MohammadJavad NoroozOliaee

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In this paper, we derive and evaluate private objective functions for large-scale, distributed opportunistic spectrum access (OSA) systems. By means of any learning algorithms, these derived objective functions enable OSA users to assess, locate, and exploit unused spectrum opportunities effectively by maximizing the users' average received rewards. We(More)
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(More)
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 SU's rewards measured in terms of the average received throughput. We show that the proposed objective functions are: near-optimal, as(More)
We develop resource and service management techniques to support secondary users (SUs) with QoS 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 SUs’ long-term received service satisfaction levels.(More)
Most existing studies on cognitive radio networks assume that cognitive users can switch to any available channel, regardless of the frequency gap between the target channel and the current channel. However, due to hardware limitations, cognitive users can actually jump only so far from where the operating frequency of their current channel is. This paper(More)
We propose an adaptive service model that maximizes the amount of service that spectrum users (SUs) achieve from accessing DSA systems. The proposed model allows SUs to utilize available spectrum efficiently by enabling them to locate spectrum opportunities in a distributed manner, thereby maximizing the long-term rewards that SUs receive. In this model,(More)
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(More)
We develop efficient coordination techniques that support inelastic traffic in large-scale distributed dynamic spectrum access (DSA) networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or “rewards” to be(More)
We develop objective functions for large-scale distributed dynamic spectrum access (DSA) networks that, by means of any learning algorithm, enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or "rewards" to be more general). We show that the proposed functions are: (i) optimal by enabling(More)
We develop efficient coordination techniques that support inelastic traffic in large-scale distributed dynamic spectrum access (DSA) networks. By means of any learning algorithm, the proposed techniques enable DSA users to locate and exploit spectrum opportunities effectively, thereby increasing their achieved throughput (or “rewards” to be more general).(More)