认知网络中的能量回收技术：性能分析及优化设计

通信网络中有限的能源和频带资源限制了网络容量的进一步提升.对能量回收技术在认知网络中的应用进行研究,量化评估用户可回收的能量以及可达吞吐率,并进行优化设计很有必要.在所分析的系统中,当授权用户进行通信时,非授权用户可回收无线信号中所带有的能量,并利用回收的能量进行频谱检测;当检测到授权用户空闲时,非授权用户将接入频谱,利用回收到的能量进行数据传输.采用马尔科夫链模型对通信场景进行描述分析,发现授权用户的活跃程度对非授权用户可回收的能量、获得的传输机会带来影响,进而决定了非授权用户的可达吞吐量.在此基础上,提出一种通过控制授权用户业务量,以最大化网络能量效用和频谱效用的优化方案,并通过仿真证实了理论分析的正确性.     

Throughput Maximization in Cognitive Radio System with Transmission Probability Scheduling and Traffic Pattern Prediction

In this paper, we propose a novel transmission probability scheduling (TPS) scheme for the opportunistic spectrum access based cognitive radio system (OSA-based CRS), in which the secondary user (SU) optimally schedules its transmission probabilities in the idle period of the primary user (PU), to maximize the throughput of the SU over a single channel when the collision probability perceived by the PU is constrained under a required threshold. Particularly, we first study the maximum achievable throughput of the SU when the proposed TPS scheme is employed under the assumption that the distribution of the PU idle period is known and the spectrum sensing is perfect. When the spectrum sensing at the SU is imperfect, we thoroughly quantify the impact of sensing errors on the SU performance with the proposed TPS scheme. Furthermore, in the situation that the traffic pattern of the PU and its parameters are unknown and the spectrum sensing is imperfect, we propose a predictor based on hidden Markov model (HMM) for the proposed TPS scheme to predict the future PU state. Extensive simulations are conducted and show that the proposed TPS scheme with the HMM-based predictor can achieve a reasonably high SU throughput under the PU collision probability constraint even when the sensing errors are severe.     

认知Ad Hoc网络能量有效频谱接入策略

针对认知无线Ad Hoc网络中次用户能量受限问题,提出一种基于能量有效性的机会频谱接入策略。联合考虑信道状态的时变性和次用户的频谱感知准确性,基于部分可观测马尔科夫决策过程（POMDP）建立了一种最大化能量有效性的分析架构,指导次用户选择能效最佳信道,并根据信念状态、信道增益和检测概率,自适应控制传输功率。仿真结果表明,该策略能够有效提高次用户传输的能量有效性,通过对传输功率的有效控制,实现了传输性能和能量开销的有效折中。      

Joint optimisation of transmission and waiting times in cognitive radio

Transmission time optimisation is one of the key considerations of cognitive network design. There are many studies in cognitive radio networks (CRNs) focusing on finding the best transmission time for secondary users (SUs) to maximise transmission or energy efficiency. While longer sensing duration leads to a higher sensing accuracy and causes less interference, the SU spends less time for transmission and more energy on sensing spectrum. On the other hand, when the transmission duration becomes longer, although the SU has more opportunities to access the channel, it may encounter higher interference due to primary user (PU) returns and the probability of collision becomes higher. In this article, in a decentralised slotted protocol for CRN, the SU spectrum access is proved as a renewal process, then the interference due to PU return during SU transmission, the missed opportunities due to waiting for the channel to become idle and the energy consumed by the SU in the whole spectrum access process including idling energy, transmission energy and sensing energy consumption are formulated and integrated into newly defined efficiency to obtain the optimum transmission time and waiting time.     

Ergodic capacity and outage probability optimization for secondary user in cognitive radio networks under interference outage constraint

This paper considers a cognitive radio network where a secondary user (SU) coexists with a primary user (PU). The interference outage constraint is applied to protect the primary transmission. The power allocation problem to jointly maximize the ergodic capacity and minimize the outage probability of the SU, subject to the average transmit power constraint and the interference outage constraint, is studied. Suppose that the perfect knowledge of the instantaneous channel state information (CSI) of the interference link between the SU transmitter and the PU receiver is available at the SU, the optimal power allocation strategy is then proposed. Additionally, to manage more practical situations, we further assume only the interference link channel distribution is known and derive the corresponding optimal power allocation strategy. Extensive simulation results are given to verify the effectiveness of the proposed strategies. It is shown that the proposed strategies achieve high ergodic capacity and low outage probability simultaneously, whereas optimizing the ergodic capacity (or outage probability) only leads to much higher outage probability (or lower ergodic capacity). It is also shown that the SU performance is not degraded due to partial knowledge of the interference link CSI if tight transmit power constraint is applied.     

