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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.

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.

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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.