Primary User Traffic Patterns Research Articles

Infrastructure based spectrum sensing scheme in VANET using reinforcement learning

Spectrum sensing is one of the fundamental functionality performed by a cognitive radio to identify vacant radio spectrum for dynamic spectrum access (DSA). However, there are many challenges still existing before the benefits of DSA can be realized. The challenges include multipath fading, shadowing and hidden primary user (PU) problem. The challenges are more severe in vehicular communication due to unique characteristics such as dynamic topology caused by vehicle mobility. Furthermore, spectrum sensing is dependent on the activities of the PU traffic pattern which are not known in advance. In a typical cognitive radio network, the PU plays a passive role. Therefore, a sensing technique should account for traffic pattern of the PU autonomously. However, most of the proposed spectrum sensing schemes in vehicular communication assumes a static ON/OFF PU model which does not realistically model the PU traffic pattern. In this paper, we propose reinforcement learning (RL) to model the traffic pattern of the PU and use the model to predict channels likely to be free in future. The RL is implemented on road side unit (RSU) which send predicted vacant PU channels to vehicles on the road. Before the channels can be used, vehicles perform spectrum sensing. To account for multipath fading and shadowing, adaptive spectrum sensing is proposed. The results from spectrum sensing, sensing time and PU channel capacity are calculated into a scalar value and used as reward for RL at RSU. The RSU continuously update the reward for channels of interest using sensing history from passing vehicles as reward. Compared to history based schemes from literature, the RL technique proposed in this paper performs better.

Incorporating Primary Occupancy Patterns in Compressive Spectrum Sensing

Wideband spectrum sensing remains one of the challenging problems facing the wide deployment of cognitive radio networks. Compressive sensing (CS) was proposed as a promising approach to this problem by utilizing the sparse structure of the underutilized spectrum to capture the spectrum with fewer measurements and simpler hardware requirements. Most of the work in compressive spectrum sensing solely exploits the spatial- and frequency-domain structure of the spectrum neglecting the temporal structure arising from the regularity of primary user (PU) traffic patterns. In this paper, we explore the effectiveness of incorporating PU traffic patterns in compressive spectrum sensing. This achieves improved sensing performance by exploiting the statistics of the PU activity in the CS recovery algorithms. The experimental analysis through simulation shows that the proposed schemes can substantially improve the receiver operating characteristic performance at lower sampling rate noisy spectrum measurements.

Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation: Random and Adaptive Spectrum Sensing Strategies

Hardware limitation at the secondary user (SU) terminal makes multiband (wideband) spectrum sensing more challenging. This paper considers spectrum sensing under SU hardware limitation, where the SU can only sense a small portion of the multiband spectrum for a given time period. This introduces a design issue of selecting channels to sense at a given time. A random spectrum sensing strategy (RSSS) is presented to select the subchannels to sense in a totally random fashion. Considering the Markov property of the state transition of a primary user (PU), an adaptive spectrum sensing strategy (ASSS) is then proposed to take advantage of the PU traffic patterns in determining the subchannels to sense. In the proposed ASSS, a novel decision rule is designed and two decision combinations are obtained. The ASSS decision rule adaptively selects a decision combination to determine the subchannels to sense for SU such that the selected subchannels are more likely to be available for the SU network. A metric called spectrum sensing capability (SSC) is defined to evaluate the performance of any spectrum sensing strategies. The SSC expressions for both RSSS and ASSS are derived. Numerical results show significant performance gain of ASSS as compared to RSSS.

Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation

In this paper, we consider hardware limitation at the secondary user, which makes multiband (wideband) spectrum sensing more challenging. Under secondary user (SU) hardware limitation, the SU can only sense a small portion of the multiband spectrum for a given time period, which introduces a design issue of selecting subchannels to sense at a given time. A random spectrum sensing strategy (RSSS) is presented to select the subchannels to sense in a totally random fashion. With the Markov assumption of the primary user (PU) behavior, a persistent spectrum sensing strategy (PSSS) is proposed to take advantage of the PU traffic patterns in determining the channels to sense. Theoretical and simulation results show that RSSS and PSSS display different performance in different ranges of PU traffic parameters. We finally propose an adaptive spectrum sensing strategy (ASSS), which determines whether to use RSSS or PSSS for spectrum sensing at a given time based on the estimated PU traffic parameters. Numerical results under various system parameters are presented to evaluate the performance of RSSS, PSSS, and ASSS. The ASSS is shown to gain the advantages of both RSSS and PSSS in different ranges of PU traffic parameters and provide more available subchannels for SU.

