الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
The last years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a high throughput to support applications such as voice, real-time video, movies, and games. Demands for wireless throughput and the number of wireless devices will always increase. In addition, there is a growing concern about energy consumption of wireless communication systems. Thus, future wireless systems have to satisfy three main requirements: (i) having a high throughput; (ii) simultaneously serving many users; and iii) having less energy consumption. Massive multiple-input multiple-output (MIMO) technology, where a base station (BS) equipped with very large number of antennas (collocated or distributed) serves many users in the same time-frequency resource, can meet the above requirements, and hence, it is a promising candidate technology for next generations of wireless systems. A huge throughput and energy efficiency can be achieved owing to the multiplexing gain and the array gain. It can utilize one power hungry radio frequency (RF) with each antenna. Owing to high power consumption of RF chain components, the practical implementation of massive MIMO is considered a challenging problem. This thesis presents high power consumption problem from two different aspects.
The first aspect depends on reducing the number of RF chains and divides into two parts. The first part, hybrid precoding consists of mixed of analog and digital processing which is based on minimum mean square error (MMSE) algorithm. Extensive simulation programs are executed to compare the performance of the proposed MMSE based hybrid precoding
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scheme with analog beamforming performance, block diagonalization (BD) based hybrid precoding and zero forcing (ZF) based hybrid precoding. Moreover, studies on the impact of modified MMSE based hybrid beamforming parameters on the system performance are done. These parameters such as; the number of BS antennas, the number of mobile station antennas, number of effective channel quantization bits and number of RF beamforming quantization bits. Besides, the impact of rate threshold on the coverage probability is studied under different values of NBS antennas.
The second part, spatial modulation can utilize multiple transmit antennas (TAs) for every user with one RF chain and hundreds of antennas at BS with small number of RF chain. Moreover, it activates part of available antenna element to send information in spatial domain in addition to information bits that sent through classical modulation symbols (e.g. 16QAM). This technique combines with hybrid precoding under name hybrid beamforming spatial modulation (HBFSM) to provide beamforming gain, multiplexing gain, low cost and low complexity. The MATLAB simulation programs are executed to study the performance of the proposed HBFSM over the conventional SM systems and hybrid precoding in terms of the bit error rate (BER) probability. The second aspect depends on reducing power consumption and hardware cost. Where, each antenna in massive MIMO systems is connected to high resolution analog to digital converters (ADC) in the RF chain. This sets a heavy load on both power consumption and hardware cost particularly with large signal bandwidth. Additionally, the large request for the signal processing with high precision ADCs will increase circuit power consumption and hardware cost in practical massive MIMO relaying systems. Double resolution technique is introduced to reduce
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power consumption and hardware cost. In this thesis, two forms for double resolution are introduced double resolution ADCs at BS receivers as one quantized case and double resolution ADCs/DACs as double quantized case. Massive MIMO is an innovative technology that helps in the achievement of higher system throughput and reliable transmission for 5G and beyond wireless networks. This wireless networks such as wireless sensor network (WSN) can utilize in nuclear reactors and nuclear power plants to monitor radiation level, specially, in hot areas, besides the other required variables. Moreover, it can be employed in high-temperature gas-cooled reactors (HTGR) to achieve intelligent monitoring. Moreover, there are another nuclear application for massive MIMO.