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Unmanned aerial vehicles (UAVs) are a quintessential example of automation in the field of avionics. UAVs provide a platform for performing a wide variety of tasks, but in each case the concept of path planning plays an integral role. It helps to generate a pathway free of obstacles, having minimum length leading to lesser fuel consumption, lesser traversal time and helps in steering the aircraft and its corresponding antenna power signature safely around the hostile antenna to avoid detection. To optimize path planning to incorporate all the above-mentioned constraints, this paper presents two new hybrid algorithms particle swarm optimization (PSO) with harmony search algorithm and PSO with genetic algorithm. The hybrid algorithms perform both an exploratory and exploitative search, unlike the existing algorithms which are biased, towards either an exploitative search or an exploratory search. Furthermore, the hybrid algorithms are compared to the existing optimization algorithms and in all cases the hybrid algorithms give a minimum of 7% better result against PSO with up to a 40% better result against Invasive Weed optimization algorithm for a fixed computational time, suggesting better real-time applications.

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The need for faster wireless connectivity is increasing rapidly in all the sectors of the technologies. Whether it is a patient monitoring system, military application, entertainment services, streaming services, or global stock markets, there is a tremendous increase in the need for enhanced wireless telecommunication services. The wireless telecommunication consumers rely on bulk data, and massive growth in the number of users has resulted in the spectrum congestion. To avoid such spectrum congestion and to satisfy the data hunger of the wireless telecommunication users, the possible solution is Cognitive Radio Network (CRN). A CRN, therefore, plays a significant role in the field of wireless communication, and an efficient spectrum sensing enhances the effectiveness of the CRN. In this paper, complete research carried out so far in the field of spectrum sensing for CRN is discussed. Different soft computing techniques (GA, PSO, ABC, ACO, FFA, FSS, Cuckoo Search, ANN, FIS, GFIS) are surveyed in this paper, along with a detailed comparative analysis between conventional and soft computing techniques for spectrum sensing. In addition to that, the challenges faced in the implementation of CRN and its requirements is also addressed. Different spectrum sensing elements and requirements are presented and road map of spectrum sensing with soft computing techniques towards 5G is discussed. Furthermore, the paper also suggests the future prospects, research challenges and open issues associated with soft computing techniques for spectrum sensing in CRN.

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Satellite communication systems can provide seamless wireless coverage directly or through complementary groundterrestrial components and are projected to be incorporated into future wireless networks, particularly 5G and beyond networks. Increased capacity and flexibility in telecom satellite payloads based on classic radio frequency technology have traditionally translated into increased power consumption and dissipation. Much of the analog hardware in a satellite communications payload can be replaced with highly integrated digital components that are often smaller, lighter, and less expensive, as well as software reprogrammable. Digital beamforming of thousands of beams simultaneously is not practical due to the limited power available onboard satellite processors. Reduced digital beamforming power consumption would enable the deployment of a full digital payload, resulting in comprehensive user applications. Beamforming can be implemented using matrix multiplication, hybrid methodology, or a discrete Fourier transform (DFT). Implementing DFT via fast Fourier transform (FFT) reduces the power consumption, process time, hardware requirements, and chip area. Therefore, in this paper, area-power efficient FFT architectures for digital beamforming are analyzed. The area in terms of look up tables (LUTs) is estimated and compared among conventional FFT, fully unrolled FFT, and a 4-bit quantized twiddle factor (TF) FFT. Further, for the typical satellite scenarios, area, and power estimation are reported.

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This paper jointly designs linear precoding (LP) and codebook-based beamforming implemented in a satellite with massive multiple-input multiple-output (mMIMO) antenna technology. The codebook of beamforming weights is built using the columns of the discrete Fourier transform (DFT) matrix, and the resulting joint design maximizes the achievable throughput under limited transmission power. The corresponding optimization problem is first formulated as a mixed integer non-linear programming (MINP). To adequately address this challenging problem, an efficient LP and DFT-based beamforming algorithm are developed by utilizing several optimization tools, such as the weighted minimum mean square error transformation, duality method, and Hungarian algorithm. In addition, a greedy algorithm is proposed for benchmarking. A complexity analysis of these solutions is provided along with a comprehensive set of Monte Carlo simulations demonstrating the efficiency of our proposed algorithms.