Near-optimal spectrum allocation for cognitive radios: A frequency-time auction perspective

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use a given licensed spectrum band as long as there are no primary, licensed, users occupying this band in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. Heterogeneity in channel availability forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take this heterogeneity property and its effect on the network performance into consideration. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by the heterogeneity in channel availability. We proposed a

This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> focuses on spectrum allocation and medium access in future cognitive radio wireless networks, e.g. IEEE 802.22 networks. For a given network topology, the proposed spectrum allocation method first generates an interference graph (or conflict graph) based on the network topology, traffic flows and interference constraints. Then a graph coloring algorithm is applied to the interference graph and the chromatic number of the graph is obtained. This number is the minimum number of distinct spectrum slices required. Further, spectrum allocation is done based on the max-min fair share algorithm or proportional fair algorithm according to the spectrum demands for different spectrum blocks. For medium access among secondary users, we then describe and implement a MAC protocol for 802.22-type cognitive radio networks. Simulation results are presented to demonstrate its effectiveness.

With the rapid development of wireless services and applications, the currently radio spectrum is becoming more crowded. How to accommodate more wireless services and applications within the limited radio spectrum becomes a big challenge faced by modern society. Cognitive radio (CR) is proposed as a promising technology to tackle this challenge by introducing secondary users (SUs) to opportunistically or concurrently access the spectrum allocated to primary users (PUs). Currently, there are two prevalent CR models: the spectrum sharing model and the opportunistic spectrum access model. In the spectrum sharing model, the SUs are allowed to coexist with the PUs as long as the interferences from SUs do not degrade the quality of service (QoS) of PUs to an unacceptable level. In the opportunistic spectrum access model, SUs are allowed to access the spectrum only if the PUs are detected to be inactive. These two models known as underlay and overlay schemes, respectively. This thesis studies a number of topics in CR networks under the framework of these two schemes. First, studied cognitive radio transmissions under QoS delay constraints. Initially, we focused on the concept: effective capacity for cognitive radio channels in order to identify the performance in the presence of QoS constraints. Both underlay and overlay schemes are studied taking into consideration the activity of primary users, and assuming the general case of channel fading as Gamma distribution. For this setting, we further proposed a selection criterion by which the cognitive radio network can choose the adequate mode of operation. Then, we studied the cognitive radio transmissions focusing on Rayleigh fading channel and assumed that no prior channel knowledge is available at the transmitter and the receiver. We investigated the performance of pilot-assisted transmission strategies. In particular, we analyzed the channel estimation using minimum mean-square-error (MMSE) estimation, and analyzed efficient resource allocation strategies. In both cases, power allocations and effective capacity optimization were obtained. Effective capacity and interference constraint were analyzed in both single-band and multi-band spectrum sensing settings. Finally, we studied optimal access probabilities for cognitive radio network using Markov model to achieve maximum throughput for both CR schemes.

Quality of experience (QoE) of a secondary spectrum user is mainly governed by its spectrum utilization, the energy consumption in spectrum sensing and the impact of channel switching in a cognitive radio network. It can be enhanced by prediction of spectrum availability of different channels in the form of their idle times through historical information of primary users’ activity. Based on a reliable prediction scheme, the secondary user chooses the channel with the longest idle time for transmission of its data. In contrast to the existing method of statistical prediction, the use and applicability of supervised learning based prediction in various traffic scenarios have been studied in this paper. Prediction accuracy is investigated for three machine learning techniques, artificial neural network based Multilayer Perceptron (MLP), Support Vector Machines (SVM) with Linear Kernel and SVM with Gaussian Kernel, among which, the best one is chosen for prediction based opportunistic spectrum access. The results highlight the analysis of the learning techniques with respect to the traffic intensity. Moreover, a significant improvement in spectrum utilization of the secondary user with reduction in sensing energy and channel switching has been found in case of predictive dynamic channel allocation as compared to random channel selection.

