进制指数退避算法(Binary Exponential Backoff,BEB)以最大重传次数作为分组丢弃依据其时效性性能差的问题,提出一种基于时效性约束的二进制指数退避算法(Delay-constrained Binary Expo-nential Backoff,DC_BEB)。以分组自身的超时门限替代最大重传次数作为分组丢弃的依据,能在不显著降低系统归一化吞吐量的同时,提高系统归一化有效吞吐量,适合于对分组时效性要求高的无线自组织网络。构建了Markov模型来分析采用DC_BEB算法时的系统性能。通过仿真实验,验证了模型的近似准确性以及DC_BEB算法相对于BEB算法的优势。

适于无线多媒体传感器网络的MAC层退避算法

提出了服务区分动态退避算法——SDDB算法,通过对优先级不同的业务采用不同的退避方式,能在不显著降低网络吞吐量的同时,实现合理退避及业务优先级区分服务,有效避免二次冲突,支持突发业务流传输,保障优先级高的业务的实时传输要求,更适合多种业务并存的无线多媒体传感器网络。

IEEE 802.11无线网络的两步指数退避算法

IEEE 802.11标准引入二进制指数退避(Binary Exponential Backoff,BEB)算法以降低节点发送数据包碰撞的概率。然而,BEB存在着不足之处,当数据包碰撞概率较大时,节点的竞争窗口长度会出现振荡,即节点每次发送数据包之前,需要多次加倍扩大竞争窗口长度,而在发送成功之后又把竞争窗口长度缩小到最小值,这个过程反复出现。为了克服竞争窗口振荡问题以增加吞吐量,该文提出两步指数退避(Two-step BEB,TBEB)算法,利用2维马尔可夫链进行建模,导出TBEB算法中节点的退避状态概率分布、平均竞争窗口长度、平均退避次数、每发送一个数据帧所耗时间以及吞吐量等指标,并通过仿真进行验证。通过求解一个简单的优化问题,TBEB可以获得最优竞争窗口长度复位值,使吞吐量达到最优。

IEEE 802．16竞争解决方案的性能分析

目前,IEEE 802.16标准推荐采用基于截断二进制指数回退算法的竞争解决方案.分析了该方案同IEEE 802.11竞争机制的区别,给出了传送机会利用率u、带宽请求延时d以及带宽请求丢失率p_d等性能指标的计算方法.通过性能模拟,讨论了初始化回退窗口W、用户站数目n以及单位时间帧内传送机会数目N_(?)等参数对性能指标的影响,进而得出基站调整性能参数的一般策略.这些策略对于基站进行上行带宽资源的分配具有指导意义.

无线传感器网络能效和延时性能分析

为全面评估能效和延时性能并分析网络参数对它们的影响,从通信三要素出发,考虑发送端退避机制、传输信道误码率和接收端碰撞概率,建立了网络参数与性能间的关系模型。通过对能效、延迟性能的理论推导及仿真验证,得出结论:数据包负载长度、误码率、节点数、退避窗口和最大允许重传次数限制会不同程度地影响丢包率,从而影响网络性能。

一种最小竞争窗口自适应调整的802.11退避算法

该文在对原有的IEEE 802.11 DCF研究的基础上,提出了一种基于最小竞争窗口自适应调整的退避算法(Minimum Contention Window Self-adaptive Adjusting,MCWSA)。该算法的思想是每个站点根据网络中当前的时隙利用率和理论上最优时隙利用率进行比较,周期性动态调整自身的最小竞争窗口,以适应不同的网络拥塞状况。仿真表明,该算法提高了IEEE 802.11局域网在拥塞情况下的性能,在饱和吞吐量和时延上都有不同程度的改善。