In this paper, we propose an adaptive medium access control (MAC) protocol for full-duplex (FD) cognitive radio networks in which FD secondary users (SUs) perform channel contention followed by concurrent spectrum sensing and transmission, and transmission only with maximum power in two different stages (called the FD sensing and transmission stages, respectively) in each contention and access cycle. The proposed FD cognitive MAC (FDC-MAC) protocol does not require synchronization among SUs, and it efficiently utilizes the spectrum and mitigates the self-interference in the FD transceiver. We develop a mathematical model to analyze the throughput performance of the FDC-MAC protocol, where both half-duplex (HD) transmission and FD transmission modes are considered in the transmission stage. Then, we study the FDC-MAC configuration optimization through adaptively controlling the spectrum sensing duration and transmit power level in the FD sensing stage. We prove that there exists optimal sensing time and transmit power to achieve the maximum throughput, and we develop an algorithm to configure the proposed FDC-MAC protocol. Extensive numerical results are presented to illustrate the optimal FDC-MAC configuration and the impacts of protocol parameters and the self-interference cancellation quality on the throughput performance. Moreover, we demonstrate the significant throughput gains of the FDC-MAC protocol with respect to the existing HD MAC and single-stage FD MAC protocols.
The FD technology has been employed for more efficient spectrum access design in cognitive radio networks @cite_12 -- @cite_9 where SUs can perform sensing and transmission simultaneously. In @cite_12 , a FD MAC protocol is developed which allows simultaneous spectrum access of the SU and PU networks where both PUs and SUs are assumed to employ the @math -persistent MAC protocol for channel contention resolution and access. This design is, therefore, not applicable to the hierarchical spectrum access in the CRNs where PUs should have higher spectrum access priority compared to SUs.
@cite_9 @cite_12
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Because of the asynchronization between primary and secondary wireless networks, the synchronization between primary users (PUs) and secondary users (SUs) can be hardly guaranteed in nontime-slotted cognitive radio networks (CRNs). In this paper, we propose a novel framework for multichannel nontime-slotted CRNs, where the PUs randomly access and leave the licensed channels. Since the PUs cannot distinguish between primary and secondary signals, the PUs may sense a busy channel when the PUs start to reactivate during the SUs' transmission, thus generating a collision or entering the backoff stage. To guarantee the high-throughput transmission of the PUs and increase the channel utilization of the SUs, in this paper, we propose the wireless full-duplex spectrum sensing (FD-SS) scheme for SUs in multichannel nontime-slotted CRNs. Using our developed FD-SS scheme, the SUs can timely sense the PUs' reactivation during the same time when the SUs are transmitting their signals. Then, based on our proposed wireless FD-SS scheme, we further develop and analyze the wireless full-duplex cognitive medium access control (FDC-MAC) protocol for multichannel nontime-slotted CRNs. We conduct extensive numerical analyses, showing that our developed FD-SS scheme and FDC-MAC protocol can efficiently guarantee the high-throughput transmission of the PUs and increase the channel utilization of the SUs without requiring the synchronization between the PUs and the SUs over the multichannel nontime-slotted CRNs. In this paper, we consider the Medium Access Control (MAC) protocol design for full-duplex cognitive radio networks (FDCRNs). Our design exploits the fact that full-duplex (FD) secondary users (SUs) can perform spectrum sensing and access simultaneously, which enable them to detect the primary users' (PUs) activity during transmission. The developed FD MAC protocol employs the standard backoff mechanism as in the 802.11 MAC protocol. However, we propose to adopt the frame fragmentation during the data transmission phase for timely detection of active PUs where each data packet is divided into multiple fragments and the active SU makes sensing detection at the end of each data fragment. Then, we develop a mathematical model to analyze the throughput performance of the proposed FD MAC protocol. Furthermore, we propose an algorithm to configure the MAC protocol so that efficient self-interference management and sensing overhead control can be achieved. Finally, numerical results are presented to evaluate the performance of our design and demonstrate the throughput enhancement compared to the existing half-duplex (HD) cognitive MAC protocol. In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrum-sensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration. In this paper, we investigate a joint scheduling and power control for an infrastructure-based cognitive radio network (CRN) in coexistence with a cellular primary radio network (PRN). The PRN uses a set of licensed nonoverlapping orthogonal frequency channels for transmission. This set of channels is also accessed in an opportunistic manner by a set of cognitive radio base stations (CR-BSs) to support secondary users (SUs). The problem is formulated to maximize the spectrum utilization of SUs without causing excessive interference to active primary users (PUs) of the PRN. In