Spatio-Temporal Population Density and Spatial Dynamic Spectrum Allocation

A realistic population density distribution scenario in conjunction with the spatial dynamic spectrum allocation （DSA） is taken into account to mitigate the spectrum wastage in terms of extra guard bands. For the insertion of the extra guard bands, an efficient strategy based on self-assessment is applied to each victim cell individually and independently. Consequently, it is no more required to spread the extra guard band over the whole DSA region. Simulation results StlOW an improvement of 3% -4% in percentage of satisfied users for Universal Mobile Telecommunications System （UMTS） network and 4%-5% for Digital Video Broadcasting Terrestrial （DVB-T） network.

Extended Virtual Boundary and Its Application in Dynamic Spectrum Allocation

This paper presents an efficient dynamic spectrum allocation （DSA） scheme in a flexible spectrum licensing environment where multiple networks coexist and interfere with each other. In particular, an extension of virtual boundary concept in DSA is proposed, which is spectrally efficient than the previous virtual boundary concept applied to donor systems only. Here, the same technique is applied to both donor and rental systems so as to further reduce the occurrences where the insertion of guard bands is obligatory and as a result provides better spectral efficiency. The proposed extension improves the spectrum utilization without any compromise on interference and fairness issues.

A Dynamic Distributed Spectrum Allocation Mechanism Based on Game Model in Fog Radio Access Networks

With the explosive growth of highspeed wireless data demand and the number of mobile devices, fog radio access networks(F-RAN) with multi-layer network structure becomes a hot topic in recent research. Meanwhile, due to the rapid growth of mobile communication traffic, high cost and the scarcity of wireless resources, it is especially important to develop an efficient radio resource management mechanism. In this paper, we focus on the shortcomings of resource waste, and we consider the actual situation of base station dynamic coverage and user requirements. We propose a spectrum pricing and allocation scheme based on Stackelberg game model under F-RAN framework, realizing the allocation of resource on demand. This scheme studies the double game between the users and the operators, as well as between the traditional operators and the virtual operators, maximizing the profits of the operators. At the same time, spectrum reuse technology is adopted to improve the utilization of network resource. By analyzing the simulation results, it is verified that our proposed scheme can not only avoid resource waste, but also effectively improve the operator's revenue efficiency and overall network resource utilization.

Genetic approach for Cell-by-Cell dynamic spectrum allocation in the heterogeneous scenario

In this paper,a genetic algorithm (GA) is investigated to deal with cell-by-cell dynamic spectrumallocation (DSA) in the heterogeneous scenario with temporal and spatial traffic demand changes,whichis also known as a difficult combinatorial optimization problem.A new two-dimensional chromosome encodingscheme is defined according to characteristics of the heterogeneous scenario,which prevents forminginvalid solutions during the genetic operation and enables much faster convergence.A novel randomcoloring gene generation function is presented which is the basic operation for initialization and mutationin the genetic algorithm.Simulative comparison demonstrates that the proposed GA-based cell-by-cellDSA outperforms the conventional contiguous DSA scheme both in terms of spectral efficiency gain andquality of service (QoS) satisfaction.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Utility-optimization dynamic subcarrier allocation algorithm for SC-FDMA systems

Two utility-optimization dynamic subcarrier allocation(DSA) algorithms are designed for single carrier frequency division multiple access system(SC-FDMA).The two proposed algorithms aim to support diverse transmission capacity requirements in wireless networks,which consider both the channel state information(CSI) and the capacity requirements of each user by setting appropriate utility functions.Simulation results show that with considerable lower computational complexity,the first utility-optimization algorithm can meet the system capacity requirements of each user effectively.However,the rate-sum capacity performance is poor.Furthermore,the second proposed utility-optimization algorithm can contribute a better trade-off between system rate-sum capacity requirement and the capacity requirements of each user by introducing the signal to noise ratio(SNR) information to the utility function based on the first utility-optimization algorithm,which can improve the user requirements processing capability as well as achieve a better sum-rate capacity.

Joint Optimization of Satisfaction Index and Spectrum Efficiency with Cache Restricted for Resource Allocation in Multi-Beam Satellite Systems

Dynamic resource allocation(DRA) is a key technology to improve system performances in GEO multi-beam satellite systems. And, since the cache resource on the satellite is very valuable and limited, DRA problem under restricted cache resources is also an important issue to be studied. This paper mainly investigates the DRA problem of carrier resources under certain cache constraints. What's more, with the aim to satisfy all users' traffic demands as more as possible, and to maximize the utilization of the bandwidth, we formulate a multi-objective optimization problem(MOP) where the satisfaction index and the spectrum efficiency are jointly optimized. A modified strategy SA-NSGAII which combines simulated annealing(SA) and non-dominated sorted genetic algorithm-II(NSGAII) is proposed to approximate the Pareto solution to this MOP problem. Simulation results show the effectiveness of the proposed algorithm in terms of satisfaction index, spectrum efficiency, occupied cache, and etc.