Cognitive radio has developed as a novelapproach that enables efficient use of unexploited or underutilized parts of frequency bands. Wireless cognitive radio networks aim to speculativelyfeat the empty or underutilized parts of the spectrum by listening to the frequency bands of their secondary users with various sensing techniques. For this purpose, the process of switching from a frequency channel used by secondary users for various reasons to another free channel is defined as spectrum allocation. At the beginning of the cases where the spectrum allocation takes place, the frequency channels allocated to secondary users are put back into the service of primary users. Severaltechniques are utilized for the spectrum allocation process decided by the access points. Artificial intelligence techniques are used to make the decision to realize the spectrum allocation transaction. In this paper, we present a fuzzy logic based spectrum allocation technique and thus ensure better management. The simulation results shows the performance in terms of received signal strength and signal to noise power.

The emerging feature-rich and high-data-rate wireless applications have put increasing demand on radio spectrum. The scarcity of spectrum and the inefficiency in its usage, as caused by the current radio spectrum regulations, necessitate the development of new dynamic spectrum allocation policies to better exploit the existing spectrum. The current spectrum allocation regulations assign specific bands to particular services, and grant licensed band access to licensed users only. Cognitive Radio (CR) will revolutionize the way spectrum is allocated. In a CR network, the intelligent radio part allows unlicensed users (secondary users) to access spectrum bands licensed to primary users, while avoiding interference with them. In this scheme, a secondary user can use spectrum sensing hardware/software to locate spectrum portions with reduced primary user activity or idle spectrum slots, select the best available channel, coordinate access to this channel with other secondary users, and vacate the channel when a primary user needs it. To achieve this, the transceiver in a CR system should have awareness of the radio environment in terms of spectrum usage, power spectral density of transmitted/received signals, and wireless protocol signaling, should be able to adaptively tune system parameters such as transmit power, carrier frequency, and modulation strategy. The transceiver should also be ended with an antenna system that can simultaneously operate over a wide frequency band (sensing) and a chosen narrow band (communication), or operate over an ultra-wide frequency band while possibly blocking signals in a narrow frequency range. Ultra-wideband (UWB) is a transmission technique that uses pulses with a very short time duration across a very large frequency portion of the spectrum. UWB is different from other radio frequency communication techniques in that it does not use RF carriers, but instead employs modulated high frequency pulses of low power with a duration of less than 1 nanosecond. From the perspective of other communication systems, the UWB transmissions are part of the low power background noise. Therefore UWB promises to enable the usage of licensed spectrum without harmful interference to primary communication systems, and can be used as an enabling technology for implementing CR. In this chapter, we offer a general overview of Cognitive Radio and dynamic spectrum access, discuss the advantages of using UWB as an enabling technology for CR, and give

Recently, cognitive radio has been proposed to improve the spectrum resource utilisation. In cognitive radio networks, the secondary or unauthorised users SUs are allowed to sense, detect and utilise the frequency bands that are not currently being used. Once the primary user PU is active, the secondary users have to vacate the channel within certain amount of time. Thus, it is a challenging task to find the suitable sensing period for better spectrum utilisation. In this paper, we propose a selectable sensing period optimisation SSPO mechanism. In the algorithm, each secondary user use the different periods for channel detection according to the different channel states. so that we can deal with the tradeoff between spectrum utilisation and energy consumption more flexibly. We consider the constraint of interference to PU and the energy consumption to compute the optimal sensing periods. Our simulation results demonstrate that, with the constraint of energy consumption, the SSPO algorithm can gain the spectrum utilisation 22:8% more than the previous algorithm. Moreover, with the constraint of spectrum utilisation, the SSPO algorithm can save the energy consumption 20:6% more than the previous algorithm.

This paper considers a cognitive radio (CR) network comprising one cognitive base station (CBS) and a number of secondary users (SUs) that shares a set of channels with a primary network. Both uplink and downlink scenarios are considered in the CR network. The objective is to maximize the throughput of the CR network while protecting the primary transmission and maintaining fairness among the SUs. To fairly allocating resources among the SUs, minimum/maximum number of channels constraint of the SU is introduced to flexibly distribute available channels between the SUs. For both uplink and downlink scenarios, we propose a joint channel allocation and power control scheme. Additionally, a low-complexity two-phase channel allocation and power control scheme is also proposed in the downlink scenario. Simulation results are obtained to validate the performance of the proposed schemes. It is shown that the proposed schemes offer flexibility and controllability in fairly allocating channels to the SUs and yet achieve high throughput. Furthermore, it is shown that the performance gap between the joint scheme and the two-phase scheme is negligible.

