Access Control Engine with Dynamic Priority Resource Allocation for Cognitive Radio Networks

An access control engine with dynamic priority resource allocation (ACE-DPRA) is proposed for unlicensed users to utilize free spectrum of wireless communication systems. A cognitive radio (CR) network with sensing and learning abilities is essential for unlicensed users to achieve ACE-DPRA. Three algorithms are included in ACE-DPRA to improve the spectral efficiency. While requesting to set up connection, unlicensed CR users generate excessive interferences to licensed users. The proposed ACE-DPRA with an admission control scheme allows the connection of unlicensed CR users without degrading the communication quality of licensed users. The priority algorithm for utilizing the unused spectrum is designed according to the location information of unlicensed users. A transmitted power control method is achieved by a fuzzy-learning mechanism. The spectral efficiency of wireless communication systems can be increased after adopting the proposed ACE-DPRA algorithm. Simulation results show that licensed users keep the advantages of high transmission data rate, low interference power, and low average outage probability after the connection of unlicensed CR users.     

Evaluation of an Adaptive Resource Allocation for LoRaWAN

Long Range Wide Area Network (LoRaWAN) emerges to connect devices that require long-range and low-cost (bandwidth and power) communication services. In this context, the adoption of this technology brings new challenges due to the densification of IoT devices, which causes signal interference and affects the QoS directly. On the other hand, the LoRaWAN transmission configurations’ flexibility allows higher management to use end-device parameters, allowing better resource utilization and improve network scalability. We evaluate an adaptive solution that defines the best LoRaWAN parameter settings to reduce the channel utilization and, consequently, maximize the number of packets delivered. Additionally, to validate the method, we used a mixed-integer linear programming solution and compared the results obtained with those given by the heuristics. The results achieved by the heuristics were very close to those provided by the optimal result, demonstrating the effectiveness of the heuristics.

     

基于认知无线电的双向中继网络的资源分配

针对认知无线电中的双向中继网络,研究了基于非完美感知的频谱机会式接入和频谱共享接入下的资源分配问题.与传统基于认知的双向中继的资源分配方法不同,提出的算法联合考虑了感知时间和次级系统的发射功率.在感知时间和发射功率满足条件的情况下,使次级系统的吞吐量最大.为了有效地保护主用户不受干扰,在每一跳都考虑了平均干扰功率约束,同时考虑次级系统总的功率约束.在非完美感知的频谱机会式接入和频谱共享接入机制下,新算法能够获得最优的感知时间和功率分配.最后,仿真比较了两种方案的性能,验证了新算法的有效性.     

LTE-Advanced系统与S-band雷达共享频谱的载波聚合资源分配

频谱共享可以缓解频谱资源有限以及已分配的频谱资源利用率非常低的压力,而且载波聚合技术可以应用于频谱共享场景中。针对以上问题,首先,提出一个具体的MIMO雷达与LTE-Advanced蜂窝系统之间的频谱共享场景,利用载波聚合技术实现两载波聚合;其次,由于不同的用户设备运行的应用程序不尽相同导致其所需带宽也不尽相同,于是,提出基于用户应用类型的资源分配最优算法,给予实时应用用户优先权;最后,通过MATLAB仿真平台对此算法的性能进行仿真和验证。仿真结果表明,载波聚合可以提供更大的带宽从而提高用户设备的数据传输速率,资源分配最优算法可以保证用户的最小服务质量。     

Optimum Resource Allocation for Relay Networks with Differential Modulation

In this paper, we investigate the resource allocation in a differentially modulated relay network. In addition to the energy optimization, we also consider location optimization to minimize the average symbol error rate (SER). The closed- form solution is derived for the single-relay case, and formulas allowing numerical search are provided for multiple-relay cases. Analytical and simulated comparisons confirm that the optimized systems provide considerable improvement over the unoptimized systems, and that the minimum SER can be achieved via the joint energy-location optimization.     

具有能量收集功能的无线中继网络资源分配

针对具有能量收集功能的无线中继网络,通过分析有限能量存储约束和能量消耗的因果约束对资源分配的影响,提出了基于马尔科夫决策的功率分配算法以最大化中继网络的吞吐量。所提算法根据无线信道状态信息和电池容量信息对功率分配进行动态调整,合理利用收集到的绿色能源,实现网络吞吐量的极大提升。对具有不同能量收集效率的场景进行了仿真,验证了所提算法的优越性。     

Resource Allocation for Massive Machine Type Communications in the Finite Blocklength Regime

