An adaptive step-size spectrum auction mechanism for two-tier heterogeneous networks

Recently, with the rapid growth of demands for wireless communications, dynamic spectrum allocation is one of the key technologies in cognitive radio networks to resolve the realistic problem of low utilization efficiency of spectrum. It mainly focuses on how the spectrum owner dynamically allocates idle spectrum to secondary users who have no licensed spectrum for communications. In this paper, a dynamic spectrum allocation model based on auction theory in a two-tier heterogeneous network is proposed, in which the primary users (PUs) are the sellers, the central processor (CP) auctioneer is the coordinator, and femtocell base station (FBS) as the buyer bids for the idle spectrum and act as a wireless access point that provides communication services for secondary users (SUs). Its basic process is as follows: the auctioneer gradually raises the spectrum price from the reserved price; each bidder decides whether participates in the purchase or not. It is characterized by distributed execution and low complexity which can reduce unnecessary information exchange between primary users or secondary users. Meanwhile it can enhance the utilization of spectrum and improve the efficiency of the auction by generate the incentive mechanism.     

A spectrum auction algorithm based on joint power control and beamforming in small cell networks

Spectrum auction is considered as a suitable approach to efficiently allocate spectrum among unlicensed users. However, in previous studies of spectrum auction, competition can hardly be reflected in the traditional spectrum allocation and the spectrum efficiency is still not high after the allocation. In this paper, we enhance the factor of competition in the auctions, in which bidders need to pay for the competition and the interference to macro cell users (MUs). We consider a communication system with one macro cell and several small cells, thus a licensed radio spectrum is shared by both MUs and small cell users (SUs). A truthful auction algorithm is proposed for spectrum allocation and the spectrum is divided into multiple channels in different time slots, so that SUs can make their choice for bidding under the guidance of history. In order to raise the communication quality, we propose a power control and beamforming algorithm in the heterogeneous network to enhance the system performance. Simulation results are presented to verify the effectiveness of the proposed algorithm in the small cell network.     

Distributed dynamic spectrum access through multi-agent deep recurrent Q-learning in cognitive radio network

This paper addresses the problem of distributed dynamic spectrum access in a cognitive radio (CR) environment utilizing deep recurrent reinforcement learning. Specifically, the network consists of multiple primary users (PU) transmitting intermittently in their respective channels, while the secondary users (SU) attempt to access the channels when PUs are not transmitting. The problem is challenging considering the decentralized nature of CR network where each SU attempts to access a vacant channel, without coordination with other SUs, which result in collision and throughput loss. To address this issue, a multi-agent environment is considered where each of the SUs perform independent reinforcement learning to learn the appropriate policy to transmit opportunistically so as to minimize collisions with other users. In this article, we propose two long short-term memory (LSTM) based deep recurrent Q-network (DRQN) architectures for exploiting the temporal correlation in the transmissions by various nodes in the network. Furthermore, we investigate the effect of the architecture on success rate with varying number of users in the network and partial channel observations. Simulation results are compared with other existing reinforcement learning based techniques to establish the superiority of the proposed method.     

Opportunistic spectrum access for TDMA-based cognitive radio networks

In this paper, opportunistic spectrum access is proposed for TDMA-based cognitive radio networks. In TDMA-based networks, the time is divided into slots with fixed length one by one. If a primary user (PU) needs to transmit data, one or several slots will be used. Otherwise, the slots are idle and can be utilized by secondary users (SUs). When SUs want to use the licensed channel, they should sense the channel at the beginning period of each slot. Then SUs exchange their sensing results and make the same decision about the channel state (idle or used by PUs), which could reduce the probability of false sensing. The aforementioned duration is called spectrum sensing phase. When SUs decide there is an idle channel, they contend to use the channel at the rest time of the slot. The duration is called access phase. In this period, SUs contend the channel with backoff counters. When the remaining time is less than one data transmission duration, SUs cannot transmit data packets. Therefore, the remaining time is wasted. To solve this problem, SUs transmit control packets with small length in the remaining time instead. The SU who exchange control packets successfully reserves the channel and sends a data packet prior to other SUs in access phase of the next idle slot. Obviously, this reserved transmission is without collision. The independent spectrum sensing, channel state decision and control packets reservation influence the performance of SUs. The proposed scheme is formulated with all above factors. Simulations which consist with the numerical results show the proposed access scheme achieve higher throughput than the existed scheme without channel reservation.     