Cognitive Radio Networks (CRNs) is a formidable evolution in wireless technology owing to its potentiality to resolve spectrum scarcity crisis. In CRNs, cognitive radio user’s sense idle spectrum as well as opportunistically entrée sensed spectrum devoid of approved users. The best spectrum is said to be selected opportunistically, shared with other users with no severity to licensed users. Many CRN devices that are fueled by battery possessing constrained energy capacity. Prevailing battery mechanisms yet couldn’t contribute CRN devices works for a long time. Therefore, conservation of energy is mandatory as far as routing in CRN is concerned. In this work a machine learning based method called, Regression Cooperative Spectrum Sensing and Gradient Flying Fox Route Optimization (RCSS-GFFRO) for energy and bandwidth aware data routing with spectrum utilization in CRNs is proposed. Protection of primary users (PUs) as well as optimization of secondary user (SU) transmission included in proposed method. In the proposed method a cooperative spectrum sensing using machine learning (ML) model called, Median Regression function is applied. The Median Regression Cooperative Spectrum Sensing in our work with the bandwidth and energy efficiency ensures acceptable probability of interference to primary users due to secondary user’s access by measuring the relationship between bandwidth and energy efficiency simultaneously. Next, optimal route identification is performed using Gradient Flying Fox Route Optimization algorithm. By applying gradient optimization function minimizes the interference with primary users and increases secondary user performance via sensing time and transmission time parameters with higher routing performance. Simulation estimated with parameters.

Both the internet-connected devices, i.e. IoT and Cognitive Radio Network (CRN) are considered to be the future technologies for the fifth generation of cellular wireless standards (5G). On the one hand, Internet of Things (IoT) focuses primarily on how to allow general objects to see, hear, and smell their own physical environment and make them connected to share the observations. On the other hand, a CRN is based on a complex spectrum allocation system, and licenced primary users (PUs) or unlicenced secondary users (SUs) are allowed to share the spectrum, provided they do not cause significant interference. The IoTs are meaningless if IoT objects are not equipped with cognitive radio capability. In cognitive radio, it is important to control the transmission power of SUs so that the interference should not be harmful to the quality of service of PUs. In this paper, the authors addressed the effects of imperfect power control between primary users (PUs) and the secondary users (SUs) of an IoT-based CRN. The effect of the co-channel interference (CCI) and adjacent channel interferences (ACIs) occurring in CRN using MC-CDMA system is also analysed. A new expression of the signal-to-interference-noise ratio (SINR) for CRN-based MC-CDMA system over a Nakagami-[Formula: see text] fading channel with imperfect power control condition is derived and investigated. The performance of IoT-based CRN using MC-CDMA system over the frequency selective multipath fading channel is examined with varying the number of users, the SINR per bit, number of fading path and number of sub-carriers. From the simulation results, we have seen that the SINR performance is affected by these parameters. The result of the analysis will provide relevant information to design the physical layer protocol for high-speed IoT-based CRN system for 5G.

In a cognitive radio network (CRN), secondary users (SUs) utilize primary users (PUs) licensed spectrum in an opportunistic manner. Spectrum sensing is of the utmost importance in CRN to find and use the available spectrum without harmful interference to the PUs. Conventionally, to implement spectrum utilization, SUs are required to sense the primary spectrum first and then transmit data on the available spectrum. In this paper, we propose a dedicated wireless spectrum sensing network (WSSN), eliminating sensing overhead from SUs with the aim of improving achievable throughput. With WSSN assistance, we eliminate sensing time from the SUs frame, hence increasing the transmission time, which maximizes the achievable throughput. Additionally, the sensing duration is increased by deploying a dedicated WSSN, decreasing the probability of false alarm and achieving a targeted high probability of detection. A low probability of false alarm increases the spectrum utilization, improving the achievable throughput, while a high detection probability ensures PUs protection. Moreover, the proposed technique also addresses hidden and exposed terminal problems along with smooth spectrum mobility. Finally, we provide simulation results to demonstrate the proposed techniques, effectiveness. In the results, we have compared the achievable throughput of the proposed scheme with that of conventional CRN.

