Saleem Aslam

Fuzzy Logic Based Spectrum Sensing for Cognitive Radio Networks

It is inevitable for cognitive radio to find unutilized portion of the spectrum more accurately for successful deployment of dynamic spectrum sensing. To achieve highly reliable spectrum sensing, usually cooperative spectrum sensing is employed but still there is a margin to improve local sensing decisions. Cooperative spectrum sensing improves reliability of sensing at the cost of cooperation overhead among cognitive radio users, which can be reduced by improving local spectrum sensing. Several signal processing techniques for primary user detection have been proposed in literature but still there is room for researchers in this field to explore more sophisticated approaches to enhance sensing efficiency. This paper proposes a two stage local spectrum sensing approach. In first step, each cognitive radio perform existing spectrum sensing techniques i.e. energy detection, matched filter detection and cyclostationary detection. While in second stage, the output from each technique employed in step 1 is combined using fuzzy logic to ultimately decide about the presence or absence of primary user. The proposed approach shows significant improvement in sensing accuracy by exhibiting higher probability of detection and low false alarms.

CSPA: Channel Selection and Parameter Adaptation scheme based on genetic algorithm for cognitive radio Ad Hoc networks

The cognitive radio (CR) is evolved as the potential technology to solve the problem of spectrum scarcity and to meet the stringent requirements of upcoming wireless services. CR has two distinct features, the spectrum sensing and the parameter adaptation. The former feature helps the CR to find the vacant spectrum slots/channels in the radio band while the latter mechanism allows it to adjust the operating parameters (e.g. frequency band, modulation and power, etc.) accordingly. The primary user (PU) activity has serious effects on the overall performance of the cognitive radio network (CRN).The CR should vacate the channel if it detects the arrival of the primary user (PU) in order to avoid the interference. The channel eviction/switching phenomenon severely degrade the quality of service (QoS) of the CR user and it is perhaps the key challenge for the CRN. In this paper, we propose the dynamic channel selection and parameter adaptation (CSPA) scheme based on the genetic algorithm to provide better QoS for the CR by selecting a best channel in terms of the quality, the power and the PU activity. The CSPA deals with the problem of channel switchings and it provides better QoS to the CR user. Simulation results prove that CSPA outperforms the existing schemes in terms of channel switchings, average service time, power and throughput.

A Decentralized Heuristic Approach towards Resource Allocation in Femtocell Networks

Femtocells represent a novel configuration for existing cellular communication, contributing towards the improvement of coverage and throughput. The dense deployment of these femtocells causes significant femto-macro and femto-femto interference, consequently deteriorating the throughput of femtocells. In this study, we compare two heuristic approaches, i.e., particle swarm optimization (PSO) and genetic algorithm (GA), for joint power assignment and resource allocation, within the context of the femtocell environment. The supposition made in this joint optimization is that the discrete power levels are available for the assignment. Furthermore, we have employed two variants of each PSO and GA: inertia weight and constriction factor model for PSO, and twopoint and uniform crossover for GA. The two proposed algorithms are in a decentralized manner, with no involvement of any centralized entity. The comparison is carried out between the two proposed algorithms for the aforementioned joint optimization problem. The contrast includes the performance metrics: including average objective function, min–max throughput of the femtocells, average throughput of the femto users, outage rate and time complexity. The results demonstrate that the decentralized PSO constriction factor outperforms the others in terms of the aforementioned performance metrics.

Spectrum sharing optimization with QoS guarantee in cognitive radio networks

In this paper, we propose an optimized spectrum sharing scheme based on the Hungarian Algorithm to guarantee the quality of service (QoS) for individual cognitive radio (CR) users belonging to different CR cells. The proposed scheme is most favorable for overlapping cells where the users are demanding channels for heterogeneous applications such as chatting, web browsing, or voice and video streaming. The spectrum sharing optimization with the QoS guarantee (SSO-QG) is an optimal scheme that can operate in throughput enhancement mode, high reliability mode and collision avoidance mode based on the weight assigned to the corresponding QoS parameters in accordance with the demands of different applications. Simulation results show that the proposed scheme outperforms the existing schemes in terms of forming the optimal sharing pattern and meeting the stringent QoS requirements fairly enough according to the demands of the cell. Moreover, it reduces collisions with primary users.

A docitive Q-learning approach towards joint resource allocation and power control in self-organised femtocell networks

Femtocell is a technology that contributes towards the escalation of coverage as well as throughput. By virtue of uncertain deployment of femtocells, self-organisation is a viable solution for resource allocation. In this study, we are projecting a docitive Q-learning DQL paradigm for joint resource allocation and power control JRAPC. Moreover, the proposed learning paradigm is compared with independent Q-learning for the same JRAPC problem. In the proposed DQL paradigm, femto base stations, which are agents, learn the strategies by exploiting Q-learning and share their learned strategies with their neighbours. Concerning the shared channel environment, the problem function is formulated as the maximisation of femtocell capacity while maintaining the quality of service requirement of the macrousers. The impact of the proposed DQL paradigm is investigated on system capacity and femtocell capacity. Furthermore, comparison is carried out with the considered independent learning paradigm in terms of convergence, min-max capacity and the effect of femtocell density. Also, the fairness index is computed to have further insight. The results illustrate that DQL-based JRAPC outperforms its counterpart. Copyright

Distributed joint resource and power allocation in self-organized femtocell networks

