Dongyue Xue

Guaranteed opportunistic scheduling in multi-hop cognitive radio networks

Cognitive radio networks enable opportunistic sharing of bandwidth/spectrum. In this paper, we introduce optimal control and scheduling algorithms for multi-hop cognitive radio networks to maximize the throughput of secondary users while stabilizing the cognitive radio network subject to collision rate constraints required by primary users. We show that by employing our proposed optimal algorithm, the achievable network throughput can be arbitrarily close to the optimal value. To reduce complexity, we propose a class of feasible suboptimal algorithms that can achieve at least a fraction of the optimal throughput. In addition, we also analyze the optimal algorithm in the fixed-routing scenario and deduce the corresponding lower-bound of average end-to-end delay across a link set.

Delay-guaranteed cross-layer scheduling in multihop wireless networks

In this paper, we propose a cross-layer scheduling algorithm that achieves a throughput “ ε-close” to the optimal throughput in multihop wireless networks with a tradeoff of O([1/(ε)]) in average end-to-end delay guarantees. The algorithm guarantees finite buffer sizes and aims to solve a joint congestion control, routing, and scheduling problem in a multihop wireless network while satisfying per-flow average end-to-end delay constraints and minimum data rate requirements. This problem has been solved for both backlogged as well as arbitrary arrival rate systems. Moreover, we discuss the design of a class of low-complexity suboptimal algorithms, effects of delayed feedback on the optimal algorithm, and extensions of the proposed algorithm to different interference models with arbitrary link capacities.

Opportunistic Periodic MAC Protocol for Cognitive Radio Networks

Cognitive radio networks enable opportunistic sharing of bandwidth/spectrum. In this paper, we propose a new Opportunistic Periodic MAC protocol (OP-MAC), a protocol that aims at improving the coexistence of licensed primary users and unlicensed secondary users in cognitive radio networks. Under OP-MAC, secondary users cooperate to periodically sense channels, report channel states and exchange control signals, in such a way that we can avoid the common control channel problem and the multi-channel hidden terminal problem. Analysis is provided for an ON/OFF channel scenario and is shown to be consistent with simulation results. With the requirement of only one transceiver per secondary user, OP-MAC is shown to provide higher capacity than a Random-MAC and a control-channel-based CO-MAC.

Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations

In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2 (S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations.

Adoption of Cognitive Radio Scheme to Class-Based Call Admission Control

By using cognitive radio technology, opportunistic spectrum access has the potential to solve the underused spectrum problem. In this paper, by first introducing a specific cognitive radio scheme, we analyze the secondary users capacity and the collision probability. Employing this scheme, we build two call admission control models for three classes of service, namely, handoff voice calls, new voice calls and data calls. We show that they achieve improved blocking probability and throughput by exploiting the cognitive radio scheme. Further, we show a tradeoff between collision probability and blocking/throughput in the call admission control.

On reducing delay and temporal starvation of queue-length-based CSMA algorithms

Recently, a group of queue-length-based CSMA algorithms have been proposed to achieve throughput optimality in wireless networks with single-hop transmissions. These algorithms suffer from two problems: (1) large delays, and (2) temporal starvation phenomenon, where communication links are inactive for a prolonged period of time before getting service. To mitigate these two problems, in this paper, we propose a novel v(t)-regulated CSMA algorithm which can be implemented in a distributed manner using the RTS/CTS mechanism. Link scheduling is performed such that links with longer queues are favored so as to reduce average delay. The v(t)-regulated CSMA algorithm also ensures a more frequent switch between schedules such that the effect of temporal starvation is reduced. We prove that the proposed algorithm is throughput optimal, and show through numerical evaluations that the algorithm indeed mitigates the temporal starvation problem and achieves far better delay performance than other throughput-optimal CSMA algorithms.

Capacity achieving distributed scheduling with finite buffers

In this paper, we propose a distributed cross-layer scheduling algorithm for wireless networks with single-hop transmissions that can guarantee finite buffer sizes and meet minimum utility requirements. The algorithm can achieve a utility arbitrarily close to the optimal value with a tradeoff in the buffer sizes. The finite buffer property is not only important from an implementation perspective, but, along with the algorithm, also yields superior delay performance. In addition, another extended algorithm is provided to help construct the upper bounds of per-flow average packet delays. A novel structure of Lyapunov function is employed to prove the utility optimality of the algorithm with the introduction of novel virtual queue structures. Unlike traditional back-pressure-based optimal algorithms, our proposed algorithm does not need centralized computation and achieves fully local implementation without global message passing. Compared to other recent throughput/utility-optimal CSMA distributed algorithms, we illustrate through rigorous numerical and implementation results that our proposed algorithm achieves far better delay performance for comparable throughput/utility levels.

Power Optimal Control in Multihop Wireless Networks With Finite Buffers

In this paper, we propose two cross-layer algorithms, namely, the Power-optimal Scheduling Algorithm (PSA) and the Throughput-optimal Scheduling Algorithm (TSA), to minimize energy consumption and to maximize throughput, respectively, in multihop wireless networks. Our algorithms guarantee a flow-based minimum data rate and jointly integrate congestion control, power allocation, routing, and link rate scheduling. Different from traditional algorithms that assume infinite buffers, the proposed algorithms deterministically upper bound the flow-based packet queue length and thus can be employed in multihop networks with finite buffers. In addition, the algorithms achieve a power expenditure/throughput “ε-close” to the optimal value, with a tradeoff of order O(1/ε) in buffer size. The average end-to-end delay upper bound can also be derived from the finite buffer property. Finally, numerical results are presented to show the performance of the two algorithms with different system parameters.

Optimization of Periodic Channel Sensing by Secondary Users in a Cognitive Radio Network

With the employment of cognitive radio technology, dynamic spectrum management has the potential to solve the underused spectrum problem. In this paper, we introduce and compare two periodic sensing and transmission schemes for secondary users (SUs) in cognitive radio networks. In an attempt to maximize the exploitation of spectrum opportunities in these two schemes, we analyze three metrics of SUs, namely, channel utility, collision rate and sensing overhead. Optimizations of SUs slot period are analyzed under single-SU single-channel scenario. The proposed schemes are applicable to any i.i.d. ON-OFF channel distribution.

A novel queue-length-based CSMA algorithm with improved delay characteristics

Abstract Recently, a group of queue-length-based CSMA algorithms have been proposed to achieve throughput optimality in wireless networks with single-hop transmissions. These algorithms suffer from two problems: (1) large delays, and (2) temporal starvation phenomenon, where communication links are inactive for a prolonged period of time before getting service. To mitigate these two problems, in this paper, we propose a novel v ( t )-regulated CSMA algorithm which can be implemented in a distributed manner using the RTS/CTS mechanism. Link scheduling is performed such that links with longer queues are favored so as to reduce average delay. The v ( t )-regulated CSMA algorithm also ensures a more frequent switch between schedules such that the effect of temporal starvation is reduced. The proposed algorithm is throughput optimal and achieves fully local implementation without global message passing. The thresholds to regulate the proposed algorithm are studied to optimize the upper-bound of the delay performance. We show through both hardware implementation and numerical evaluations that the algorithm indeed mitigates the temporal starvation problem and achieves far better delay performance than one of the other throughput-optimal CSMA algorithms.