Performance evaluation of improved double-threshold energy detector over Rayleigh-faded sensing and imperfect reporting channels

Cognitive radio (CR) has been viewed as a promising solution to spectrum scarcity. In order to design a reliable CR system, many improvements have been proposed to enhance spectrum sensing performance of secondary users (SUs) in a CR network (CRN). Sensing reliability and transmission throughput of SUs are two important performance criteria, which should be optimized to enhance signal protection of primary user (PU) as well as spectrum utilization rate. In this paper, we consider Rayleigh-faded sensing channels and SUs use improved energy detector (IED) to make their local decisions. The final decision is made in a fusion center (FC) through the cooperative spectrum sensing (CSS) scheme with erroneous reporting channels. We show that the improved double-threshold energy detector (IDED) outperforms the conventional energy detector (CED) in terms of the total error rate. Furthermore, we evaluate the transmission throughput of the CRN through various ED schemes with detection constraints over both perfect and imperfect reporting channels. We show that the IDED has the highest achievable throughput among different ED schemes over imperfect reporting channels.     

Indoor cognitive radio operation within the broadcast TV protection contour

Although the broadcast television (TV) spectrum is currently open for unlicensed operation in the USA, a considerably large geographic area still remains excluded from the unlicensed operation due to potential interference to the licensed users. However, it might be possible to reuse primary spectrum within the protection contour if the frequency reuse occurs inside a building that shields radio signals and reduces interference to the primary system. Interference to outdoor licensed users from the indoor operations can be minimized if the unlicensed users adjust their transmit power according to their locations in the building. This paper presents an analysis and effectiveness evaluation of a novel cognitive radio (CR) system which enables CRs to access the licensed spectrum inside a building in the area within the protection contour. The system utilizes an indoor sensor network for (i) interference sensing, (ii) CR transmit power control, to limit the interference to the outdoor primary receiving antennas. Power control model of the indoor system has been developed to estimate safe transmit power for the indoor users. Two cases have been considered; single-user single-sensor (single indoor user and single sensor), and multi-user multi-sensor. Based on the power control model, a power control algorithm has been developed and its effectiveness is assessed through simulations. The algorithm is effective in realistic propagation scenarios, e.g. when internal partition walls and multipath fading are present. The outage probabilities in these propagation scenarios are found and the procedure of determining the transmit powers for CRs is presented.     

Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

Spectrum efficient power allocation schemes for OFDM cognitive radio with statistical interference constraints

In this paper, we study the power allocation problem for an orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) system. In a departure from the conventional power allocation schemes available in the literature for OFDM-based CR, we propose power allocation schemes that are augmented with spectral shaping. Active interference cancellation (AIC) is an effective spectral shaping technique for OFDM-based systems. Therefore, in particular, we propose AIC-based optimal and suboptimal power allocation schemes that aim to maximize the downlink transmission capacity of an OFDM-based CR system operating opportunistically within the licensed primary users (PUs) radio spectrum in an overlay approach. Since the CR transmitter may not have the perfect knowledge about the instantaneous channel quality between itself and the active PUs, the interference constraints imposed by each of the PUs are met in a statistical sense. We also study an optimal power allocation scheme that is augmented with raised cosine (RC) windowing-based spectral shaping. For a given power budget at the CR transmitter and the prescribed statistical interference constraints by the PUs, we demonstrate that although the on-the-run computational complexity of the proposed AIC-based optimal power allocation scheme is relatively higher, it may yield better transmission rate for the CR user compared to the RC windowing-based power allocation scheme. Further, the AIC-based suboptimal scheme has the least on-the-run computational complexity, and still may deliver performance that is comparable to that of the RC windowing-based power allocation scheme. The presented simulation results also show that both the AIC-based as well as the RC windowing-based power allocation schemes lead to significantly higher transmission rates for the CR user compared to the conventional (without any spectral shaping) optimal power allocation scheme.     

