Optimal Resource Allocation in Heterogeneous MIMO Cognitive Radio Networks

The optimal resource allocation in MIMO cognitive radio networks with heterogeneous secondary users, centralized and distributed users, is investigated in this work. The core aim of this work is to study the joint problems of transmission time and power allocation in a MIMO cognitive radio scenario. The optimization objective is to maximize the total capacity of the secondary users (SUs) with the constraint of fairness. At first, the joint problems of transmission time and power allocation for centralized SUs in uplink is optimized. Afterwards, for the heterogeneous case with both the centralized and distributed secondary users, the resource allocation problem is formulated and an iterative power water-filling scheme is proposed to achieve the optimal resource allocation for both kinds of SUs. A dynamic optimal joint transmission time and power allocation scheme for heterogeneous cognitive radio networks is proposed. The simulation results illustrate the performance of the proposed scheme and its superiority over other power control schemes.     

CRN中一种优化的数据传输方案

在认知无线电网络(CRN)中,如何有效地实现认知用户间快速可靠的数据传输是目前的研究热点。针对现有传输方案的不足,提出了一种优化的数据传输方案。在考虑节点发射功率和负载均衡的约束条件下,基于对认知用户间链路可靠性预测,联合频谱分配与按需路由策略实现认知用户数据的最优化传输。仿真实验表明,在减少路由重构次数的同时,较大地提高了系统的吞吐量。     

Reducing the Sampling Complexity of Energy Detection in Cognitive Radio Networks under Low SNR by Using the Optimal Stochastic Resonance Technique

We propose a novel noncooperative technique in cognitive radio (CR) networks, which is based on the optimal stochastic resonance (SR) technique. By introducing the dynamic system approach of SR into the noncooperative spectrum sensing process, the defect of high sampling complexity of traditional energy detector can be reduced efficiently and thus can guarantee the applicability of the optimal SR-based energy detection method. The optimization of the signal-to-noise ratio (SNR) improvement of the system ensures the lowest sampling complexity needed to reach certain performance requirement. Computer simulations show that it can reduce the sampling complexity compared with traditional energy detector used in IEEE 802.22 draft especially under low SNR environments. It can certainly be extended to other wide application areas.     

具有约束条件的认知无线电网络最优频谱价格函数

保证主用户的QoS是认知无线电网络中必须遵循的原则之一.本文利用伺机频谱共享方式中的实际约束条件,求解出既能保证主用户的QoS,又能使主业务运营商获得较大利润的最优频谱价格函数.该价格函数能反映实际的通信环境(如信道质量、业务动态性)对频谱价格的影响,同时在动态环境下,通过迭代可以使频谱价格收敛到最优价格.仿真结果表明...     

Optimal Sensing and Transmission of Energy Efficient Cognitive Radio Networks

Wireless Personal Communications - The issue of spectrum scarcity can be alleviated by the cognitive radio technology with efficient spectrum sensing and allocation of free spectrum bands....     

A Sequential Approach for Optimal Broadcast Scheduling in Packet Radio Networks

An important problem that arises in the design of packet radio networks is that of scheduling access to the high speed communications channel in such a way as to avoid interference while keeping the frame length to a minimum. The broadcast scheduling problem is known to be NP-hard and to date, this problem has been formulated as a nonlinear discrete optimization problem for a given frame length, and solved via heuristic approaches by parametrically varying the length of the frame. This paper presents a linear integer programming formulation for the composite problem of maximizing channel utilization while minimizing the length of the frame. It then introduces a solution approach based on solving two relatively easier (though still NP-complete) integer programs in succession. Computational experiments are conducted on a set of benchmark cases and additional randomly generated problem instances. Results show that this sequential integer programming approach is very effective, solving all the problems optimally within a few seconds. These results imply that optimal solutions can be identified in very little time for problems of realistic size, and that heuristic approaches will be needed only when problems get much larger than those considered in the literature to date.     

Optimal Channel Sensing Order for Various Applications in Cognitive Radio Networks

In cognitive radio (CR) networks, the channel sensing order is crucial for the CR users to find an available channel as fast as possible. In this paper, besides the primary user activities, the statistics of Signal-to-Noise Ratio for each channel are explored using pilot signals. Based on the fluctuating nature of heterogeneous channels as well as the QoS requirements of various applications, two channel sensing order methods are proposed. For real-time applications, a minimum delay-based channel sensing order is proposed to find an idle channel which meets the sustainable rate constraint as fast as possible. For best-effort applications, a maximum capacity-based channel sensing order is proposed to maximize the transmission rates for the CR users, and two different stopping rules are considered. One is that a CR user should stop and transmit at the first free channel, while for the other one, a q-stage look-ahead stopping problem is considered. The simulation results show that the opportunity-discovery delay is reduced for real-time applications. For best-effort applications, the second stopping method is better than the first one when the time cost of the pilot signal is small.     

