Kehao Wang

On Optimality of Myopic Policy for Opportunistic Access With Nonidentical Channels and Imperfect Sensing

We consider the access problem in a multichannel opportunistic communication system with imperfect sensing, where the state of each channel evolves as a nonidentical and independently distributed Markov process. This problem can be cast into a restless multiarmed bandit (RMAB) problem, which is intractable for its exponential computation complexity. A promising approach that has attracted much research attention is the consideration of an easily myopic policy that maximizes the immediate reward by ignoring the impact of the current policy on future reward. Specially, we formalize a family of generic functions, which is referred to as g-regular functions, characterized by three axioms, and then establish a set of closed-form conditions for the optimality of the myopic policy and illustrate the engineering implications behind the obtained results.

Multichannel Opportunistic Access by Overhearing Primary ARQ Messages

We consider the scenario of a cognitive radio network overlaying on top of a legacy primary network by overhearing feedback signals over primary channels. The considered problem can be cast into a restless multiarmed bandit (RMAB) problem that is of fundamental importance in decision theory. It is well known that the optimal policy of the RMAB problem is PSPACE-hard to obtain due to its exponential computation complexity. A natural alternative is to consider an easily implementable myopic policy that maximizes immediate reward but ignores the impact of the current strategy on future reward. In this paper, we perform an analytical study on the structure, optimality, and performance of the myopic policy for the considered RMAB problem. The myopic policy is shown to have a simple queue structure, and then, its optimality is established for accessing N - 1 of N channels and conjectured for the general case. The performance of the myopic policy is analyzed, which, based on the structure of the myopic policy and the domination theory, characterizes the lower and upper bounds of the throughput of a multichannel opportunistic communication system.

Cooperative Energy-Efficient Content Dissemination Using Coalition Formation Game Over Device-to-Device Communications

This paper investigates the problem of cooperative energy-efficient content dissemination among a number of cellular user equipments (UEs), with the assumption that these UEs are seeking to receive the same content from a common wireless access point, such as an eNodeB. We formulate the problem as a nontransferable utility coalition formation game, in which a utility function is characterized by taking into consideration energy efficiency and mutual interference in the scenario of proximity device-to-device (D2D) communications. Then, we develop a distributed coalition formation algorithm. With the proposed algorithm, UEs that are in proximity of each other can cooperate and then self-organize into independent disjoint coalitions, and finally minimize overall network energy consumption using D2D communications. The simulation results show that using the proposed algorithm, significant energy savings can be achieved compared with the cellular multicasting case and the noncooperative case, which validates the high energy efficiency of the proposed game-theory-based algorithm in wireless content dissemination scenarios.

On Missing Tag Detection in Multiple-Group Multiple-Region RFID Systems

We formulate and study a missing tag detection problem arising in multiple-group, multiple-region radio frequency identification (RFID) systems, where a mobile reader needs to detect whether there is any missing event for each group of tags. The problem we tackle is to devise missing tag detection protocols with minimum execution time while guaranteeing the detection reliability requirement for each group. By leveraging the technique of Bloom filter, we develop a suite of three missing tag detection protocols, each decreasing the execution time compared to its predecessor by incorporating an improved version of the Bloom filter design and parameter tuning. By sequentially analyzing the developed protocols, we gradually iron out an optimum detection protocol that works in practice.

Finding Needles in a Haystack: Missing Tag Detection in Large RFID Systems

Radio frequency identification technology has been widely used in missing tag detection to reduce and avoid inventory shrinkage. In this application, promptly finding out the missing event is of paramount importance. However, the existing missing tag detection protocols cannot efficiently handle the presence of a large number of unexpected tags whose IDs are not known to the reader, which shackles the time efficiency. To deal with the problem of detecting missing tags in the presence of unexpected tags, this paper introduces a two-phase Bloom filter-based missing tag detection (BMTD) protocol. The proposed BMTD exploits Bloom filter in sequence to first deactivate the unexpected tags and then test the membership of the expected tags, thus dampening the interference from the unexpected tags and considerably reducing the detection time. Moreover, the theoretical analysis of the protocol parameters is performed to minimize the detection time of the proposed BMTD and achieve the required reliability simultaneously. In addition, we derive a critical threshold on the unexpected tag size for the execution of first phase in BMTD. Extensive experiments are then conducted to evaluate the performance of the proposed BMTD. The results demonstrate that the proposed BMTD significantly outperforms the state-of-the-art solutions.