A spectrum handoff scheme for optimal network selection in Cognitive Radio vehicular networks: A game theoretic auction theory approach

The recent strides in vehicular networks have emerged as a convergence of multi radio access networks having different user preferences, multiple application requirements and multiple device types. In future Cognitive Radio (CR) vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. Hence, it becomes a challenge for CR vehicular node to select the optimal network for the spectrum handoff decision. A game theoretic auction theory approach is interdisciplinary effective approach to select the optimal network for spectrum handoff. The competition between different CR vehicular node and access networks can be formulated as multi-bidder bidding to provide its services to CR vehicular node. The game theory is the branch of applied mathematics which make intelligent decision to select the optimal alternative from predetermined alternatives. Hence, this paper investigates a spectrum handoff scheme for optimal network selection using game theoretic auction theory approach in CR vehicular networks. The paper has also proposed a new cost function based multiple attribute decision making method which outperforms other existing methods. Numerical results revel that the proposed scheme is effective for spectrum handoff for optimal network selection among multiple available networks.     

A tree-structured DFT filter bank based spectrum detector for estimation of radio channel edge frequencies in cognitive radios

Cognitive Radio (CR) aims to provide efficient spectrum utilization in spectrum scarce wireless environments. One of the key CR functionalities is the spectrum sensing, which allows CRs to monitor the electromagnetic spectrum and detect unused bands of spectrum. Wideband spectrum sensing needs to be employed for better spectrum opportunity detection and interference avoidance both in the case of commercial and military applications. Accurate sensing needs to be employed for blocker detection in commercial systems such as LTE for the design of transmit/receive path. In military radios, the challenge lies in the robust detection of the location of the center frequencies and bandwidths of individual radio channels in the wideband input signal. In this paper, an energy detector based on tree-structured discrete Fourier transform based filter bank (TDFTFB) is proposed for detecting the edges of the channels in the spectrum. The proposed method is compared with the conventional wavelets based method for complexity and performance. The design example and simulations show that the gate count resource utilization of the proposed detection scheme is 22.9% lesser than the wavelets method at the cost of a slight degradation (0.5%) in detection accuracy. Over-the-air tests performed using Universal Software Radio Peripheral 2 (USRP2) and MATLAB/SIMULINK showed that the present method is not input specific whereas the conventional wavelet based approach depends on the spectral location of the input.     

认知无线电网络中基于抢占式排队论的频谱切换模型

针对认知无线电网络中认知用户广义传输时间的优化问题, 提出了一种基于抢占式续传优先权M/G/m排队理论的频谱切换模型. 在该排队模型中, 为了最小化认知用户广义传输时间, 采用混合排队-并列式服务的排队方式. 在此基础上, 深入分析多个认知用户、多个授权信道、多次频谱切换条件下认知用户信道使用情况, 从而推导出广义传输时间表达式. 最后探讨了该模型下自适应频谱切换策略. 仿真结果表明, 相比于已有的频谱切换模型, 该模型不仅能够更加完整地描述认知用户频谱切换行为, 而且使得认知用户传输时延更小, 广义传输时间更短. 此外, 认知无线电网络允许的认知用户服务强度增加, 能够容纳的认知用户数量增多. 因此, 该模型提升了认知用户频谱切换的性能, 更好地实现了认知用户与授权用户的频谱共享.     

Channel resource allocation and availability prediction in hybrid access femtocells

In this paper, three channel assignment models are proposed for channel resource allocation in femtocells. The models proposed are based on the Markov chain process. According to the access mechanisms in femtocells, the Third-Generation Partnership Project (3GPP) has described two kinds of users in the femtocell application; open users and the closed subscriber group (CSG). In hybrid access mechanism for femtocells, both CSG users and open users are usually referred as subscribers and non-subscribers respectively. So, in this work, for all the proposed models, the CSG and open users are categorized into two groups; the subscriber group (SG) and the non-subscriber group (NSG) respectively. The proposed models provide priority based channel resource allocation strategies between the SG and NSG. Furthermore, the focus of this research is to provide variable channel resource sharing among the SG and NSG to keep the wastage of channel resources minimum for better quality of service (QoS). The analysis is conducted in terms of channel resource blocking management for all the models and to validate the analysis, simulations are performed at the end of this paper. Further, channel resource blocking prediction, based on the blocking probability results for SG and NSG users, is also provided at the end. The prediction is done through risk analysis using the @ Risk tool. The simulations are provided in two parts; 1) the probability curves for SG and NSG against the total number of channels and 2) the risk analysis results for blocked channels prediction using the @ Risk tool.     