Femtocells, which are widely deployed within a macrocell, are considered to be a novel technology that leads to the escalation of indoor coverage and capacity. However, due to lack of coordination between the femtocell and the macrocell, designing a distributive resource and power allocation is a challenging task. In this study, a potential game (PG)-theoretic approach is proposed for joint resource and power allocation (JRPA), which is demonstrated to exhibit unique Nash Equilibrium. Specifically, femto-base stations, which are considered as the players of the PG, learn the strategies in terms of resource and power allocation by taking into account the interest of other entities. To this end, the utility function of players is designed such that it minimizes the impact interference and the satisfaction for improving the femtocell capacity, without jeopardizing the macrocell performance. Precisely, the utility function incorporates all the sources of interference such as co-tier and cross-tier, and also the reward of each player in terms of capacity. The proposed PG-based JRPA is solved by employing the better response dynamics, which selects the resources and the power levels by utilizing a particle swarm optimization-constriction factor model. The performance of PG-based JRPA is analyzed in regard to average femtocell capacity and system capacity. Additionally, two different traffic cases are considered: high load traffic and low load traffic. For the sake of comparison, random allocation is employed. Simulation results are carried out in terms of the performance metrics, which includes convergence, min-max capacity, varying resource blocks, femtocell density and fairness. The results illustrate the superior performance of the proposed PG in terms of the aforementioned performance metrics. Graphical abstractDisplay Omitted HighlightsSelf-organized interference management for femtocell networks is investigated.The potential game is employed for distributed joint resource and power allocation.The utility and potential function is designed by considering other players.The game is solved by using better response dynamics.Performance is evaluated in terms of average femtocell and system capacity.

Fair, efficient, and power-optimized spectrum sharing scheme for cognitive radio networks

The cognitive radio network (CRN) is a promising solution to the problem of spectrum scarcity. To achieve efficient spectrum utilization, cognitive radio requires a robust spectrum sensing and spectrum sharing scheme. Therefore, spectrum sharing scheme plays a key role in achieving the optimal utilization of the available spectrum. The spectrum sharing in CRN is more challenging than traditional wireless network. The main factors besides throughput and fairness which need to be addressed in spectrum sharing of CRN are primary user (PU) activity, transmission power, and variations in the radio environment. In this article, we propose fair, efficient, and power-optimized (FEPO) spectrum sharing scheme that will incorporate all critical factors mentioned above to maximize the spectrum utilization. Simulation results show that FEPO scheme outperforms in terms of transmission power by reducing the number of retransmissions and guarantees required level of throughput and fairness. Moreover, periodic monitoring helps to reduce the number of collisions with PUs.

Joint Sensor-Node Selection and Channel Allocation Scheme for Cognitive Radio Sensor Networks

The conventional wireless sensor networks (WSNs) operating on the license-free ISM band are now experiencing large interference from other ISM band based wireless devices. The cognitive radio sensor network (CRSN) can solve this issue of interference and it can provide better services within the domain of the WSNs. In this paper, we propose a novel joint sensor node selection and channel allocation scheme to improve the performance of the CRSNs. We consider the cluster oriented sensor network and form clusters using the K-means clustering algorithm. The node selection scheme is formulated using the knapsack problem where each cluster head selects the optimal number of sensor nodes (SNs). Later on, the Hungarian algorithm is employed to allocate the best channels among SNs for their reporting process. Simulation results show that our scheme outperforms the existing schemes in terms of prolonging the network life, selecting the more reliable SNs and allocating the optimal channels among SNs for their data transmission during the reporting process.

CSIT: channel state and idle time predictor using a neural network for cognitive LTE-Advanced network

Cognitive radio (CR) is a novel methodology that facilitates unlicensed users to share a licensed spectrum without interfering with licensed users. This intriguing approach is exploited in the Long Term Evolution-Advanced (LTE-A) network for performance improvement. Although LTE-A is the foremost mobile communication standard, future underutilization of the spectrum needs to be addressed. Therefore, dynamic spectrum access is explored in this study. The performance of CR in LTE-A can significantly be enhanced by employing predictive modeling. The neural network-based channel state and idle time (CSIT) predictor is proposed in this article as a learning scheme for CR in LTE-A. This predictive-based learning is helpful in two ways: sensing only those channels that are predicted to be idle and selecting the channels for CR transmission that have the largest predicted idle time. The performance gains by exploiting CSIT prediction in CR LTE-A network are evaluated in terms of spectrum utilization, sensing energy, channel switching rate, packet loss ratio, and average instantaneous throughput. The results illustrate that significant performance is achieved by employing CSIT prediction in LTE-A network.

Optimized Energy Harvesting, Cluster-Head Selection and Channel Allocation for IoTs in Smart Cities

This paper highlights three critical aspects of the internet of things (IoTs), namely (1) energy efficiency, (2) energy balancing and (3) quality of service (QoS) and presents three novel schemes for addressing these aspects. For energy efficiency, a novel radio frequency (RF) energy-harvesting scheme is presented in which each IoT device is associated with the best possible RF source in order to maximize the overall energy that the IoT devices harvest. For energy balancing, the IoT devices in close proximity are clustered together and then an IoT device with the highest residual energy is selected as a cluster head (CH) on a rotational basis. Once the CH is selected, it assigns channels to the IoT devices to report their data using a novel integer linear program (ILP)-based channel allocation scheme by satisfying their desired QoS. To evaluate the presented schemes, exhaustive simulations are carried out by varying different parameters, including the number of IoT devices, the number of harvesting sources, the distance between RF sources and IoT devices and the primary user (PU) activity of different channels. The simulation results demonstrate that our proposed schemes perform better than the existing ones.