A DYNAMIC SPECTRUM ACCESS NETWORK BASED ON COGNITIVE RADIO

Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.

Database-Assisted Dynamic Spectrum Access with QoS Guarantees:A Double-Phase Auction Approach

Since FCC's opening for white space(WS) utilization,database-assisted dynamic spectrum access(DSA) has become the de facto solution for the realization of dynamic spectrum sharing(DSS),due to its simplicity and compatibility with commercial off-the-shelf(COTS) devices.It is envisioned that such technology will strongly support the prosperous wireless multimedia networking(WMN) applications with satisfying QoS guarantees in the future.However,how to counter the time-frequency variant property when exploiting the WS spectrum for the provision of these services to secondary users(SUs) still remains a great challenge.In such context,a dynamic secondary access scheme for database-assisted spectrum sharing networks is proposed in this paper.In the beginning,the spectrum requirements of SUs for diverse services are modeled by considering the minimum required service data-rate and spectrum access duration.Afterwards,the spectrum demand evaluation and bidding policy are formulated based on the service classes of SUs.Furthermore,a doublephase(DP) spectrum allocation scheme,which consists of the initial resource allocation phase and resource allocation adjustment phase,is carefully designed for DSA.Finally,extensive simulations are conducted and the results demonstrate that our scheme can increase the spectrum trading revenue and adapt to varying service requirements.

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios is proposedin this paper. The key idea of the algorithm is to pursue the maximum total spectrum utilization of thesystem by adopting a parallel technique in every spectrum allocation, and to ensure the statistical fairnessrule by deploying a particular scheme during a series of allocations. The simulation results show that theproposed algorithm not only achieves a fairer and more efficient allocation of spectrum resources, but alsohas much shorter allocation duration than the color sensitive graph coloring (CSGC) algorithm.

基于滑动窗口的协作频谱感知对抗拜占庭攻击

认知无线电技术允许从用户动态地接入主要用户被授权的频谱,提高频谱利用率。协作频谱感知是认知无线电技术的一个重要组成部分,通过空间分集检测主用户信号。然而,由于认知无线网络的开放性,协作频谱感知过程可能会受到拜占庭攻击,恶意用户伪造有关主用户信号的状态信息,然后对主用户的通信造成干扰或自私地占用频谱资源,此外,协作频谱感知因多个从用户协作而需要更多的时间来检测主用户信号,因而将导致协作频谱感知的性能和效率进一步降低。针对上述问题,提出了基于滑动窗口的协作频谱感知方案,以减轻拜占庭攻击的负面影响,提高协作效率。在深入分析融合中心盲的问题的基础上,从恶意用户的角度出发,建立了一个随机拜占庭攻击模型来描述恶意行为。为了解决感知样本融合过程中的盲的问题,提出了一种交付评估机制,为基于滑动窗口的协作频谱感知奠定了坚实的基础,并在一个滑动窗口内进一步评估信誉值,以提高报告阶段的协作效率。仿真结果表明,无论恶意比例如何,基于滑动窗口的协作频谱感知在始终攻击的情况下只需要6个平均样本数就可以提供100%的检测准确率,而在恶意比例超过50%的随机攻击的情况下依然能够展现出显著的性能优势。

基于串扰感知技术的光网络频谱动态分配研究

以提升光网络通信质量为目的,研究基于串扰感知及时的光网络频谱动态分配方法,提升频谱动态分配效果。通过计算光网络串扰值,建立以最小芯间串扰为目标函数,芯间串扰产生条件约束,以及芯间串扰阈值约束等为约束条件的光网络频谱动态分配模型;在量子遗传算法内,引入混沌优化算法,得到改进量子遗传算法,避免算法陷入局部最优解;利用改进量子遗传算法,求解频谱动态分配模型,得到最小芯间串扰的光网络频谱动态分配方案。实验证明:不同光网络类型下,该方法均可有效完成光网络频谱动态分配,降低光网络的芯间串扰值,在不同网络负载时,本方法的光网络带宽阻塞率低0.17,频谱利用率超过了0.13。

面向6G无线通信系统的动态频谱管理技术研究

随着通信技术的快速发展,6G无线通信系统将面临更为严峻的频谱资源调配难题。文章通过深入研究6G无线通信系统的动态频谱管理技术,阐述动态频谱管理的基础理念,包括其种类、追求的目标、运作的原理以及优势。重点分析6G无线通信技术对无线电频谱资源的需求,包括提升频谱使用效率、在多元用户和多样化业务情境下的频谱分配与调度等。同时,深入探索针对无线电频谱的先进管理策略,如无线电频谱的感知与预测、借助机器学习和人工智能的智能管理技术以及面向6G通信技术的动态频谱分配技巧等。最后,剖析无线频道的共用与买卖规则,展望未来研究的趋势与方向。