addition, all the serviced SUs must meet a certain Quality of Service (QoS), such as satisfying a predefined signal to interference noise ratio (SINR). A centralized solution for joint scheduling and power control is derived to make the global accessing decision for all unserved SUs. With the assumption that the knowledge of all subscribers is available, a coordinator of the CRN can use the joint scheduling and power control algorithm to maximize the spectrum utilization of serviced SUs by solving a mixed-integer linear programming (MILP) with an NP-hard complexity. To avoid the NP-hard complexity, we propose a suboptimal heuristic greedy algorithm that can be obtained at a much lower complexity based on the coloring interference graph among unserved SUs effected by serviced SUs and active PUs. Its superior performance over the existing algorithms is demonstrated through simulations. This paper introduces a novel technique for access by a cognitive Secondary User (SU) to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique allows the SU to perform selective retransmissions of previously corrupted SU data packets. The temporal redundancy introduced by the primary ARQ protocol and by the selective SU retransmission process can be exploited by the SU receiver to perform Interference Cancellation (IC) over the entire interference pattern, thus creating a "clean" channel for the decoding of the concurrent message. The chain decoding technique, initiated by a successful decoding operation of a SU or PU message, consists in the iterative application of IC, as previously corrupted messages become decodable. Based on this scheme, we design an optimal policy that maximizes the SU throughput under a constraint on the average long-term PU throughput degradation. We show that the optimal policy can be found by first optimizing the SU access policy using a Markov Decision Process formulation, and then applying a chain decoding protocol defined by five basic rules. Such an approach enables a compact state representation of the protocol, and its efficient numerical optimization. Finally, we show by numerical results the throughput benefit of the proposed technique. For achieving optimized spectrum usage, most existing opportunistic spectrum sensing and access protocols model the spectrum sensing and access problem as a partially observed Markov decision process by assuming that the information states and or the primary users' (PUs) traffic statistics are known a priori to the secondary users (SUs). While theoretically sound, the existing solutions may not be effective in practice due to two main concerns. First, the assumptions are not practical, as before the communication starts, PUs' traffic statistics may not be readily available to the SUs. Second and more serious, existing approaches are extremely vulnerable to malicious jamming attacks. By leveraging the same statistic information and stochastic dynamic decision-making process that the SUs would follow, a cognitive attacker with sensing capability can sense and jam the channels to be accessed by SUs, while not interfering PUs. To address these concerns, we formulate the antijamming, multichannel access problem as a nonstochastic multi-armed bandit problem. By leveraging probabilistically shared information between the sender and the receiver, our proposed protocol enables them to hop to the same set of channels with high probability while gaining resilience to jamming attacks without affecting PUs' activities. We analytically show the convergence of the learning algorithms and derive the performance bound based on regret . We further discuss the problem of tracking the best adaptive strategy and characterize the performance bound based on a new regret . Extensive simulation results show that the probabilistic spectrum sensing and access protocol can overcome the limitation of existing solutions and is highly resilient to various jamming attacks even with jammed acknowledgment (ACK) information. Cognitive Radio Network (CRN) is regarded as a promising way to address the increasing demand for wireless channel resources. It solves the channel resource shortage problem by allowing a Secondary User (SU) to access the channel of a Primary User (PU) when the channel is not occupied by the PU. The latest FCC's rule in May 2012 enforces database-driven CRNs, in which an SU queries a database to obtain spectrum availability information by submitting a location based query. However, one concern about database-driven CRNs is that the queries sent by SUs will inevitably leak the location information. In this study, we identify a new kind of attack against location privacy of database-drive CRNs. Instead of directly learning the SUs' locations from their queries, our discovered attacks can infer an SU's location through his used channels. We propose Spectrum Utilization based Location Inferring Algorithm that enables the attacker to geo-locate an SU. To thwart location privacy leaking from query process, we propose a novel Private Spectrum Availability Information Retrieval scheme that utilizes a blind factor to hide the location of the SU. To defend against the discovered attack, we propose a novel prediction based Private Channel Utilization protocol that reduces the possibilities of location privacy leaking by choosing the most stable channels. We implement our discovered attack and proposed scheme on the data extracted from Google Earth Coverage Maps released by FCC. Experiment results show that the proposed