认知无线电网络中基于功率有效性的最优功率分配

在资源受限的认知无线电网络中,如何提高次用户网络的功率利用率是一个值得考虑的问题。针对这个问题,本文首先提出了认知无线电网络中基于功率有效性的次用户最优功率分配算法,该算法不仅考虑主用户网络中断概率对次用户发射功率的限制,而且兼顾次用户网络本身的中断概率要求。其次,为了进一步降低节点的计算复杂度,本文通过降维处理将目标最优化问题转化为两个子问题进行求解,从而提出一种次优的低复杂度功率分配算法。仿真结果表明,次优算法相比最优算法仅带来有限的性能损失,但是却有效地节省了计算时间和存储空间;此外,当中继节点靠近源节点时更有利于系统功率效率的提高,源节点到目的节点链路相比中继链路对系统的性能影响更大。      

Optimal Transmission Strategies for Dynamic Spectrum Access in Cognitive Radio Networks

Cognitive radio offers a promising technology to mitigate spectrum shortage in wireless communications. It enables secondary users (SUs) to opportunistically access low-occupancy primary spectral bands as long as their negative effect on the primary user (PU) access is constrained. This PU protection requirement is particularly challenging for multiple SUs over a wide geographical area. In this paper, we study the fundamental performance limit on the throughput of cognitive radio networks under the PU packet collision constraint. With perfect sensing, we develop an optimum spectrum access strategy under generic PU traffic patterns. Without perfect sensing, we quantify the impact of missed detection and false alarm, and propose a modified threshold-based spectrum access strategy that achieves close-to-optimal performance. Moreover, we develop and evaluate a distributed access scheme that enables multiple SUs to collectively protect the PU while adapting to behavioral changes in PU usage patterns. Our results provide useful insight on the trade-off between the protection of the primary user and the throughput performance of cognitive radios.     

Optimal Sensing Interval Considering Per-primary Transmission Protection in Cognitive Radio Networks

Sufficient protection of primary user is a challenging issue in cognitive radio networks. The probability of detection and the interference ratio (probability of collision) have been considered as the main constraints for primary protection in the literature. Based on the sensing parameters designed to comply with these constraints, secondary users are often considered to be able to use licensed bands without giving harmful interference to primary users. However, satisfying these constraints might not guarantee that each primary transmission (i.e., each busy period) is sufficiently protected. Obviously, if a large fraction of a busy period is interfered, the busy period may be subject to the useless transmission potentially degrading the quality of service of primary users. We suggest that the busy period impaired more than a certain ratio of so-called required per-transmission interference ratio (PTIR) is subject to the primary transmission failure (PTF), which has not been considered in the literature. As the first attempt, with the assumption of perfect sensing, the effect of sensing interval on the PTIR and PTF is investigated. The probability of PTF is derived as a function of sensing interval given the required PTIR. Then, given the required PTIR and probability of PTF, the optimal sensing interval that maximizes the throughput for secondary users is derived. Performance evaluation shows that primary users can be more protected with the optimal sensing interval obtained by using the proposed constraint.     

基于OFDM认知无线电网络的最优路径算法

针对基于正交频分复用的分布式认知无线电网络,在考虑频谱移动特性的同时,为保证认知用户数据传输的可靠性,提出一种最优路径的基于OFDM认知无线电网络路由算法.该算法首先通过建立认知无线电网络的模型计算信道的期望传输时间和信道干扰,结合类似于按需路由的基本流程得到所有可能的路径.最后,根据最小累积期望传输时间和路径平均吞吐量的指标来选择最优路径.仿真的结果表明,OROCR算法可以明显地减少平均端到端时延,大大地提高平均端到端吞吐量.     

Optimal Sensing Time Strategy Under Primary Outage Constraint in Cognitive Radio Networks

With the underlay approach,Secondary users (SUs) can utilize the same frequency bands simultaneously with Primary users (PUs) in Cognitive radio networks (CRNs).How to choose the appropriate transmission power of SUs under the influence caused by other cells is a problem.To solve this problem,spectrum sensing is introduced to identify the existence of interference which using pilot signal to perform coherent processing.Consider the probability of detection of SUs,there exists a trade-off between the sensing time and the achievable throughput of CRNs.When the prior probability of other cells' activity is unknown to SUs,throughput of the CRNs can be viewed as a concave function.According to solving the optimization problem,the optimal sensing time is obtained.Simulation results show the feasibility and correctness.     