Analysis of secondary user performance in cognitive radio networks with reactive spectrum handoff

Cognitive radio networks use dynamic spectrum access of secondary users (SUs) to deal with the problem of radio spectrum scarcity . In this paper, we investigate the SU performance in cognitive radio networks with reactive-decision spectrum handoff. During transmission, a SU may get interrupted several times due to the arrival of primary (licensed) users. After each interruption in the reactive spectrum handoff, the SU performs spectrum sensing to determine an idle channel for retransmission. We develop two continuous-time Markov chain models with and without an absorbing state to study the impact of system parameters such as sensing time and sensing room size on several SU performance measures. These measures include the mean delay of a SU, the variance of the SU delay, the SU interruption probability, the average number of interruptions that a SU experiences, the probability of a SU getting discarded from the system after an interruption and the SU blocking probability upon arrival.     

Optimization of sensing time in multichannel sequential sensing for cognitive radio

This paper studies the design of sensing time in multichannel sequential sensing for cognitive radio under the multichannel sensing slots and one transmission slot scheme. Firstly, we consider the case where the channel idle probability is known. In this case, when the channels’ signal‐to‐noise ratios (SNRs) are equal, we prove that there exist optimal sensing times that maximize the achievable throughput for secondary user, while primary user is sufficiently protected. When the channels’ SNRs are different, brute‐force search is used to find the optimal sensing order that maximizes the achievable throughput of secondary user, and the corresponding optimal sensing times will be obtained. Then, a suboptimal and low‐complexity approach of one‐slot sensing time without knowing the channel idle probability is proposed. The proposed approach solves the problem of sensing order by selecting the channel with the minimum sensing time in the next sensing slot when the channels’ SNRs are different. Numerical results show that the proposed approach achieves a performance close to that of the optimal sensing time under different conditions and has significant performance improvement over the approach of fixed sensing time. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimality of Myopic Sensing in Multichannel Opportunistic Access

This paper considers opportunistic communication over multiple channels where the state (“good” or “bad”) of each channel evolves as independent and identically distributed (i.i.d.) Markov processes. A user, with limited channel sensing capability, chooses one channel to sense and decides whether to use the channel (based on the sensing result) in each time slot. A reward is obtained whenever the user senses and accesses a “good” channel. The objective is to design a channel selection policy that maximizes the expected total (discounted or average) reward accrued over a finite or infinite horizon. This problem can be cast as a partially observed Markov decision process (POMDP) or a restless multiarmed bandit process, to which optimal solutions are often intractable. This paper shows that a myopic policy that maximizes the immediate one-step reward is optimal when the state transitions are positively correlated over time. When the state transitions are negatively correlated, we show that the same policy is optimal when the number of channels is limited to two or three, while presenting a counterexample for the case of four channels. This result finds applications in opportunistic transmission scheduling in a fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and antijamming.      

Optimal transmission scheduling over a fading channel with energy and deadline constraints

We seek to maximize the average data throughput of a single transmitter sending data over a fading channel to a single user class. The transmitter has a fixed amount of energy and a limited amount of time to send data. Given that the channel state determines the throughput obtained per unit of energy expended, the goal is to obtain a policy for scheduling transmissions that maximizes the expected data throughput. We develop a dynamic programming formulation that leads to an optimal transmission schedule, first where the present channel state is known just before transmission, and then to the case where the current channel state is unknown before transmission, but observed after transmission and evolves according to a Markov process. We then extend our approach to the problem of minimizing the expected energy required to send a fixed amount of data over a fading channel given deadline constraints.     

Adaptive channel selection and slot length configuration in cognitive radio

This paper investigates the channel selection and slot time configuration in a cognitive radio network with a number of potential channels. Each channel alternates between ON state (i.e., the primary user is using the channel) and OFF state (i.e., the primary user does not use the channel), and the state evolution process is modeled as a continuous‐time Markov process. The traffic parameters (the transition rates) of the Markov process also evolve with time, modeled as a discrete‐time Markov process. A secondary user adopts a slotted structure with dynamic slot length. At each slot, the secondary user needs to determine which channel to sense and, if the channel is sensed idle, how long the slot length should be. Considering both the amount of data that the secondary user can transmit and the duration when the secondary user interferes with primary activities, a reward definition is given. Based on the reward definition, an adaptive channel selection and slot length configuration method is proposed, which includes a reward maximization procedure to maximize the achieved reward and an update procedure for the channel state belief vector and traffic parameter state belief vector. Numerical results are given to demonstrate the effectiveness and features of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

