Small cells (SCs) based ultra-dense heterogeneous networks (HetNets) are one of the promising solutions for increased coverage and capacity in 5G cellular networks. However, in multi-tiered architecture, co-tier and cross-tier interferences are both performance-limiting factors. Efficient resource allocation techniques can handle interferences effectively, however, their complexity linearly increases with the density of the HetNets resulting from dynamic and unplanned deployment of SCs. Therefore, HetNets can be implemented only through an algorithm that is self-organizing and adaptive to the dynamic conditions. In this research paper, a Q-Learning (QL) based adaptive resource allocation scheme is proposed and evaluated for SC-based ultra-dense HetNets. This QL scheme allocates optimal power to the small cell base station (SBS) to meet the minimum required capacity of macrocell user equipment (MUEs) and the small cell user equipment (SUEs) to provide quality of service (QoS). The proposed QL scheme not only maintains the minimum required capacities of the MUEs and SUEs but has also shown a significant improvement in the capacities of MUEs and SUEs in high interference scenarios as compared to the prior works. In the high co-tier and cross-tier interference scenario, where the state of the art schemes fail to maintain the minimum required capacity of the MUE, the proposed scheme provides a minimum MUE capacity of 2 b/s/Hz, which is twice the minimum required QoS threshold. In a similar way, the proposed solution guarantees QoS up to 16 SCs which are 37.5% more SC than the previously reported works in high interference scenario while maintaining a minimum SUE capacity of 1.5 b/s/Hz, which is 33% higher than the minimum required QoS threshold. By simultaneously mitigating co-tier and cross-tier interferences in ultra-dense HetNets, the proposed solution not only improved the minimum capacities of MUEs and SUEs but also sets a new benchmark for minimum QoS threshold.

In many applications of industrial wireless sensor networks, sensor nodes need to determine their own geographic position coordinates so that the collected data can be ascribed to the location from where it was gathered. We propose a novel intelligent localization algorithm which uses variable range beacon signals generated by varying the transmission power of beacon nodes. The algorithm does not use any additional hardware resources for ranging and estimates position using only radio connectivity by passively listening to the beacon signals. The algorithm is distributed, so each sensor node determines its own position and communication overhead is avoided. As the beacon nodes do not always transmit at maximum power and no transmission power is used by unknown sensor nodes for localization, the proposed algorithm is energy efficient. It also provides control over localization granularity. Simulation results show that the algorithm provides good accuracy under varying radio conditions.

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Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

Heterogeneous networks are an integral part of the 5G cellular networks as they are one of the important enabling technologies for increased coverage and capacity. However, interferences in multi-tiered architecture bottleneck its performance. Although multiple schemes have been proposed for efficient radio resource management to handle the interferences in heterogeneous networks but provision of quality of service to macrocell and small cell user equipment simultaneously, is still an open research problem. Intelligent schemes for radio resource management in heterogeneous networks have proved their effectiveness due to their self-optimization capabilities. In this research article, a cooperative Q-Learning, algorithm is proposed for efficient joint radio resource management in ultra-dense heterogeneous networks to handle interferences by adaptive power allocation to small cell base stations while considering the minimum quality of service requirements. In this proposed cooperative Q-Learning algorithm, small cell base stations interacts with the neighboring small cell base stations to exchange information and performs self-optimization based on a joint reward function. The proposed solution not only provided significant improvement in the capacity of macrocell and small cell user equipment as compared to other state of art Q-Learning based radio resource management schemes but also ensure the provision of quality of service to all macrocell and small cell user equipment simultaneously in the cluster of 16 small cells. The proposed solution provided a minimum capacity of 2 b/s/Hz to macrocell and small cell user equipment which is 100% higher than the minimum quality of service requirements defined in literature where none of recently proposed solution could meet minimum quality of service requirements. The results analysis shows that cooperation among the small cells yields a significant improvement of 48% in capacity of small cell user equipment at the cost of a slight increase in computational time as compared to independent learning.