Optimal Mode Selection Policies in Cognitive Radio Networks with RF Energy Harvesting

Applying energy harvesting technology in cognitive radio networks (CRNs) leads to a tradeoff between the time allocated for spectrum sensing followed by spectrum accessing and that for energy harvesting. This tradeoff can be formulated as a mode selection problem for the secondary users. In this paper, we consider a CRN working in the time-slotted manner. The secondary users powered by radio frequency energy harvesting can perform overlay transmission or cooperate with the primary users. To maximize the long-term throughput of the secondary network, we propose two optimal mode selection policies by formulating this problem under a partially observable Markov decision process framework. Numerical simulations show that both of our proposed policies achieve more throughput than the overlay-only policy. Finally, we also evaluate the effect of the cooperative threshold and the energy harvesting process on the optimal policies.     

Optimal Cooperative Inactive Secondary Sensing Order Using Users in Cognitive Radio Networks

A novel cooperative spectrum sensing or- der which utilizes inactive Secondary users （SUs） efficiently based on maximum throughput has been proposed in Cog- nitive radio networks （CRNs）. In order to predict the states of Primary users （PUs）, we build the PU＇s traffic pattern as a Continuous-time Markov chain （CTMC） pro- cess. CRNs obtain the maximum throughput while SUs sense the licensed channels with the optimal order. The numerical simulation results show that the proposed or- der based on spectrum sensing scheme can achieve larger channel utilization and lower sensing overhead as compared with the spectrum sensing scheme without using the opti- mal order sensing. After considering the report overhead of SUs, the optimal number of inactive SUs for the maxi- mum throughput can be found.     

Integrated Optimal Cell Site Selection and Frequency Allocation for Cellular Radio Networks

Two major planning problems are encountered when designing a cellular radio network. The initial question is where to locate the base transmitter stations such that full coverage is achieved at low interference. This is relevant for frequency division (FDMA) as well as code division multiple access (CDMA) technology. If the locations of base stations are given, then for an FDMA-system frequencies have to be assigned such that there is a sufficient number of channels per cell available at a low total interference level. Since cell site selection and frequency allocation have mutual influences on each other, the ultimate goal is to deal with both problems in a single design step. The main intention of this paper is to model the above planning issues as linear integer programs, and to discuss solution methods for the corresponding NP-hard problems. According to their increasing complexity we proceed from channel allocation via cell site selection to an integrated single setup.     

Optimal SMDP‐Based Connection Admission Control Mechanism in Cognitive Radio Sensor Networks

Traffic management is a highly beneficial mechanism for satisfying quality‐of‐service requirements and overcoming the resource scarcity problems in networks. This paper introduces an optimal connection admission control mechanism to decrease the packet loss ratio and end‐to‐end delay in cognitive radio sensor networks (CRSNs). This mechanism admits data flows based on the value of information sent by the sensor nodes, the network state, and the estimated required resources of the data flows. The number of required channels of each data flow is estimated using a proposed formula that is inspired by a graph coloring approach. The proposed admission control mechanism is formulated as a semi‐Markov decision process and a linear programming problem is derived to obtain the optimal admission control policy for obtaining the maximum reward. Simulation results demonstrate that the proposed mechanism outperforms a recently proposed admission control mechanism in CRSNs.     

认知无线网络中基于混合频谱切换的最优目标信道选择算法

在主动频谱切换中,预先确定目标信道存在高失效风险,从而增加切换时延的问题。该文提出一种基于混合主被动频谱切换的最优目标信道选择算法。该算法结合主动频谱切换和被动频谱切换的优点,引入最小累积切换时延评价标准,综合考虑次用户的多次中断、主用户的到达率和非理想频谱检测对次用户累积切换时延的影响,并详细分析和推导了非理想检测下次用户的累积切换时延。仿真表明,相较于主动和被动频谱切换中目标信道选择策略,在信道状态变化频繁或业务量较大等极端情况下,该算法的性能优势尤为明显。     

The Asymptotic Analysis of Some Packet Radio Networks

The purpose of this paper is to present an asymptotic performance analysis of a class of multihop radio networks using the calculus of residues. The analysis provides a closed-form approximation of global performance measures, whose precision increases exponentially with the size of the system. The complexity of computing these approximations is independent of the problem size. The method is easily applicable to a variety of topologies and access protocols.