Channel Availability Statistics Research Articles

An urban expressway forwarding scheme for cognitive Internet of vehicles

The Internet of vehicles is an essential component for building smart cities that can improve traffic safety and provide multimedia entertainment services. The cognitive radio–enabled Internet of vehicles was proposed to resolve the conflict between the increasing demand of Internet of vehicles applications and the limited spectrum resources. The multi-hop transmission is one of the most important issues in cognitive radio–enabled Internet of vehicles networks. Nevertheless, most existing forwarding solutions designed for the cognitive radio–enabled Internet of vehicles did not consider the urban expressway scenario, where primary base stations are densely installed with small coverage areas. In this case, it is difficult to ensure that the sender and the receiver of the same cognitive radio link have similar channel availability statistics, which makes cognitive radio links more likely to be interrupted. To address this challenge, we develop a multi-hop forwarding scheme to minimize the end-to-end delay for such networks. We first formulate the delay minimization problem as a non-linear integer optimization problem. Then, we propose an approach to select the relay candidates by jointly considering the high mobility of vehicles and the unique cognitive radio spectrum usage distributions in urban expressway scenarios. Finally, we propose the low-latency forwarding strategies by considering the channel availability and the delay cost of different situations of relay candidates. Simulations show the advantages of our proposed scheme, compared with state-of-art methods.

Decentralized Learning for Opportunistic Spectrum Access: Multiuser Restless Multiarmed Bandit Formulation

In opportunistic spectrum access, each secondary user selects a channel from a pool of multiple channels based on their local observations. The challenge here is to learn the best channel in terms of availability, as the channel availability statistics are unknown. In order to learn these unknown statistics, a novel decentralized multiuser learning technique termed as DSEE for channel selection in dynamic networks (DSEE-CSDN) has been proposed. DSEE-CSDN allows secondary users to enter the network during different time slots. Thus, the number of secondary users is not known beforehand. Moreover, the availability status of different independent channels is considered to be changing according to the two-state restless Markov chain model, which, in practice, is more realistic as compared to independent and identically distributed channel state model. Thus, the problem is formulated as a stochastic multiuser restless multiarmed bandit. The proposed algorithm achieves system-wide order-optimal performance under self-play. Results indicate that DSEE-CSDN is able to achieve a logarithmic order of regret. Furthermore, collisions and switching cost are just around 5% and 2% of total time slots, respectively. Also, DSEE-CSDN can achieve probabilistic fairness in channel selection without any preagreement among users.

Partially observable Markov decision process‐based sensing scheduling for decentralised cognitive radio networks with the awareness of channel switching delay and imperfect sensing

An optimal multi-slot channel sensing schedule is proposed in this study that considers an opportunistic spectrum access with the awareness of channel switching delay and imperfect sensing. A practical case is considered where channel availability statistics are usually correlated in time slots and in frequency channels. The switching delays between channels, hardware constraints, and collision with other cognitive users are considered to find an optimal sensing order of the channels that maximises throughput of cognitive user. The optimal sensing order is obtained using the partially observable Markov decision process framework. Throughput of cognitive user, with and without channel sensing errors, is analytically derived and for each case an algorithm is developed. The proposed scheme mitigates the effect of channel sensing errors on the throughput. Performance of the proposed scheme is evaluated through simulations by comparing it with the existing schemes in the literature.

Energy-Efficient Adaptive Rate Control for Streaming Media Transmission Over Cognitive Radio

In future mobile computing systems, cognitive radio (CR) emerges as a promising solution for alleviating spectrum shortage and satisfying the high bandwidth demand of multimedia streaming, while it presents tough challenges in provisioning user experience-perceived quality of service (QoS) and conserving transmission energy. In this paper, an adaptive rate control (ARC) scheme with the aid of the receive buffer is presented for energy-efficient transmission of streaming media over CR with QoS guarantee. The QoS metric, either display smoothness or transmission delay, is quantified by the state of the receive buffer. Cross-layer information is utilized to form a closed-loop feedback optimal control. A novel analytical model called event-driven discrete-time Markov control process is introduced to formulate the QoS-guaranteed energy-efficient ARC problem. Based on potential theory, a policy iteration algorithm that combines potentials estimation and stochastic approximation is proposed for finding the optimal policy online. By exploiting the system dynamics, this algorithm does not depend on any prior knowledge of channel availability or fading statistics, and it can converge to the global optimum with a low computational cost and reasonable speed. Simulation results demonstrate the effectiveness of the proposed method.

Dynamic Spectrum Access via Channel-Aware Heterogeneous Multi-Channel Auction With Distributed Learning

We consider the design of dynamic spectrum access (DSA) mechanism. Assuming heterogeneous primary channels with distinct availability statistics unknown to each secondary user (SU), we consider the auction-based approaches for spectrum access. We first apply a unit demand (UD) auction by exploring the instantaneous link condition of each SU for its throughput maximization. To address the disadvantages faced in the UD auction, we propose a learning-based unit demand (LBUD) auction. It incorporates a distributed learning of the primary channel availabilities into the auction mechanism to explore both primary channel availability statistics and instantaneous link gains of the SUs for their throughput maximization. The new mechanism not only substantially reduces communication overhead, but also improves the SUs' throughputs when the primary channels have dissimilar availability statistics. We show that the proposed LBUD auction for channel allocation among SUs preserves the strong property of the UD auction. We further propose an adaptive price increment algorithm to improve convergence speed of the iterative procedure used in the auction. Numerical results show the effectiveness of our proposed auction mechanism in terms of the throughput gain.

Opportunistic Spectrum Access for CR-VANETs: A Game-Theoretic Approach

In this paper, we investigate the opportunistic spectrum access for cognitive radio vehicular ad hoc networks. The probability distribution of the channel availability is first derived through a finite-state continuous-time Markov chain, jointly considering the mobility of vehicles and the spatial distribution and temporal channel usage pattern of primary transmitters. Utilizing the channel availability statistics, we propose a game-theoretic spectrum access scheme for vehicles to opportunistically access licensed channels in a distributed manner. In particular, the spectrum access process is modeled as a noncooperative congestion game. The existence of the Nash equilibrium (NE) is proved, and its efficiency is analyzed when employing the uniform medium access control protocol and slotted ALOHA, respectively. Furthermore, a spectrum access algorithm is devised to achieve a pure NE with high efficiency and fairness. Simulation results validate our analysis and demonstrate that the proposed spectrum access scheme can achieve higher utility and fairness, compared with a random access scheme.

Throughput Optimal Multi-Slot Sensing Procedure for a Cognitive Radio

We consider a cognitive radio system with M primary channels where users' data transmissions follow a slotted structure. We consider the scenario where the channel availability statistics are correlated across channels as well as across time slots. In each slot, the cognitive user (CU) senses channels one by one until a suitable channel is found for data transmission. CU adapts theCognitive Radio data rate based on the channel's fading gain. We employ a Markov chain model to capture the channel availability statistics across the slots. We address the problem of finding optimal sensing order/procedure in every slot based on the history of sensed channels (including the previous slots), in order to maximize the total CU throughput over N-slots. Using theory of optimal stopping, we derive recursive expressions for the optimal multi-slot CU throughput and find the optimal multi-slot sensing order/procedure using dynamic programming. We also study few sub-optimal sensing procedures. Using numerical results, we illustrate the gains in exploiting the correlation of channel availability statistics.

Opportunistic Spectrum Access in Unknown Dynamic Environment: A Game-Theoretic Stochastic Learning Solution

We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.