protocols can significantly improve the location privacy. We venture beyond the "listen-before-talk" strategy that is common in many traditional cognitive radio access schemes. We exploit the bi-directional nature of most primary communication systems. By intelligently choosing their transmission parameters based on the observation of primary user (PU) communications, secondary users (SUs) in a cognitive network can achieve higher spectrum usage while limiting their interference to the PU. Specifically, we propose that the SUs listen to the PU's feedback channel to assess their interference on the primary receiver (PU-Rx), and adjust radio power accordingly to satisfy the PU's interference constraint. We investigate both centralized and distributed power control algorithms without active PU cooperation. We show that the PU feedback information inherent in many two-way primary systems can be used as important coordination signal among multiple SUs to distributively achieve a joint performance guarantee on the primary receiver's quality of service. Given the highly variable physical layer characteristics in cognitive radio sensor networks (CRSN), it is indispensable to provide the performance analysis for cognitive radio users for smooth operations of the higher layer protocols. Taking into account the dynamic spectrum access, this paper formulates the two fundamental performance metrics in CRSN; bandwidth and delay. The performance is analyzed for a CSMA-based medium access control protocol that uses a common control channel for secondary users (SUs) to negotiate the wideband data traffic channel. The two performance metrics are derived based on the fact that SUs can exploit the cognitive radio to simultaneously access distinct traffic channels in the common interference region. This feature has not been exploited in previous studies in estimating the achievable throughput and delay in cognitive radio networks. Performance analysis reveals that dedicating a common control channel for SUs enhances their aggregated bandwidth approximately five times through the possibility of concurrent transmissions on different traffic channels and reduces the packet delay significantly. In this paper, a new spectrum-sharing model called sensing-based spectrum sharing is proposed for cognitive radio networks. This model consists of two phases: In the first phase, the secondary user (SU) listens to the spectrum allocated to the primary user (PU) to detect the state of the PU; in the second phase, the SU adapts its transit power based on the sensing results. If the PU is inactive, the SU allocates the transmit power based on its own benefit. However, if the PU is active, the interference power constraint is imposed to protect the PU. Under this new model, the evaluation of the ergodic capacity of the SU is formulated as an optimization problem over the transmit power and the sensing time. Due to the complexity of this problem, two simplified versions, which are referred to as the perfect sensing case and the imperfect sensing case, are studied in this paper. For the perfect sensing case, the Lagrange dual decomposition is applied to derive the optimal power allocation policy to achieve the ergodic capacity. For the imperfect sensing case, an iterative algorithm is developed to obtain the optimal sensing time and the corresponding power allocation strategy. Finally, numerical results are presented to validate the proposed studies. It is shown that the SU can achieve a significant capacity gain under the proposed model, compared with that under the opportunistic spectrum access or the conventional spectrum sharing model. Database-driven cognitive radio has been well recognized as an efficient way to reduce interference between Primary Users (PUs) and Secondary Users (SUs). In database-driven cognitive radio, PUs and SUs must provide their locations to enable dynamic channel allocation, which raises location privacy breach concern. Previous studies only focus on unilateral privacy preservation, i.e., Only PUs' or SUs' privacy is preserved. In this paper, we propose to protect bilateral location privacy of a PU and an SU. The main challenge lies in how to coordinate the PU and SU to maximize their utility provided that their location privacy is protected. We first introduce a quantitative method to calculate both PU's and SU's location privacy, and then design a novel privacy preserving Utility Maximization protocol (UMax). UMax allows for both PU and SU to adjust their privacy preserving levels and optimize transmit power iteratively to achieve the maximum utility. Through extensive evaluations, we demonstrate that our proposed mechanism can efficiently increase the utility of both PU and SU while preserving their location privacy. In this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd. (By jointly considering the channel gain and the interference level of the OFDM-based cognitive radio systems, the highest achievable rate of each subchannel can be calculated, which indicates the ability of carrying bits of the subchannel. Using the normalized cost index list, an efficient algorithm is proposed to maximize the sum capacity of the cognitive OFDM systems while keeping the proportional rate constraints satisfied.) Market-based mechanisms offer promising approaches for spectrum access in cognitive radio networks. In this paper, we focus on two market models, one with a monopoly primary user (PU) market and the other with a multiple PU market, where each PU