Massive machine type communications(mMTC)have been regarded as promising applications in the future.One main feature of mMTC is short packet communication.Different from traditional long packet communication,short packet communication suffers from transmission rate degradation and a significant error rate is introduced.In this case,traditional resource allocation scheme for mMTC is no longer applicable.In this paper,we explore resource allocation for cellular-based mMTC in the finite blocklength regime.First,to mitigate the load of the base station(BS),we establish a framework for cellularbased mMTC,where MTCGs reuse the resources of cellular users(CUs),aggregate the packets generated by MTCDs,and forward them to the BS.Next,we adopt short packet theory to obtain the minimum required blocklength of a packet that transmits a certain amount of information.Then,by modeling the process of MTCGs-assisted communication as a queuing process,we derive the closed-form expression of the average delay of all MTCDs.Guided by this,we propose a joint power allocation and spectrum sharing scheme to minimize the average delay.Finally,the simulation results verify the correctness of the theoretical results and show that the proposed scheme can reduce the average delay efficiently.     

考虑用户优先级的OFDMA下行链路自适应子载波分配

针对OFDMA下行链路系统,在总功率以及用户数据速率成比例的约束下,以获取整个系统容量极大化为准则,提出一种考虑用户优先级的自适应子载波分配算法.该算法初始分配时允许每个用户根据用户数据速率的相对比例以及自己的信道状态在所有子载波上独立的进行最优选择,当出现多个用户同时选择一个子载波,即出现冲突时,由平均信道增益的大小来决定用户选择该子载波的优先级.文中分别研究了平均信道增益大者为高优先级以及平均信道增益小者为高优先级的两种冲突解决办法,仿真结果表明,由平均信道增益小的用户来优先选择冲突子载波的算法综合考虑了公平性和频谱效率,与系统容量上限相比,性能损失较小,复杂度低,速度快,能够满足实时要求.     

Cognitive Engine with Dynamic Priority Resource Allocation for Wireless Networks

A cognitive engine with dynamic priority resource allocation (CE-DPRA) is proposed for wireless networks by utilizing a maximum likelihood estimation method. The receiving signal strength (RSS) from an unlicensed cognitive radio (CR) user can be measured through estimating the unknown position of a licensed mobile user. The priority algorithm for access control enables the selection of a proper CR user waiting for transmission. Both data rate and spectral efficiency can be increased after adapting CE-DPRA. Also the power constraint method can avoid excessive interference caused by signal transmitting from CR users so as to improve the communication quality of mobile users. Simulation results show that the proposed CE-DPRA achieves the performance of high transmission data rate, less interference power, and low average outage probability.     

一种基于公平性的无人机基站通信智能资源调度方法

空天地一体化网络是未来6G的关键内容。结合高精度波束赋形,无人机(UAV)的视距链路(LoS)可很好地作为空天地一体化网络的补充,但地面用户与基站间的相对运动极易造成信道容量失衡。提出一种噪声深度确定性策略梯度(Noisy-DDPG)方法。该方法以最大化通信公平性和系统容量为目标,利用DDPG优化分配方案,通过调整奖励函数策略参数来实现公平性和信道容量的平衡;通过在策略网络中利用可学习参数噪声进行扰动,得到更合理的分配方案。仿真实验验证了该算法的有效性。     

Adaptive Clustering and Scheduling for Dynamic Region-based Resource Allocation in V2V Communications

In this paper, we propose a scheme named “Adaptive Clustering and Scheduling for Dynamic Region-based Resource Allocation” (ACSR) to solve the problems in 3GPP’s fixed zone resource allocation schemes for 3GPP’s infrastructure-aided Vehicle-to-Vehicle (V2V) communication technology:Cellular V2V or C-V2V communications. In 3GPP’s fixed-zone resource allocation schemes, the radio channels are separated into groups each of which is reused amid pre-determined geographical zones with a fixed area and locations regardless of the number of vehicles sharing the group of channels assigned to a zone. On the other hand, in the proposed ACSR scheme, vehicles are dynamically and adaptively clustered by their geographical locations and banks of radio channels are reused amid clusters. This flexibility of vehicle clustering in which the number of vehicles in a cluster could fit the number of channels in a re-usable group of channels largely reduces the chance of co-channel interference and hence improve the transmission performance. A vehicle in a cluster is elected to be the cluster head for assigning radio channels to those which in its cluster are about to transmit so that both the overhead of signaling transmissions and computation complexity at eNB/gNB are reduced. The performance of ACSR are compared with several other typical resource allocation schemes by extensive simulations under the simulation scenario setting defined by 3GPP specifications. The performance metrics are (1) the average computation offloading ratio (COR) in reference to the centralized brute-force optimization scheme at eNB/gNB and (2) the average successful packet reception ratio (PRR) defined in 3GPP specification. The average COR for ACSR is 30.9%. The ACSR’s average PRR improvements over a typical fixed zone resource allocation scheme, the FZRA scheme, are greater than 5%, 10% and 21.8% when the transmission distance is 120 meters, 180 meters, and 320 meters, respectively. Simulation results shown that in general the ACSR scheme significantly improves the performance as compared to 3GPP’s fixed zone resource allocation schemes.