Cooperative Prediction for Cognitive Radio Networks

Combining spectrum sensing (SS) and primary user (PU) traffic forecasting provides a cognitive radio network with a platform from which informed and proactive operational decisions can be made. The success of these decisions is largely dependent on prediction accuracy. Allowing secondary users (SU) to perform these predictions in a collaborative manner allows for an improvement in the accuracy of this process, since individual SUs may suffer from SS and prediction inaccuracies due to poor channel conditions. To overcome these problems a collaborative approach to forecasting PU traffic behaviour, that combines SS and forecasting through SU cooperation, has been proposed in this article. Both pre-fusion and post-fusion scenarios for cooperative prediction were investigated and a number of binary prediction methods were considered (including the authors' own simple technique). Cooperative prediction performance was investigated, under various PU traffic conditions, for a group of ten SUs experiencing different channel conditions and a sub-optimal cooperative forecasting algorithm was proposed to minimise cooperative prediction error. Simulation results indicated that the accuracy of the prediction methods was influenced by the PU traffic pattern and that cooperative prediction lead to a significant improvement in prediction accuracy under most of the traffic conditions considered. However, this came at the cost of increased computational complexity. The pre-fusion scenario was found to be the most accurate scenario (up to 25 % improvement), but was also eleven times more complex than when no fusion was employed. The cooperative forecasting algorithm was found to further improve these results.

On the feasibility of synchronous, retransmission-based cognitive networks

This paper presents a new, joint Markov chain model for the primary and secondary traffic in a cognitive radio network to assess the feasibility of opportunistic spectrum access for different operational scenarios, validated by extensive realistic Monte-Carlo simulations. Enhancing on the current literature, the generalized model allows for the consideration of a retransmission-based traffic as well as the presence of transmission queues for the primary user. The generalized model also allows for the investigation of the system behavior in the presence of imperfect sensing at the secondary users. The presented model is applicable for all primary user traffic models that use discrete-time Markov chains. An infinitely backlogged secondary user network is considered to investigate the maximum possible network utilization. The paper concludes that a percentile channel occupation of the primary user on its own is not always a sufficient metric to assess whether secondary transmission is feasible. It is shown that the interplay between primary network traffic characteristics, as well as the sensing frequency and probabilities of missed detection and false alarm for the secondary network detector are of primal importance for such feasibility claims. The results reveal that the preferred sensing frequency for the secondary users is a function of the primary user traffic pattern and that the commonly used frequency of sensing every transmission slot is not always optimal. The results also show that when sensing frequency is low, secondary utilization decreases with more bursty primary traffic, whereas the impact on primary utilization becomes less.

Dynamic Sensing Strategies for Efficient Spectrum Utilization in Cognitive Radio Networks

For cognitive radio (CR) networks with user hierarchy, the sensing strategy with "listen-before-talk" (LBT) policy plays a key role in spectrum utilization and primary user (PU) protection. However, existing sensing strategies do not handle satisfactorily the randomness of both user locations and channel conditions in the network environment, resulting in inefficient spectrum utilization. To cope with such randomness, this paper develops three dynamic sensing strategies that can adaptively schedule the sensing slots/cycles according to the online link conditions without assuming knowledge of the PU traffic model. The proposed strategies can improve the efficiency of spectrum utilization while being robust with respect to the uncertainty in the PU traffic pattern. To maximize spectrum utilization, the proposed strategies are formulated through closed-form expressions. Efficient methods are introduced to compute the optimal values of the parameters used in the strategies, such as sensing time and sensing threshold. Simulations verify that the proposed sensing strategies offer an evident improvement on the spectrum utilization of the CR network.

Opportunistic Access to Spectrum Holes Between Packet Bursts: A Learning-Based Approach

We present a cognitive radio (CR) mechanism for opportunistic access to the frequency bands licensed to a data-centric primary user (PU) network. Secondary users (SUs) aim to exploit the short-lived spectrum holes (or opportunities) created between packet bursts in the PU network. The PU traffic pattern changes over both time and frequency according to upper layer events in the PU network, and fast variation in PU activity may cause high sensing error probability and low spectrum utilization in dynamic spectrum access. The proposed mechanism learns a PU traffic pattern in real-time and uses the acquired information to access the frequency channel in an efficient way while limiting the probability of collision with the PUs below a target limit. To design the channel learning algorithm, we model the CR system as a hidden Markov model (HMM) and present a gradient method to find the underlying PU traffic pattern. We also analyze the identifiability of the proposed HMM to provide a condition for the convergence of the proposed learning algorithm. Simulation results show that the proposed algorithm greatly outperforms the traditional listen-before-talk algorithm which does not possess any learning functionality.

Optimal Transmission Strategies for Dynamic Spectrum Access in Cognitive Radio Networks

Cognitive radio offers a promising technology to mitigate spectrum shortage in wireless communications. It enables secondary users (SUs) to opportunistically access low-occupancy primary spectral bands as long as their negative effect on the primary user (PU) access is constrained. This PU protection requirement is particularly challenging for multiple SUs over a wide geographical area. In this paper, we study the fundamental performance limit on the throughput of cognitive radio networks under the PU packet collision constraint. With perfect sensing, we develop an optimum spectrum access strategy under generic PU traffic patterns. Without perfect sensing, we quantify the impact of missed detection and false alarm, and propose a modified threshold-based spectrum access strategy that achieves close-to-optimal performance. Moreover, we develop and evaluate a distributed access scheme that enables multiple SUs to collectively protect the PU while adapting to behavioral changes in PU usage patterns. Our results provide useful insight on the trade-off between the protection of the primary user and the throughput performance of cognitive radios.