sells its temporarily unused spectrum to secondary users (SUs). We propose a pricing-based spectrum trading mechanism that enables SUs to contend for channel usage by random access, in a distributed manner, which naturally mitigates the complexity and time overhead associated with centralized scheduling. For the monopoly PU market model, we first consider SUs contending via slotted Aloha. The revenue maximization problems here are nonconvex. We first characterize the Pareto optimal region, and then obtain a Pareto optimal solution that maximizes the SUs' throughput subject to the SUs' budget constraints. To mitigate the spectrum underutilization due to the “price of contention,” we revisit the problem where SUs contend via CSMA, and show that spectrum utilization is enhanced, resulting in higher revenue. When the PU's unused spectrum is a control parameter, we study further the tradeoff between the PU's utility and its revenue. For the multiple PU market model, we cast the competition among PUs as a three-stage Stackelberg game, where each SU selects a PU's channel to maximize its throughput. We characterize the Nash equilibria, in terms of access prices and the spectrum offered to SUs. Our findings reveal that the number of equilibria exhibits a phase transition phenomenon, in the sense that when the number of PUs is greater than a threshold, there exist infinitely many equilibria; otherwise, there exists a unique Nash equilibrium, where the access prices and spectrum opportunities are determined by the budgets elasticity of SUs and the utility level of PUs. In this letter, a resource allocation strategy based on a deep neural network (DNN) is proposed for multi-channel cognitive radio networks, where the secondary user (SU) opportunistically utilizes channels without causing excessive interference to the primary user (PU). In the proposed scheme, the allocation of transmit power in each channel for SUs is found by utilizing the newly proposed DNN model, which separately determines the overall transmit power of individual SUs and the proportion of transmit power allocated to each channel. Both the spectral efficiency (SE) of the SU and the amount of interference caused to the PU are considered in the training of the DNN model, such that the interference caused to the PUs can be properly regulated while the SE of the SU is improved. Through simulations, we show that our scheme enables a high SE of the SU to be achieved while the interference caused to the PU can be maintained at less than the threshold. Cognitive Radio Networks (CRNs) enable opportunistic access to the licensed channel resources by allowing unlicensed users to exploit vacant channel opportunities. One effective technique through which unlicensed users, often referred to as Secondary Users (SUs), acquire whether a channel is vacant is cooperative spectrum sensing. Despite its effectiveness in enabling CRN access, cooperative sensing suffers from location privacy threats, merely because the sensing reports that need to be exchanged among the SUs to perform the sensing task are highly correlated to the SUs' locations. In this paper, we develop a new Location Privacy for Optimal Sensing (LPOS) scheme that preserves the location privacy of SUs while achieving optimal sensing performance through voting-based sensing. In addition, LPOS is the only alternative among existing CRN location privacy preserving schemes (to the best of our knowledge) that ensures high privacy, achieves fault tolerance, and is robust against the highly dynamic and wireless nature of CRNs. Cognitive radios (CRs) have a great potential to improve spectrum utilization by enabling users to access the spectrum dynamically without disturbing licensed primary radios (PRs). A key challenge in operating these radios as a network is how to implement an efficient medium access control (MAC) mechanism that can adaptively and efficiently allocate transmission powers and spectrum among CRs according to the surrounding environment. Most existing works address this issue via suboptimal heuristic approaches or centralized solutions. In this paper, we propose a novel joint power channel allocation scheme that improves the performance through a distributed pricing approach. In this scheme, the spectrum allocation problem is modeled as a noncooperative game, with each CR pair acting as a player. A price-based iterative water-filling (PIWF) algorithm is proposed, which enables CR users to reach a good Nash equilibrium (NE). This PIWF algorithm can be implemented distributively with CRs repeatedly negotiating their best transmission powers and spectrum. Simulation results show that the social optimality of the NE solution is dramatically improved through pricing. Depending on the different orders according to which CRs take actions, we study sequential and parallel versions of the PIWF algorithm. We show that the parallel version converges faster than the sequential version. We then propose a corresponding MAC protocol to implement our resource management schemes. The proposed MAC allows multiple CR pairs to be first involved in an admission phase, then iteratively negotiate their transmission powers and spectrum via control-packet exchanges. Following the negotiation phase, CRs proceed concurrently with their data transmissions. Simulations are used to study the performance of our protocol and demonstrate its effectiveness in terms of improving the overall network throughput and reducing the average power consumption. In