Leakage-based beamforming via game theory for reverse spectrum auction in cellular offloading systems

The explosion of mobile traffic and highly dynamic property often make it increasingly stressful for a cellular service provider to provide sufficient cellular spectrum resources to support the dynamic change of traffic demand in a day. In this paper, considering the dynamic characteristic of the cellular network traffic demand, we not only proposed an optimal, truthful reverse auction incentive framework, but also proposed a valuation function which is based on third-party access points’ capacity. We consider spectrum sharing in a third-party network where several secondary users (SUs) share spectrum with a primary user (PU). A leakage-based beamforming algorithm is proposed via game theory to maximize the sum utility of third-party access points subject to the signal-to-leakage-and-noise (SLNR) constraint of SUs and PU interference constraint. The sum throughput maximization problem is formulated as a non-cooperative game, where the SUs compete with each other over the resources. Nash equilibrium is considered as the solution of this game. Simulation results show that the proposed algorithm can achieve a high sum throughput and converge to a locally optimal beamforming vector.     

Reinforcement learning for performance improvement in cooperative spectrum sensing

Cognitive radio (CR) can provide a promising solution to the spectrum scarcity issue for upcoming wireless communication technologies. Cooperative spectrum sensing (CSS) is generally adopted to improve spectrum utilization and minimize interference to primary users (PUs). The performance of CSS is significantly affected by imperfect reporting channels, and it is an easy target for Byzantine attackers. This paper studies CSS under imperfect reporting channels and Byzantine attacks. We have considered centralized CSS with a hard combining fusion rule. The binary symmetric channel (BSC) is used to model the imperfect reporting channels, and a centralized independent probabilistic small scale attack model is chosen to model Byzantine attackers. We first analyze the traditional CSS (T-CSS) under the imperfect reporting channel and the Byzantine attack. The performance of T-CSS is found to be greatly affected in the considered scenarios. We propose a reinforcement learning-based algorithm to detect cooperating secondary users (CSUs) experiencing weak reporting channels and Byzantine attackers. Generally, in literature, the detected malicious users (MUs) are removed to improve the performance. However, in case there are CSUs with weak reporting channels, the genuine CSUs may be detected as Byzantine attackers and hence removed. To overcome this issue, we propose a weighted sum-based CSS (WS-CSS) algorithm that can improve the CSS performance under weak reporting channels and attacks from Byzantine attackers. It is demonstrated using plots that the proposed WS-CSS algorithm significantly improves the CSS performance.     

Route selection for interference minimization to primary users in cognitive radio ad hoc networks: A cross layer approach

An opportunistic routing problem in a cognitive radio ad hoc network is investigated with an aim to minimize the interference to primary users (PUs) and under the constraint of a minimum end-to-end data rate for secondary users (SUs). Both amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques are considered for message forwarding by SU nodes in the network. Unlike popular transmit power control based solutions for interference management in cognitive radio networks, we adopt a cross layer approach. The optimization problem is formulated as a joint power control, channel assignment and route selection problem. Next, closed form expression for transmission power is derived and corresponding channel selection scheme and routing metric are designed based on this solution. The proposed route selection schemes are shown to depend not only on gains of the interference channels between SUs and PUs but also on the values of the spectrum sensing parameters at the SU nodes in the network. Two distributed routing schemes are proposed based on our analysis; (i) optimal_DF and (ii) suboptimal_AF. The routing schemes could be implemented using existing table driven as well as on demand routing protocols. Extensive simulation results are provided to evaluate performance of our proposed schemes in random multihop networks. Results show significant reduction in PUs’ average interference experience and impressive performance as opportunistic routing schemes can be achieved by our schemes compared to traditional shortest path based routing schemes. Performance improvement is also reported over prominent recent schemes.     