Improving CSMA/CA network performance under hidden collision

Dynamic spectrum access policy allows a secondary user (SU) to access a primary user (PU) channel when it is idle. However, the idle state may result from the PU's silent activities, which can be wrongly perceived as an opportunity for the SU to access the channel and lead to “hidden collision” when it effectively tries to access the channel under this condition. At best of our knowledge, this issue has yet to be addressed in the literature. In this paper, we will first define a three‐state model that describes hidden collision conditions, then propose a probabilistic model in which a transient state is added to force the SU to wait a certain period of time before accessing the channel, thereby translating into better protection for the PU. Based on this model and using Carrier Sense Multiple Access/Collision Avoidance protocol, we will evaluate the PU's and SU's throughput and delay with and without transient state as well as the gain in using our approach. Our computation results show a substantial improvement of the PU's throughput from 19.6 to 61.1%.     

Dynamic wireless spectrum access using GNU Radio and software‐defined radios

This paper presents the design, implementation, and results from a dynamic wireless spectrum access system built using GNU Radio and software‐defined radios (SDRs) as part of an undergraduate senior design project. The project involved designing and implementing a dynamic wireless spectrum access system in which the secondary user (SU) learns the unknown transmission behavior (channel occupancy and time slots) of the primary user (PU) and then opportunistically transmits during time slots and using channels when they are not being used by the PUs. The main design objective was to maximize the throughput of the SU while minimizing the interference to the PU. A transmitted signal energy detection algorithm with an adaptive threshold was employed to set the channel states as occupied or not occupied. Channel state information was used to determine the PU behavior in a deterministic manner such that the unused time slots and channels could be exploited. A channel allocation scheme for the SU is proposed using the PU channel occupancy information to calculate the channel(s) and time slots available to the SU at any given time. Simulation and physical testing of the system validate the proposed algorithms. Students' feedback affirms GNU Radio and SDRs to be an effective platform for introducing abstract mathematical communications theory concepts, such as cognitive radios and dynamic spectrum allocation, in a hands‐on manner.     

Secrecy outage analysis in a hybrid cognitive relay network with energy harvesting

In this paper, we investigate the physical layer security of a hybrid cognitive relay network using an energy harvesting relay in presence of an eavesdropper. In the hybrid scheme, a secondary user (SU) as well as a cognitive relay works either in underlay or in overlay mode. In underlay, the transmit power of the SU as well as the relay is limited by the maximum acceptable interference at primary user (PU) receiver as required by an outage constraint of PU, a quality of service for PU. The secondary network consists of a decode and forward relay that harvests energy from radio frequency signal of secondary transmitter as well as PU transmitter to assist the SU in forwarding the information signal to the destination. A time switching relaying protocol is used at the relay. We evaluate the secrecy outage probability of secondary relay network assuming that channel state information of the interfering links from both the SU and relay transmitter to PU receiver is imperfect. Our results reveal the impact of imperfect channel state information, energy harvesting time, tolerable interference threshold, and PU outage constraint on the secrecy outage probability of SU.     

Prediction based channel allocation performance for cognitive radio

The interdependency, in a cognitive radio (CR) network, of spectrum sensing, occupancy modelling, channel switching and secondary user (SU) performance, is investigated. Achievable SU data throughput and primary user (PU) disruption rate have been examined for both theoretical test data as well as data obtained from real-world spectrum measurements done in Pretoria, South Africa. A channel switching simulator was developed to investigate SU performance, where a hidden Markov model (HMM) was employed to model and predict PU behaviour, from which proactive channel allocations could be made. Results show that CR performance may be improved if PU behaviour is accurately modelled, since accurate prediction allows the SU to make proactive channel switching decisions. It is further shown that a trade-off may exist between achievable SU throughput and average PU disruption rate. When using the prediction model, significant performance improvements, particularly under heavy traffic density conditions, of up to double the SU throughput and half the PU disruption rate were observed. Results obtained from a measurement campaign were comparable with those obtained from theoretical occupancy data, with an average similarity score of 95% for prediction accuracy, 90% for SU throughput and 70% for PU disruption rate.     

Performance of cognitive relay network with energy harvesting relay under imperfect CSI

This paper evaluates the performance of an underlay cognitive relay network under imperfect channel state information (CSI) where a secondary user (SU) transmits using a secondary relay (SR) based on decode and forward scheme. The outage probability (OP) of SU is investigated in a scenario where the decode and forward relay harvests energy from radio frequency signal of SU. The relay uses a fraction of time for harvesting in time switching–based relaying (TSR) while a fraction of received power is used for harvesting in power splitting–based relaying (PSR) scheme. The SU and relay control their transmit power using a scaling factor, based on CSI of the interfering links (ie, links from SU transmitter and SR to the primary user [PU] receiver) to protect the quality of service of PU. The available CSI at the SU and SR are imperfect due to practical limitation. Analytical expressions of the OP are derived for TSR‐ and PSR‐based schemes. The impact of harvesting time, power splitting ratio, imperfect CSI, PU outage constraint and interference threshold on the OP of the SU network, and average transmit power of SR is indicated. Further, the impact of multiple SRs is also shown.     

