We present an analytical framework to assess the link layer throughput of multichannel Opportunistic Spectrum Access (OSA) ad hoc networks. Specifically, we focus on analyzing various combinations of collaborative spectrum sensing and Medium Access Control (MAC) protocol abstractions. We decompose collaborative spectrum sensing into layers, parametrize each layer, classify existing solutions, and propose a new protocol called Truncated Time Division Multiple Access (TTDMA) that supports efficient distribution of sensing results in “K out of N” fusion rule. In case of multichannel MAC protocols, we evaluate two main approaches of control channel design with 1) dedicated and 2) hopping channel. We propose to augment these protocols with options of handling secondary user (SU) connections preempted by primary user (PU) by 1) connection buffering until PU departure and 2) connection switching to a vacant PU channel. By comparing and optimizing different design combinations, we show that 1) it is generally better to buffer preempted SU connections than to switch them to PU vacant channels and 2) TTDMA is a promising design option for collaborative spectrum sensing process when K does not change over time.
In later works, assumptions on the OSA network model became more realistic. Specifically, Markovian analysis of SU traffic buffering on the event of PU arrival was presented for a SU exponential service time @cite_48 and for a SU phase-type service time @cite_55 . Unfortunately the impact of spectrum sensing detection time overhead on the OSA network performance was not investigated and the connection arrangement process for new SU arrivals, i.e. method to select and access a channel for a new sender-receiver pair, was assumed to be performed by a centralized entity. A different option of the above model has been analyzed in @cite_26 , with only PU channels dedicated to the OSA network and with a mixture of PU and SU exclusive channels. SU connection buffering was not allowed, however, SU connections were able to switch to an empty SU exclusive channel on the event of channel preemption by the PU.
@cite_48 @cite_55 @cite_26
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Market-based mechanisms offer promising approaches for spectrum access in cognitive radio networks. In this paper, we focus on two market models, one with a monopoly primary user (PU) market and the other with a multiple PU market, where each PU sells its temporarily unused spectrum to secondary users (SUs). We propose a pricing-based spectrum trading mechanism that enables SUs to contend for channel usage by random access, in a distributed manner, which naturally mitigates the complexity and time overhead associated with centralized scheduling. For the monopoly PU market model, we first consider SUs contending via slotted Aloha. The revenue maximization problems here are nonconvex. We first characterize the Pareto optimal region, and then obtain a Pareto optimal solution that maximizes the SUs' throughput subject to the SUs' budget constraints. To mitigate the spectrum underutilization due to the price of contention, we revisit the problem where SUs contend via CSMA, and show that spectrum utilization is enhanced, resulting in higher revenue. When the PU's unused spectrum is a control parameter, we study further the tradeoff between the PU's utility and its revenue. For the multiple PU market model, we cast the competition among PUs as a three-stage Stackelberg game, where each SU selects a PU's channel to maximize its throughput. We characterize the Nash equilibria, in terms of access prices and the spectrum offered to SUs. Our findings reveal that the number of equilibria exhibits a phase transition phenomenon, in the sense that when the number of PUs is greater than a threshold, there exist infinitely many equilibria; otherwise, there exists a unique Nash equilibrium, where the a Because of the asynchronization between primary and secondary wireless networks, the synchronization between primary users (PUs) and secondary users (SUs) can be hardly guaranteed in nontime-slotted cognitive radio networks (CRNs). In this paper, we propose a novel framework for multichannel nontime-slotted CRNs, where the PUs randomly access and leave the licensed channels. Since the PUs cannot distinguish between primary and secondary signals, the PUs may sense a busy channel when the PUs start to reactivate during the SUs' transmission, thus generating a collision or entering the backoff stage. To guarantee the high-throughput transmission of the PUs and increase the channel utilization of the SUs, in this paper, we propose the wireless full-duplex spectrum sensing (FD-SS) scheme for SUs in multichannel nontime-slotted CRNs. Using our developed FD-SS scheme, the SUs can timely sense the PUs' reactivation during the same time when the SUs are transmitting their signals. Then, based on our proposed wireless FD-SS scheme, we further develop and analyze the wireless full-duplex cognitive medium access control (FDC-MAC) protocol for multichannel nontime-slotted CRNs. We conduct extensive numerical analyses, showing that our developed FD-SS scheme and FDC-MAC protocol can efficiently guarantee the high-throughput transmission of the PUs and increase the channel utilization of the SUs without requiring the synchronization between the PUs and the SUs over the multichannel nontime-slotted CRNs. In this work we study joint pricing and power allocation for Dynamic Spectrum Access (DSA) networks with Stackelberg game. In our model, Primary User (PU) is the game leader and jointly determines its power allocation (to guarantee its QoS requirement) and the interference price charged to Secondary User (SU) (to reap revenue). Meanwhile, SU is the game follower and determines its power demand in response to PU's decisions. We quantify PU's and SU's benefit from the channel sharing model by deriving the Stackelberg equilibrium. Our results show that PU's equilibrium profit is asymptotically upper bounded with its marginal power cost and rate requirement. A distributed algorithm is proposed to find the equilibrium. We also propose an incentive-compatible mechanism for PU and SU to keep the social welfare optimum cooperatively. We extend our Stackelberg game to the multiple SUs scenario, where the interference among SUs results in a noncooperative power demand subgame. We propose a low-complexity heuristic algorithm for PU to maximize its profit. Our results show that PU can benefit by selecting multiple SUs to share its channel if SUs' mutual interference is limited.