     

A Novel Resource Allocation for SWIPT-NOMA Enabled AF Relay Based Cooperative Network

Today, the use of large networks under the cloud and big data has become very common, as well as learning methods and artificial intelligence have been designed for them. One of the main issues in the banking and Big Data industry is granting bank facilities to people. The main challenge in evaluating the customers’ credit in the banking industry is that, due to the large volume of the data, it is impossible to review them manually, as we have to use computer algorithms for this purpose. Hence, the accuracy of the algorithm used for this purpose is essential and influential. In this paper, several predictive algorithms are employed on the problem, so that the result would be a prediction based on the combination of the results of each of them. To integrate the results of the algorithms, the weight to each of the algorithms is allocated, which presents the amount of importance of the algorithm in the result. The Ordered weighting averaging is used in this research. By applying these techniques, the proposed algorithms are described in this paper to achieve better accuracy than the existing ones.

     

Multi-Objective Deep Reinforcement Learning Based Time-Frequency Resource Allocation for Multi-Beam Satellite Communications

Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate...     

Resource Allocation for Device-to-Device Communications Based on Guard Area Underlying Cellular Networks

Device-to-Device (D2D) communications have drawn considerable attention with the obvious advantages of a higher data rate and spectrum efficiency. However, this also brings intra-cell interference due to resource sharing with traditional Cellular users (CUs). An effective resource allocation scheme for D2D communications to maximize the system throughput is developed. This scheme first utilizes the guard area model to restrict the interference between D2D users (DUs) and CUs. Then, a max-flow algorithm is used to match the pair of CUs and DUs and maximize the total sum rate of the communication system. Numeral results demonstrate that the proposed scheme can yield significant throughput gain while maintaining quality for both CUs and DUs.     

Resource Allocation and Power Control Based on Noncooperative Game for D2D Communications Underlaying Cellular Networks

Wireless Personal Communications - The development of computer technology paves the way for transferring conventional monitoring system into digital monitoring. Considering this idea, the concept...     

Symbol Error Rate Analysis and Power Allocation for Adaptive Relay Selection Schemes

In this paper, we analyse the symbol error rate (SER) performance of adaptive relay selection schemes (ARS) in a general dual-hop multiple-relay network. Specifically, we provide a closed-form SER expression for ARS which is tight over the whole signal-to-noise ratio (SNR) region. In addition, the derived SER can be readily extended to conventional relay selection schemes, i.e. amplify-and-forward relay selection (AF-RS), perfect decode-and-forward relay selection (PDF-RS), adaptive decode-and-forward relay selection (ADF-RS), and cooperative-maximum-ratio-combining decode-and-forward relay selection (CDF-RS). Transmit power allocation based on the simplified SER is presented to improve the system performance. The analytical results are verified by computer simulations.     

Adaptive Resource Reuse Scheduling for Multihop Relay Wireless Network Based on Multicoloring

In this paper, an adaptive resource reuse scheduling (ARRS) algorithm is presented with the goal of enhancing the system capacity for relay networks, which supports arbitrary topology and relay stations (RSs) mobility. Since one key step in ARRS is modeled as graph multicoloring, a fast [%p-]- approximation algorithm named dual sorting greedy (DSG) is provided for the problem. Simulation results show that ARRS achieves high system capacity and hence satisfies the multimedia service QoS requirements of relay networks efficiently.     

机器类通信中基于NOMA短编码块传输的高可靠低迟延无线资源分配优化方案

针对机器类通信(MTC)应用场景的业务特征和服务质量(QoS)要求,该文考虑基于非正交多址(NOMA)的MTC中短分组/短编码块传输,探讨MTC中基于NOMA的高可靠低迟延无线资源优化问题。首先,上行传输是基于NOMA的MTC通信的瓶颈,考虑无线蜂窝网络中支持NOMA和高可靠低迟延性能要求,该文建立了上行无线资源优化的系统模型;然后,分析上行传输迟延,导出基于距离的链路可靠性函数;进一步,以迟延、可靠性和带宽为约束下条件,提出一种最大化中心用户和速率的无线资源分配算法,并给出算法的收敛性证明和复杂度分析;最后,实验仿真验证了所提算法的性能优势。     

MIMO-OFDM系统自适应资源分配研究

MIMO-OFDM系统中动态资源分配能够有效地利用系统发射功率和频谱资源,提高系统性能.文章重点讨论了MIMO-OFDM技术、基于信道状态信息物理层自适应传输和跨层自适应资源管理及MIMO-OFDM自适应算法,并指出热点研究方向.