MohammadJavad NoroozOliaee

Analyzing Cognitive Network Access Efficiency Under Limited Spectrum Handoff Agility

Most existing studies on cognitive-radio networks assume that cognitive users (CUs) can switch to any available channel, regardless of the frequency gap between a target channel and the current channel. However, due to hardware limitations, CUs can actually jump only so far from where the operating frequency of their current channel is. This paper studies the performance of cognitive-radio networks while considering realistic channel handoff agility, where CUs can only switch to their neighboring channels. We use a continuous-time Markov process to derive and analyze the forced termination and blocking probabilities of CUs. Using these derived probabilities, we then study and analyze the impact of limited spectrum handoff agility on cognitive spectrum access efficiency. We show that accounting for realistic spectrum handoff agility reduces performance of cognitive-radio networks in terms of spectrum access capability and efficiency.

Distributed dynamic spectrum access with adaptive power allocation: Energy efficiency and cross-layer awareness

This paper proposes energy and cross-layer aware resource allocation techniques that allow dynamic spectrum access users, by means of learning algorithms, to locate and exploit unused spectrum opportunities effectively. Specifically, we design private objective functions for spectrum users with multiple channel access and adaptive power allocation capabilities. We also propose a simple, two-phase heuristic for allocating spectrum and power resources among users. The proposed heuristic splits the spectrum and power allocation problem into two sub-optimal problems, and solve each of them separately. The spectrum allocation problem is solved, during the first phase, using learning whereas, the power allocation is formulated as an optimization problem and solved, during the second phase, by traditional optimization solvers. Simulation results show that energy and cross-layer awareness and multiple channel access capability improve the performance of the system in terms of the per-user average rewards received from accessing the dynamic spectrum access system.

Achieving optimal elastic traffic rewards in dynamic multichannel access

This paper proposes objective functions for dynamic multi-channel access (DMA) networks that enable spectrum users (SUs) to assess, locate, and exploit available spectrum opportunities effectively, thereby maximizing the SUs rewards measured in terms of the average received throughput. We show that the proposed objective functions are: near-optimal, as they achieve high rewards; scalable, as they perform well in small- as well as large-scale DMA networks; learnable, as they allow SUs to reach up near-optimal rewards very quickly; and distributive, as they are implementable by requiring local information sharing only.

Distributed resource and service management for large-scale dynamic spectrum access systems through coordinated learning

We develop resource and service management techniques to support spectrum users (SUs) with quality of service requirements in large-scale distributed dynamic spectrum access (DSA) systems. The proposed techniques empower SUs to seek and exploit spectrum opportunities dynamically and effectively, thereby maximizing the long-term service satisfaction levels that SUs receive from accessing and using the DSA system. Our techniques are efficient in terms of optimality, scalability, distributivity, and fairness. First, they enable SUs to achieve high service satisfaction levels by quickly locating and accessing available spectrum opportunities. Second, they are scalable by performing well in systems with small as well as large numbers of SUs. Third, they can be implemented in a decentralized manner by relying on local information only. Finally, they ensure fairness among SUs by allowing them to receive equal amounts of service.

Online multi-resource scheduling for minimum task completion time in cloud servers

We design a simple and efficient online scheme for scheduling cloud tasks requesting multiple resources, such as CPU and memory. The proposed scheme reduces the queuing delay of the cloud tasks by accounting for their execution time lengths. We also derive bounds on the average queuing delays, and evaluate the performance of our proposed scheme and compare it with those achievable under existing schemes by relying on real Google data traces. Using this data, we show that our scheme outperforms the other schemes in terms of resource utilizations as well as average task queuing delays.

Forced Spectrum Access Termination Probability Analysis under Restricted Channel Handoff

Most existing works on cognitive radio networks assume that cognitive (or secondary) users are capable of switching/jumping to any available channel, regardless of the frequency gap between the target and the current channels. Due to hardware limitations, cognitive users can actually jump only so far from where the operating frequency of their current channel is, given an acceptable switching delay that users are typically constrained by. This paper studies the performance of cognitive radio networks with dynamic multichannel access capability, but while considering realistic channel handoff assumptions, where cognitive users can only move/jump to their immediate neighboring channels.

Analysis of guard-band-aware spectrum bonding and aggregation in multi-channel access cognitive radio networks

Adjacent channel interference (ACI) is often not considered when spectrum sharing schemes are designed for cognitive radio networks (CRNs). In practice, it is necessary to avoid interference by deploying guard bands between two distinct receptions. However, using guard bands typically reduces spectrum efficiency. In this work, we study the impact of guard bands on spectrum efficiency under different spectrum sharing schemes. Specifically, we model cognitive radio network as a continuous-time Markov process. We derive blocking probability, forced termination probability, spectrum efficiency and average number of guard bands using our Markov model. Using these metrics, we then study and analyze the impact of using guard bands on the performance of cognitive radio networks. We show that taking guard bands into consideration is critical as disregarding this realistic issue results in inaccurate conclusions and outcomes.