With limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel allocation becomes increasingly important. However, finding a conflict-free channel allocation with minimum interference and minimum channel span is NP hard. Genetic algorithm (GA) is one of the heuristic optimization tools that can be used to solve this problem efficiently. In this paper we investigate fixed and dynamic channel allocation problem for cellular networks and its optimum solution using GA.

In multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, multi-user detection (MUD) algorithms play an important role in reducing the effect of multi-access interference (MAI). A combination of the estimation of channel and multi-user detection is proposed for eliminating various interferences and reduce the bit error rate (BER). First, a novel sparse based k-nearest neighbor classifier is proposed to estimate the unknown activity factor at a high data rate. The active users are continuously detected and their data are decoded at the base station (BS) receiver. The activity detection considers both the pilot and data symbols. Second, an optimal pilot allocation method is suggested to select the minimum mutual coherence in the measurement matrix for optimal pilot placement. The suggested algorithm for designing pilot patterns significantly improves the results in terms of mean square error (MSE), symbol error rate (SER) and bit error rate for channel detection. An optimal pilot placement reduces the computational complexity and maximizes the accuracy of the system. The performance of the channel estimation (CE) and MUD for the proposed scheme was good as it provided significant results, which were validated through simulations.Technologies 2018, 6, 72 2 of 16 low), the transmitting signal vector has a sparse property due to a large number of non-zero elements. Therefore, the decoding of the transmitted signal becomes a CS problem [7]. The long-term evolution is appropriate for a system that provides a small number of high activity of users. However, this shifts for machine-type communication (MTC) where a higher number of users with fewer activity sporadically sends a small number of packets [8].Recently, researchers have focused more on OFDM systems compared to the existing air-interface techniques due to its low complexity. In OFDM systems, the subcarriers are sent through multiple channels, which permits ease of equalization in the case of low complexity during the implementation.Spyridon et al.[9] considered various types of noises, such as Additive white Gaussian noise (AWGN), phase noise (PN), Rayleigh fading, Rician fading and Doppler shift with the turbo coding technique. The simulation platform consisted of three modules (transmitter, channel and receiver). In the transmitter module, turbo coding is performed, which makes the system more immune to the effects of noise with excellent BER results. The channel model is constituted by multipath fading, Doppler shift, AWGN and PN.In reference [10] the simulation is carried under various noise types, such as complex Rayleigh fading, complex Rician noise, AWGN and phase noise.Spyridon et al.[11] split an information stream into multiple frequency carriers, which joins OFDM in the simulation platform with turbo codes to find a better turbo scheme compared to a typical parallel concatenated convolutional codes and serial concatenated convolutional codes (PCCCs and SCCCs) are a class of Forward error correction codes suitabl...

Information technology plays an important role in facilitating disaster management and allowing planners for a more efficient disaster handling. Climate change can reasonably be expected to increase countries' vulnerability to natural hazards in future. We are already witnesses of extreme meteorological phenomena, such as expanded fires and floods. This paper gives Mobile Ad-Hoc Network (MANET) along with Dynamic Source Routing protocol (DSR). Simulation results for performance measurement of DSR algorithm for normal condition are given first. Same parameters are measured after applying disaster condition on nodes is presented in next section. In last section simulation results of disaster prevention condition are given. It is observed that performance of the network after application of prevention condition is nearly same as the normal performance. The performance is evaluated in terms of Network Throughput, Packet Delivery Ratio, and Average end to end delay Keywords-MANET, DSR Algorithm, throughout, PDR, end to end delay

The main challenge of a wireless sensor network (WSN) in disaster situations is to discover efficient routing, to improve quality of service (QoS) and to reduce energy consumption. Location awareness of nodes is also useful or even necessary. Without knowing the position of sensor nodes, collected data is insignificant. Ant colony optimization (ACO) is a unique form of optimization method, which is highly suitable for adaptive routing and guaranteed packet delivery. The primary drawbacks of ACO are data flooding, huge overhead of control messages and long convergence time. These drawbacks are overcome by considering location information of sensor nodes. An event-based clustering localized energy efficient ant colony optimization (EBC_LEE-ACO) algorithm is proposed to enhance the performance of WSN. The main focus of the proposed algorithm is to improve QoS and minimize the network energy consumption by cluster formation and selecting the optimal path based on the biological inspired routing-ACO and location information of nodes. In clustering, data is aggregated and sent to the sink (base station) through cluster head (CH) which reduces overheads. EBC_LEE-ACO is a scalable and energy efficient reactive routing algorithm which improves QoS, lifetime and minimizes energy consummation of WSN as compared to other routing algorithms like AODV, ACO, ACO using RSSI. The proposed algorithm reduces energy consumption by approximately 7%, in addition to improvement in throughput, packet delivery ratio and increase in packet drop which has been observed in comparison with other algorithms, i.e. autonomous localization based eligible energetic Path_with_Ant Colony optimization (ALEEP with ACO) of the network. Use of IEEE 802.11 standard in proposed work increased packet drop.

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