In this paper, we consider the transmission needs of communication networks for two classes of secondary users (SUs), named SU1 and SU2 (lowest priority) in cognitive radio networks (CRNs). In such CRNs, primary users (PUs) have preemptive priority over both SU1's users (SU1s) and SU2's users (SU2s). We propose a preemptive scheme (referred to as the P Scheme) and a non-preemptive scheme (referred to as the Non-P Scheme) when considering the interactions between SU1s and SU2s. Focusing on the transmission interruptions to SU2 packets, we present a probabilistic returning scheme with a returning probability to realize feedback control for SU2 packets. We present a Markov chain model to develop some formulas for SU1 and SU2 packets, and compare the influences of the P Scheme and the Non-P Scheme in the proposed probabilistic returning scheme. Numerical analyses compare the impact of the returning probability on the P Scheme and the Non-P Scheme. Furthermore, we optimize the returning probability and compare the optimal numerical results yielded by the P Scheme and the Non-P Scheme.

This paper presents a novel spectrum sensing technique based on selection diversity combining in cognitive radio networks. In general, a selection diversity combining scheme requires a period to select an optimal element, and spectrum sensing requires a period to detect a target signal. We consider that both these periods are required for the spectrum sensing based on selection diversity combining. However, conventional techniques do not consider both the periods. Furthermore, spending a large amount of time in selection and signal detection increases their accuracy. Because the required period for spectrum sensing based on selection diversity combining is the summation of both the periods, their lengths should be considered while developing selection diversity combining based spectrum sensing for a constant period. In reference to this, we discuss the spectrum sensing technique based on selection diversity combining. Numerical examples are shown to validate the effectiveness of the presented design techniques.

In this work, we address a joint energy efficiency (EE) and throughput optimization problem in interweave cognitive radio networks (CRNs) subject to scheduling, power, and stability constraints, which could be solved through traffic admission control, channel allocation, and power allocation. Specifically, the joint objective is to concurrently optimize the system EE and the throughput of secondary user (SU), while satisfying the minimum throughput requirement of primary user (PU), the throughput constraint of SU, and the scheduling and power control constraints that must be considered. To achieve these goals, our algorithm independently and simultaneously makes control decisions on admission and transmission to maximize a joint utility of EE and throughput under time-varying conditions of channel and traffic without a priori knowledge. Specially, the proposed scheduling algorithm has polynomial time efficiency, and the power control algorithms as well as the admission control algorithm involved are simply threshold-based and thus very computationally efficient. Finally, numerical analyses show that our proposals achieve both system stability and optimal utility.

This letter presents a computational complexity reduction technique for space diversity based spectrum sensing when the number of receive antennas is greater than three (NR≥3 where NR is the number of receive antenna). The received signals are combined with phase inversion so as to not attenuate the combined signal, and a statistic for signal detection is computed from the combined signal. Because the computation of only one statistic is required regardless of the number of receive antenna, the complexity can be reduced. Numerical examples and simple analysis verify the effectiveness of the presented technique.

In this letter, the problem of how to set reserve prices so as to improve the primary user's revenue in the second price-sealed auction under the incomplete information of secondary users' private value functions is investigated. Dirichlet process is used to predict the next highest bid based on historical data of the highest bids. Before the beginning of the next auction round, the primary user can obtain a reserve price by maximizing the additional expected reward. Simulation results show that the proposed scheme can achieve an improvement of the primary user's averaged revenue compared with several counterparts.

In this paper, we propose a novel censor-based cooperative spectrum sensing strategy, called adaptive energy-efficient sensing (AES), in which both sequential sensing and censoring report mechanism are employed, aiming to reduce the sensing energy consumption of secondary user relays (SRs). In AES, an anchor secondary user (SU) requires cooperative sensing only when it does not detect the presence of PU by itself, and the cooperative SR adopts decision censoring report only if the sensing result differs from its previous one. We derive the generalized-form expressions false alarm and detection probabilities over Rayleigh fading channels for AES. The sensing energy consumption is also analyzed. Then, we study sensing energy overhead minimization problem and show that the sensing time allocation can be optimized to minimize the miss detection probability and sensing energy overhead. Finally, numerical results show that the proposed strategy can remarkably reduce the sensing energy consumption while only slightly degrading the detection performance compared with traditional scheme.

Dynamic spectrum access is the key approach in cognitive wireless regional area networks, and it is adopted by secondary users to access the licensed radio spectrum opportunistically. In order to realize real-time secondary spectrum usage, a dynamic spectrum access model based on stochastic differential games is proposed to realize dynamic spectrum allocation; a Nash equilibrium solution to the model is given and analyzed in this paper. From an overall perspective, the relationships between available spectrum percentage and the spectrum access rate are studied. Changes in the available spectrum percentage of the cognitive wireless regional area networks involve a deterministic component and a stochastic component which depends upon an r-dimensional Wiener process. The Wiener process represents an accumulation of random influences over the interval, and it reflects stochastic and time-varying properties of the available spectrum percentage. Simulation results show that the dynamic spectrum access model is efficient, and it reflects the time-varying radio frequency environment. Differential games are useful tools for the spectrum access and management in the time-varying radio environment.

