Downlink Achievable Rate Analysis for FDD Massive MIMO Systems

Multiple-Input Multiple-Output (MIMO) systems with large-scale transmit antenna arrays, often called massive MIMO, are a very promising direction for 5G due to their ability to increase capacity and enhance both spectrum and energy efficiency. To get the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential for downlink beamforming and resource allocation. Conventional approaches to obtain CSIT for FDD massive MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this dissertation, we improve the performance of FDD massive MIMO networks in terms of downlink training overhead reduction, by designing an efficient downlink beamforming method and developing a new algorithm to estimate the channel state information based on compressive sensing techniques. First, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink direction of arrivals (DoAs) and downlink direction of departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming. Second, By exploiting the sparsity structure of downlink channel matrix, we develop an algorithm that selects the best features from the measurement matrix to obtain efficient CSIT acquisition that can reduce the downlink training overhead compared with conventional LS/MMSE estimators. In both cases, we compare the performance of our proposed beamforming method with traditional methods in terms of downlink achievable rate and simulation results show that our proposed method outperform the traditional beamforming methods

Modulation and performance of synchronous demodulation for speech signal detection and dialect intelligibility

Speech processing is one of the fundamental operations in computer science and it is particularly difficult to process and distinguish speech in different Arabic dialects when background noise is present. In any nation, communication skills are crucial. Pushing a button is all it takes for the typical person to make phone calls and leave voicemails but for telecommunications experts, the process is very different. We understand how communication actually works. The terms detection and demodulation are commonly used when addressing the full demodulation process. The procedures and circuits are substantially the same under both designations. As the name implies, demodulation is the opposite of modulation, which is applying a signal, such as an audio signal, to a carrier. The demodulation process isolates the output signal from the audio or other signal that was transmitted using amplitude shifts on the carrier. In this study, a system for distinguishing speech signals was developed using modulation and demodulation to transmit speech by extracting it from a variety of factors, the most significant of which is background noise in addition to a wide variety of dialects, which poses a significant challenge in speech processing. The proposed system was applied to a dataset that was created for a group of voices in different dialects, and by using important techniques, the noise accompanying the voices was deleted and then the voices were processed with other techniques such as modulation and demodulation to distinguish the dialect. The system has proven effective by distinguishing dialects