The optimal admission control for secondary users (SUs) using blockchain technology in a cognitive radio (CR) network is investigated in this paper. When an SU comes to the system, it will decide to either enter the system, or give up its service and then leave the system based on the current number of PU and SU users in the network. SUs are linked as a block in the waiting area to access the spectrum in an opportunistic way without interfering the operations of PUs. When a PU arrives, if the band is occupied by SU(s), the SU service on the band is interrupted and SU(s) is then placed back to the waiting queue. Admitting an SU would bring a reward to the system provider but holding an SU in the system would also incur an expense to the provider. We concentrate on the optimization problem of when to admit or reject an arriving SU in order to achieve the maximum total discounted expected reward for any initial state. By establishing a discounted Continuous Time Markov Decision Process (CTMDP) model, we verify that the optimal policies for admitting SUs are state-related control limit policies. An extensive numerical analysis is implemented to show how to determine the value of optimal threshold and of the corresponding maximum objective function. Especially, a simulation by using the idea of the neural network and machine learning is implemented to show how to determine the optimal threshold value for any new input parameters through learning process for a given input parameters. The idea and the corresponding result of this paper can be applied in designing optimal cognitive radio networks.

Cognitive radio technology is considered to be an effective solution to improve the spectrum utilization efficiency. The dynamic spectrum allocation algorithm assigns the available spectrum to the secondary users. In this paper, we propose a spectrum allocation algorithm based on the graph-coloring theory. Unlike some already existed allocation algorithm, our algorithm considers the bandwidth matching between secondary users' bandwidth requirements and the available channels. The interference avoidance to the primary users is also taken into account. Our objective is to optimize the total spectrum reward under the premise of fully protecting the primary users. Simulation results prove that this algorithm can improve the total spectrum reward, the satisfaction rate of secondary users, the protection rate of primary users, and the spectrum allocation efficiency.

In this paper, we propose a new multiple-level power allocation strategy for the secondary user (SU) in cognitive radio (CR) networks. Different from the conventional strategies, where SU either stays silent or transmit with a constant/binary power depending on the busy/idle status of the primary user (PU), the proposed strategy allows SU to choose different power levels according to a carefully designed function of the receiving energy. The way of the power level selection at SU side is optimized to maximize the achievable rate of SU under the constraints of average transmit power at SU and average interference temperature to PU. Simulation results demonstrate that the proposed strategy can significantly improve the throughput of SU compared to the conventional strategies.

For efficient wireless communication, the main resource is spectrum. Proper allocation of spectrum is one of the key requirements to effectively utilize the available resources. As the available spectrum is fixed and utilized by proper licensing and control, it is essential to use the spectrum to the extent possible to meet the ever increasing demand of various services including multimedia services that eat up more spectrums. Cognitive radio (CR) is a potential technology to solve spectrum scarcity problem for 5G cellular systems. CR provides flexibility, adaptability and interoperability. As CR users are opportunistic and try to use the licensed spectrum without causing any adverse effect on QoE of the licensed users. CR is the enabling technology, using which dynamic sharing of spectrum is done for unlicensed users to use frequency bands of the licensed users and hence improve the spectrum utilization. In CR, secondary users may require spectrum handoff when licensed users arrives and the channels currently being used by secondary user should be returned to the primary users. This paper, provide a survey of spectrum handoff in cognitive radio network (CRN). We have first introduced the cognitive radio network and concept of spectrum holes. Then this paper discuss about functions of CRN. Also, the concept of Quality of Experience (QoE) in CRN is explained. Subsequently, some recently proposed spectrum handoff algorithms in CRN are reviewed and different approaches to handle spectrum handoff are described. Finally, the paper is concluded with outlining the directions for future research in this area.