In this paper, we focus on a centralized spectrum access strategy in a cognitive radio network, in which a single licensed spectrum with one primary user (PU) and several secondary users (SUs) (multiple input streams) are considered. We assume the spectrum can be divided into multiple channels and the packets from variable SUs can arrive at the system simultaneously. Taking into account the priority of the PU, we suppose that one PU packet can occupy the whole licensed spectrum, while one SU packet will occupy only one of the channels split from the licensed spectrum when that channel is not used. Moreover, in order to reduce the blocking ratio of the SUs, a buffer with finite capacity for the SUs is set. Regarding the packet arrivals from different SUs as multiple input streams, we build a two-dimensional Markov chain model based on the phase of the licensed spectrum and the number of SU packets in the buffer. Then we give the transition probability matrix for the Markov chain. Additionally, we analyze the system model in steady state and derive some important performance measures for the SUs, such as the average queue length in the buffer, the throughput and the blocking ratio. With the trade-off between different performance measures, we construct a net benefit function. At last, we provide numerical results to show the change trends of the performance measures with respect to the capacity of the SU buffer under different network conditions, and optimize the capacity of the SU buffer accordingly.

Recently, cooperative spectrum sensing is being studied to greatly improve the sensing performance of cognitive radio networks. To develop an adaptable cooperative sensing algorithm, an important issue is how to properly induce selfish users to participate in spectrum sensing work. In this paper, a new cognitive radio spectrum sharing scheme is developed by employing the trust-based bargaining model. The proposed scheme dynamically adjusts bargaining powers and adaptively shares the available spectrum in real-time online manner. Under widely different and diversified network situations, this approach is so dynamic and flexible that it can adaptively respond to current network conditions. Simulation results demonstrate that the proposed scheme can obtain better network performance and bandwidth efficiency than existing schemes.

This paper proposed three channel aggregation schemes for cognitive radio networks, a constant channel aggregation scheme, a probability distribution-based variable channel aggregation scheme, and a residual channel-based variable channel aggregation scheme. A cognitive radio network can have a wide bandwidth if unused channels in the primary networks are aggregated. Channel aggregation schemes involve either constant channel aggregation or variable channel aggregation. In this paper, a Markov chain is used to develop an analytical model of channel aggregation schemes; and the performance of the model is evaluated in terms of the average sojourn time, the average throughput, the forced termination probability, and the blocking probability. Simulation results show that channel aggregation schemes can reduce the average sojourn time of cognitive users by increasing the channel occupation rate of unused channels in a primary network.

This letter proposes a novel connected dominanting set based decentralized cooperative spectrum sensing algorithm for cognitive radio networks. It is analytically shown that the proposed algorithm distributively converges to the average consensus as that of traditional distributed consensus algorithm, while reducing both the convergence time and message complexity significantly.

We study joint rate control and resource allocation with a packet collision constraint that maximizes the total utility of secondary users in cognitive radio networks. We formulate and decouple the original optimization problem into separable subproblems and then develop an algorithm that converges to optimal rate control and resource allocation. The proposed algorithm can operate on different time-scales to reduce the amortized time complexity.

In this paper, we present a distributed and interactive admission and power control protocol for spectrum underlay environments. The protocol enables distributed primary users (PUs) to estimate and adjust the level of tolerable interference as their transmitting powers evolve to a given signal-to-interference-plus-noise ratio (SINR) target. The protocol also guides the powers of distributed secondary users (SUs) to achieve their own targets while restricting the transmitting powers from SUs so as not to interfere with the PUs. This restriction of interference from SUs to PUs is an essential part of cognitive radio networks (CRNs) and is facilitated by sending a warning tone from PUs to SUs in the proposed protocol. The SUs that have frequently received the warning tones turn off their transmitters and so autonomously drop from the system. This paper proves that, under the proposed interactive protocol, every PU finally achieves its target if it is originally feasible without SUs and the transmit powers of remaining SUs converge to a fixed point. The proposed method protects PUs perfectly in the sense that all the PUs reach their targets after power control. Numerical investigation shows how safely PUs are protected and how well SUs are admitted as a function of protocol parameters, the frequency of warning tones, the number of SUs to be admitted and the number of active PUs.

The traditional spectrum auctions require a central auctioneer. Then, the secondary users (SUs) can bid for spectrum in multiple auction or sealed auction way. In this paper, we address the problem of distributed spectrum sharing in the cognitive networks where multiple owners sell their spare bands to multiple SUs. Each SU equips multi-interface/multi-radio, so that SU can buy spare bands from multiple owners. On the other hand, each owner can sell its spare bands to serval SUs. There are two questions to be addressed for such an environment: the first one is how to select bands/the owners for each SU; the second one is how to decide the competitive prices for the multiple owners and multiple SUs. To this end, we propose a two-side multi-band market game theoretic framework to jointly consider the benefits of all SUs and owners. The equilibrium concept in such games is named core. The outcomes in the core of the game cannot be improved upon by any subset of players. These outcomes correspond exactly to the price-lists that competitively balance the benefits of all SUs and owners. We show that the core in our model is always non-empty. When the measurement of price takes discrete value, the core of the game is defined as discrete core. The Dynamic Multi-band Sharing algorithm (DMS) is proposed to converge to the discrete core of the game. With small enough measurement unit of price, the algorithm can achieve the optimal performance compared with centralized one in terms of the system utility.

Cognitive radio has emerged as an efficient approach to reusing the licensed spectrums. How to appropriately set parameters of secondary user (SU) plays a rather important role in constructing cognitive radio networks. In this letter, we have analyzed the theoretical value of SUs' density, which provides a standard for controlling the number of SUs around one primary receiver, in order to guarantee that primary communication links do not experience excessive interference. The simulation result of secondary density well matches with the theoretical result derived from our analysis. Additionally, the achievable rate of secondary user under density control is also analyzed and simulated.