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.

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)2utilizes 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.

Efficient scheduling of wireless resources has always been one of the most challenging tasks for wireless networks. To achieve throughput-optimality, traditional back-pressure algorithms calculate a maximal weight matching at each time slot. However, these algorithms need centralized scheduling with high complexity, and thus are not suitable for practical distributed implementations. A class of distributed queue-length-based CSMA algorithms have been proposed that achieve throughput optimality, which we refer to as regular throughput-optimal. These algorithms suffer from two problems: large delays, and temporal starvation. In this demo we demonstrate the operation of the v(t)-regulated CSMA algorithm that mitigates these two problems while provably retaining throughput optimality. The demo allows the participants to see the the performance advantage of v(t)-regulated CSMA over queue-length-based CSMA algorithms and change the different system parameters.

In this paper, a cross-layer optimal scheduling algorithm for cooperative multi-hop Cognitive Radio Networks (CRNs) is presented, where secondary users (SUs) assist primary users' (PUs') multi-hop transmissions and in return gain an immediate time-share of the channel proportional to their assistance. While providing deterministic upper-bounds for PU queue backlogs, the proposed algorithm approaches the optimal PU throughput arbitrarily close, with a tradeoff in the average delay upper-bounds of flows. The analysis is further extended to a model with a more general "long-term reward mechanism", where a time-averaged share of the channel is guaranteed for SUs. The proposed algorithm provides order-optimal delay for the primary traffic. Distributed implementation issues have also been investigated. The properties of the proposed algorithm have also been illustrated through simulation studies.

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.

In this paper, we develop a cross-layer algorithm to minimize energy consumption in multi-hop wireless networks with finite buffers. Our algorithm guarantees a flow-based minimum data rate and a deterministic buffer size upper-bound for individual flows at network nodes. The algorithm jointly integrates congestion control, power allocation, routing and link rate scheduling. In addition, the algorithm achieves a power expenditure "epsilon-close" to the optimal value, with a tradeoff of order O(1/epsilon) in the buffer size. Finally, numerical results are presented to show the proximity to optimality with a significant reduction in queue backlog.

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.

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.

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 SU's slot period are analyzed under single-SU single-channel scenario. The proposed schemes are applicable to any i.i.d. ON-OFF channel distribution.

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 user's 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.