能量收集认知无线电网络下的多接入用户信息年龄研究

针对信道资源有限的多接入信道无线传感器网络场景,实时信息的传送需要考虑信道环境和信息新鲜度问题。该文基于认知无线电物联网（Cognitive Radio-Internet of Things, CR-IoT）系统,构建了一个具有频谱访问权限的主用户（Primary User, PU）和两个可共享PU频谱次用户（Secondary User, SU）的网络模型。在考虑PU工作状态和SU数据队列稳定的条件下,提出了一个以最小化节点平均AoI为目标的优化问题。其次使用两种策略进行优化,包括概率随机接入策略（Probabilistic Random Access Policy, PRA）,该策略下两个SU节点根据相应的概率分布做出独立的传输决策;以及基于李雅普诺夫优化框架优化时隙内调度决策的漂移加罚策略（Drift Plus Penalty Policy, DPP）。仿真结果可知,DPP策略下得到的平均AoI的值要明显低于PRA策略,表明使用DPP策略对平均AoI的优化更加显著,可以有效提升数据包的时效性和新鲜度。      

Throughput optimization using simultaneous sensing and transmission in energy harvesting cognitive radio networks

A three‐dimensional continuous‐time Markov model is proposed for an energy harvesting cognitive radio system, where each secondary user (SU) harvests energy from the ambient environment and attempts to transmit data packets on spectrum holes in an infinite queuing buffer. Unlike most previous works, the SU can perform spectrum sensing, data transmission, and energy harvesting simultaneously. We determine active probability of the SU transmitter, where the average energy consumption for both spectrum sensing and data transmission should not exceed the amount of harvested energy. Then, we formulate achievable throughput of secondary network as a convex optimization problem under average transmit and interference energy constraints. The optimal pair of controlled energy harvesting rate and data packet rate is derived for proposed model. Results indicate that no trade‐off is available among harvesting, sensing/receiving, and transmitting. The SU capability for self‐interference cancelation affects the maximum throughput. We develop this work under hybrid channels including overlay and underlay cases and propose a hybrid solution to achieve the maximum throughput. Simulation results verify that our proposed strategy outperforms the efficiency of the secondary network compared to the previous works.     

Adaptive dual‐radio spectrum‐sensing scheme in cognitive radio networks

In this paper, a novel spectrum‐sensing scheme, called adaptive dual‐radio spectrum‐sensing scheme (ADRSS), is proposed for cognitive radio networks. In ADRSS, each secondary user (SU) is equipped with a dual radio. During the data transmission, with the received signal‐to‐noise ratio of primary user (PU) signal, the SU transmitter (SUT) and the SU receiver (SUR) are selected adaptively to sense one channel by one radio while communicating with each other by the other one. The sensing results of the SUR are sent to the SUT through feedback channels (e.g., ACK). After that, with the sensing results from the SUT or the SUR, the SUT can decide whether the channel switching should be carried out. The theoretical analysis and simulation results indicate that the normalized channel efficiency, defined as the expected ratio of time duration without interference to PUs in data transmission to the whole frame length, can be improved while satisfying the interference constraint to PUs. After that, an enhanced ADRSS is designed by integrating ADRSS with cooperative spectrum sensing, and the performance of ADRSS under imperfect feedback channel is also discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

Reconnection Analysis for a Cognitive Radio Network with Unreliable Sensing

In cognitive radio networks, an initiating secondary user senses a channel that is unused by a primary user and then makes use of the idle channel. An ongoing secondary user still needs to sense when a primary user accesses its channel and then either moves to another idle channel or moves to a buffer. In the latter case, the secondary user’s call waits in the buffer until either a channel becomes available or a predefined maximum waiting time expires. In this letter, an analysis of reconnection opportunity is presented for the queued secondary calls in the buffer in a cognitive radio network under unreliable sensing through developing a 3-D Markov process, where the unreliable sensing is modeled by different false alarm and misdetection events for both initiating and ongoing secondary users. A closed-form expression of the reconnection probability for an arbitrary secondary call in the buffer is obtained and the numerical computation is provided to highlight the analysis.     

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.