Multi-layer based multi-path routing algorithm for maximizing spectrum availability

Last 2 decades have witnessed the spectrum resources scarcity which is caused by wireless networks’ ubiquitous applications. To utilize the rare spectrum resources more efficiently, Cognitive Radio (CR) technology has been developed as a promising scheme. However, in CR networks, a novel NP-Hard disjoint multi-path routing problem has been encountered due to the Primary Users’ (PUs’) random movements. To settle this problem, we present a Spectrum History Matrix mechanism to define long-term spectrum sensing information on time-spectrum level such that spectrum availability and communication efficiency can be quantized in CR networks. To lessen the possibility for an active PU to interrupt all paths simultaneously, a sub-optimal Multi-layer based Multi-path Routing Algorithm (MMRA) is provided to determine how to route multiple paths which are not under the same PUs’ interference ranges. Through theoretical and simulation analyses, MMRA can not only settle the disjoint multi-path routing problem in polynomial time complexity, but also maximize communication efficiency.

On optimality of myopic policy in multi-channel opportunistic access

We consider the channel access problem arising in opportunistic scheduling over fading channels, cognitive radio networks, and server scheduling. The multi-channel communication system consists of N channels. Each channel evolves as a time-nonhomogeneous multi-state Markov process. At each time instant, a user chooses M channels to transmit information. Some reward depending on the states of the chosen channels is obtained for each transmission. The objective is to design an access policy that maximizes the expected accumulated discounted reward over a finite or infinite horizon. The considered problem can be cast into a restless multi-armed bandit (RMAB) problem with PSPACE-hardness. A natural alternative is to consider the easily implementable myopic policy. In this paper, we perform an theoretical analysis on the considered RMAB problem, and establish a set of closed-form conditions to guarantee the optimality of the myopic policy.

Optimality of Myopic Policy for Multistate Channel Access

We consider the multichannel access problem in which each of N channels is modeled as a multistate Markov chain. At each time instant, a transmitter accesses M channels and obtains some reward depending on the states of those chosen channels. The considered problem can be cast into a restless multiarmed bandit (RMAB) problem. It is well-known that solving the RMAB problem is PSPACE-hard. A natural alternative is to consider the myopic policy that maximizes the immediate reward but ignores the impact of the current strategy on the future reward. In this letter, we perform an analytical study on structure, optimality, and performance of the myopic policy for the considered RMAB problem. We show that the myopic policy has a simple robust structure that reduces channel selection to a round-robin procedure. The optimality of this simple policy is established for accessing M = N - 1 of N channels and conjectured for the general case of arbitrary M based on the structure of myopic policy.

Myopic policy for opportunistic access in cognitive radio networks by exploiting primary user feedbacks

The authors consider a cognitive radio network overlaying on top of a legacy primary network in which a secondary user is allowed to access primary channel by overhearing feedback signals over the primary channels. Each channel is assumed to be a two state Makovian process. Aiming at maximising the expected accumulated discounted network throughput, the considered sequential decision-making problem can be cast into a restless multi-armed bandit (RMAB) problem which is well-known to be PSPACE-hard, and thus a natural alternative approach is to seek a simple myopic policy. This study presents a theoretical study on the optimality of the proposed myopic policy for the special RMAB problem by considering four different cases: negatively correlated homogeneous channels, heterogeneous channels, positively correlated heterogeneous channels and negatively correlated heterogeneous channels. More specifically, the authors establish the closed-form conditions to guarantee the optimality of the myopic policy for the four cases, respectively, which, combined with the case of positively correlated homogeneous channels, constitute a complete paradigm for the optimality of the myopic policy.

On-demand ecology-inspired spectrum allocation mechanism for heterogeneous cognitive radio networks

As increasing number of cognitive radio network (CRN) standards are developed in TV White Spaces band with incompatible communication patterns, heterogeneous CRNs coexistence problem could not be avoided. However, most solutions on this problem have not considered CRNs’ actual data transmission demands and weighted fairness simultaneously. Therefore, we would like to introduce a novel On-demand ecological Species Competition based HEterogeneous networks coexistence MEchanism (O-SCHEME) in this paper. Inspired by ecology species competition model, O-SCHEME utilizes an ecology based spectrum allocation mechanism for guaranteeing heterogeneous CRNs’ spectrum shares weighted fairness. And by employing CRNs’ communication spectrum requirement constraints, actual data transmission needs could be satisfied without wasted communication resources. Through both theoretical and simulation analyses, we demonstrate that O-SCHEME can achieve stable and fair spectrum allocations among coexisting networks with high communication efficiency.