cognitive radio, spectrum sensing is used to find the white spectrum or protect the primary user from interference caused by the secondary user (SU). There are two conventional spectrum sensing approaches: quiet and active. However, these conventional approaches have several problems. In quiet sensing, the quiet period degrades the SU capacity. With active sensing, the SU capacity is also degraded by the need for additional resource consumption and the mismatch in feedback information. In order to mitigate these problems, the structure of simultaneous PU sensing and data transmission is introduced. This structure is equipped with antenna isolation and self-interference cancellation in which the communication and the sensing radios are already assumed to be significantly isolated. This approach is designed so that the SU transmitter can sense PU signals and transmit data signals at the same time by dividing its spatial resources. Expanding on this work, we propose a concept of "TranSensing" which adaptively uses spatial resource according to the surrounding environments. To effectively use TranSensing, we propose a two-stage algorithm (TSA). Finally, the impact of residual interference on TranSensing is investigated. Simulation results show that TranSensing with TSA enhances the SU capacity over the conventional quiet or active sensing. In this paper, a cross-layer framework for joint distributed spectrum sensing, estimation and scheduling in agile wireless networks is presented. A network of secondary users (SUs) opportunistically accesses portions of the spectrum left unused by a network of licensed primary users (PUs). A central controller (CC) schedules the spectrum bands detected as idle for access by the SUs, based on compressed measurements acquired by the SUs and local feedback information from the PUs. The sparsity in the spectrum occupancy dynamics is exploited: leveraging the spectrum occupancy estimate in the previous slot, the CC needs to estimate only a residual uncertainty vector via sparse recovery techniques, thus few measurements suffice. The sensing probability of the SUs and the spectrum scheduling are adapted over time by the CC, based on the current spectrum occupancy estimate, and jointly optimized so as to maximize the SU throughput, under constraints on the PU throughput degradation and the sensing-transmission cost incurred by the SUs. The high dimensionality of the POMDP formulation is reduced by resorting to a compact state space representation via minimization of the Kullback-Leibler divergence. Simulation results demonstrate improvements up to 70 in the SU throughput over a scheme where sensing is done only locally at the CC, at a fraction of the sensing cost with respect to a scheme where sensing is done in each slot by the SUs. An uplink multisecondary user (SU) cognitive radio system having average delay constraints as well as an interference constraint to the primary user (PU) is considered. If the interference channels between the SUs and the PU are statistically heterogeneous due to the different physical locations of the different SUs, the SUs will experience different delay performances. This is because SUs located closer to the PU transmit with lower power levels. Two dynamic scheduling-and-power-allocation policies that can provide the required average delay guarantees to all SUs irrespective of their locations are proposed. The first policy solves the problem when the interference constraint is an instantaneous one, while the second is for problems with long-term average interference constraints. We show that although the average interference problem is an extension to the instantaneous interference one, the solution is totally different. The two policies, derived using the Lyapunov optimization technique, are shown to be asymptotically delay optimal while satisfying the delay and interference constraints. Our findings are supported by extensive system simulations and shown to outperform the existing policies as well as shown to be robust to channel estimation errors. In a cognitive radio scenario, we consider a single secondary user (SU) accessing a multichannel system. The SU senses the channels sequentially to detect if a primary user (PU) is occupying the channels and stops its search to access a channel if it offers a significantly high throughput. The optimal stopping rule and power control problem is considered. The problem is formulated as an SU's throughput-maximization problem under power, interference, and packet delay constraints. We first show the effect of the optimal stopping rule on packet delay and then solve this optimization problem for both the overlay system, where the SU transmits only at the spectrum holes, and the underlay system, where tolerable interference (or tolerable collision probability) is allowed. We provide closed-form expressions for the optimal stopping rule and show that the optimal power control strategy for this multichannel problem is a modified waterfilling approach. We extend the work to a multi-SU scenario and show that when the number of SUs is large, the complexity of the solution becomes smaller than that of the single-SU case. We discuss the application of this problem in typical networks where packets simultaneously arrive and have the same departure deadline. We further propose an online adaptation policy to the optimal stopping rule that meets the packets' hard-deadline constraint and, at the same time, gives higher throughput than the offline policy.