Rate analysis of ZF receiver for uplink cell-free massive MIMO systems with D2D communications

This paper proposes a transmission structure of zero forcing (ZF) receiver for uplink cell-free massive multiple-input multiple-output (MIMO) systems with device-to-device (D2D) communications, followed by a rate analysis. We assumed that D2D users (DUEs) can utilize orthogonal radio resources to improve the efficiency of the scarce utilization or repurpose the time–frequency-spectrum resources currently used by the cell-free users (CFUEs). Assuming that the imperfect channel state information (CSI) is realizable, after that, the use-and-forget bounding technique is then used to respectively obtain the closed-form expressions of the CFUEs and DUEs, which provide the lower bounds on the ergodic approximate realizable rate of both communication links. First, we calculate the minimum-mean-square error (MMSE) estimation for all channels. Then, the derived results of the achievable uplink sum rate provide us with a tool that enables us to explain how some important parameters, such as the number of access points (APs)/CFUEs, each AP/CFUE/antenna, and the density of DUEs, affect system performance, highlighting the significance of cooperation between cell-free massive MIMO and D2D communication.     

Quadratic covariance matrix-based cooperative spectrum sensing method by using an evolutionary algorithm

Cognitive radio (CR) is a practical technology to solve the current low utilization of spectrum resources, and spectrum sensing is the most critical technique in a CR network. In this paper, a genetic simulated annealing algorithm based on quadratic covariance matrix and information geometry is proposed for cooperative spectrum sensing (CSS) to enhance the performance in the low signal-noise ratio (SNR). Firstly, the quadratic covariance matrix of cooperative secondary users (SUs) is used as the characteristic matrix to perform feature extraction. Secondly, based on the information geometry, the characteristic matrix is mapped on the statistical manifold to avoid information loss. Furthermore, the genetic simulated annealing algorithm is used to obtain a classifier on the statistical manifold, and the mutation process is improved by a new mutation operator to accelerate the convergence speed of the whole algorithm. Finally, the classifier is employed to implement spectrum sensing. In the simulation analysis, the proposed method has better spectrum sensing performance than the popular various methods under low SNR and faster convergence speed.     

Resource allocation for LTE-based cognitive radio network with queue stability and interference constraints

We consider the problem of interference management and resource allocation in a cognitive radio network (CRNs) where the licensed spectrum holders (primary users) share their spare capacity with the non-licensed spectrum holders (secondary users). Under such shared spectrum usage the transmissions of the secondary users should have a minimal impact on the quality of service (QoS) and the operating conditions of the primary users. Therefore, it is important to distinguish the two types of users, and formulate the problem of resource allocation considering hard restrictions on the user-perceived QoS (such as packet end-to-end delay and loss) and physical-layer channel characteristics (such as noise and interference) of the primary users. To achieve this goal, we propose to assign the bandwidth and transmission power to minimize the total buffer occupancy in the system subject to capacity constraints, queue stability constraints, and interference requirements of the primary users. We apply this approach for resource allocation in a CRN built upon a Third Generation Partnership Project (3GPP) long-term evolution (LTE) standard platform. Performance of the algorithm is evaluated using simulations in OPNET environment. The algorithm shows consistent performance improvement when compared with other relevant resource allocation techniques.     

Multi-channel sensing and resource allocation in energy constrained cognitive radio networks

We consider a cognitive radio network in a multi-channel licensed environment. Secondary user transmits in a channel if the channel is sensed to be vacant. This results in a tradeoff between sensing time and transmission time. When secondary users are energy constrained, energy available for transmission is less if more energy is used in sensing. This gives rise to an energy tradeoff. For multiple primary channels, secondary users must decide appropriate sensing time and transmission power in each channel to maximize average aggregate-bit throughput in each frame duration while ensuring quality-of-service of primary users. Considering time and energy as limited resources, we formulate this problem as a resource allocation problem. Initially a single secondary user scenario is considered and solution is obtained using decomposition and alternating optimization techniques. Later we extend the analysis for the case of multiple secondary users. Simulation results are presented to study effect of channel occupancy, fading and energy availability on performance of proposed method.     

On spectrum sensing in cognitive radio CDMA networks with beamforming

In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%).