基于DSA与JRRM联合的无线网络资源分配方法

为了实现不同运营商运营的异构网络的无线资源的有效分配,提出了一种基于动态频谱接入(DSA)与联合无线资源管理(JRRM)联合的无线通信频谱资源分配方案。该方案可实现DSA方法和JRRM方法的特性互补,同时将博弈论引入到资源分配的决策过程中,利用夏普里值模型使参与博弈的各方的收益达到最大化。通过与DAS方法及JRRM方法的比较和无线网络通信负载的模拟实验分析表明,此方案改进了无线资源利用率。

A Novel DSA-Driven MAC Protocol for Cognitive Radio Networks

With the deployment of more wireless applications, spectrum scarcity becomes an issue in many countries. Recent reports show that the reason for this spectrum shortage is the underutilization of some spectrum re-sources. Fortunately, the emergence of open spectrum and dynamic spectrum assess (DSA) technology in cognitive radio networks relieves this problem. In this paper, we propose a novel DSA-driven cognitive MAC protocol to achieve highly efficient spectrum usage and QoS provisioning. In the proposed protocol, secondary users are divided into several non-overlapping groups, and all leftover channels are allocated among groups taking the groups’ bandwidth requirements into consideration. Moreover, the allocation of vacant channels can be adjusted dynamically when members join/leave groups or primary users return/leave the current network. Simulations show that the proposed MAC protocol greatly improves the quality of ser-vice for secondary users and maximizes the utilization ratio of spectrum resources.

多无人机辅助通信中用户匹配与频谱资源联合优化方法

针对多无人机作为空中基站为地面设备提供临时服务的动态频谱分配问题,主要考虑无人机与地面用户匹配、子信道分配和功率分配三个方面。为了保证用户通信的公平性,在考虑频谱复用和共信道干扰的情况下,以最大化地面用户最小传输速率为目标,提出了一种用户匹配与频谱资源联合优化算法来解决上述混合整数非线性优化问题,通过聚类算法优化无人机与地面用户的最佳匹配,通过块坐标下降法迭代优化子信道分配和功率分配。仿真实验分析表明,提出的求解方法可以有效提升用户的传输速率,保证用户通信公平性。

面向泛在频谱信息实时更新与安全传输的动态频谱分配方法

针对动态频谱管理中泛在频谱信息更新策略和安全传输要求下频谱分配效益低问题,考虑一种泛在频谱信息动态更新策略限制下动态频谱分配场景,建立应对恶意节点侦收威胁的频谱信息安全传输模型,通过优化信道和功率资源配置来实现频谱信息的安全传输,提出了一种基于频谱信息更新策略和频谱信息隐蔽传输的动态频谱分配方法.利用降元处理将复杂的非线性非严格凸优化问题分解为简单的严格凸优化问题和混合整数线性优化问题,通过差分凸优化、整数线性优化的联合运用来实现动态频谱分配的最优化.仿真结果表明,所提方法能确保异构无线网络在满足特定频谱信息最低传输要求的前提下,快速找到近似最优的频谱分配策略,获得最优的和传输速率.

一种聚类辅助的智能频谱分配技术研究

针对传统频谱分配方案在大规模、高动态电磁频谱战系统中频谱利用率低的问题,开展智能频谱分配技术研究。首先构建复杂高动态电磁频谱作战场景,并在雷达、通信、干扰等多类型设备共存条件下,将复杂电磁环境频谱分配建模为最大化接入设备数量的优化问题。其次,提出一种基于聚类辅助的智能频谱分配算法,针对集中式资源分配算法面临动作空间维度爆炸的问题,使用多DDQN网络表征各节点的决策信息。再基于肘部法则与K-means++算法,提出多节点协同方法,簇内节点通过共享动作信息进行链式决策、簇间节点独立决策,辅助DDQN算法智能分配资源。通过设计状态、动作空间和奖励函数,并采用变学习速率实现算法快速收敛,最终各节点能够根据电磁环境变化,动态分配频/能等多维资源。仿真结果表明:在相同电磁环境下,当节点数为20时,所提算法的可接入设备数较贪婪算法提升了约80%,较遗传算法提升约30%,更适用于动态电磁环境下多设备的频谱分配。

改进蜉蝣算法求解认知车载网络频谱分配问题

针对传统认知车载网络频谱分配效率低、速度慢的问题,提出基于改进蜉蝣优化算法的频谱分配算法。以反向学习、动态惯性权重、多阶段动态扰动及正余弦优化交配机制提升标准蜉蝣优化算法的寻优性能;将频谱分配变量映射为蜉蝣个体位置信息,将网络吞吐量和接入公平性作为评估蜉蝣位置的适应度函数,利用改进蜉蝣算法搜索最优频谱分配方案。实验结果表明,改进算法的搜索精度和收敛速度都有所提升,能够更快得到频谱分配方案,车载用户收益和分配公平性方面也更有保障。