The growing traffic and global bandwidth shortage for broadband cellular communi-cation networks has motivated to explore the underutilized millimeter wave frequencyspectrum for future communications. Fifth generation (5G) is the key to empow-ering new services and use cases for people, businesses, and society at large. Withunprecedented speed and flexibility, 5G carries more data with greater reliability andresponsiveness than ever before. As 5G new radio (NR) begins to take full advantageof the high-band spectrum, i.e, the millimeter wave frequencies, new challenges arecreated. While millimeter waves offer broader bandwidth and high spatial resolution,the drawback is that the millimeter waves experience higher attenuation due to pathloss and are more prone to absorption, interference and weather conditions, thereforelimiting cell coverage.This thesis is an attempt to increase the 5G cell coverage by implementing ananalogue beamforming router in a cell. Beamforming router acts like a relay, whichextends the range of the 5G cell whenever needed, according to the position of theUser Equipment (UE) based on the information received from the gNodeB (gNB,logical 5G radio node). This thesis is investigating the downlink Signal-to-Noise Ratio(SNR) gain and thus possible increase in the data rate. Simulation and validation ofthe overall performance is done using MATLAB. The outcome of this study may beused to increase the 5G cell coverage if it is implemented in a real.

Signal Correlation is a digital signal processing technique which has a wide variety of applications, ranging from geophysical exploration to acoustic signal enhancements, or beamforming. This dissertation will consider this technique in an underwater acoustics perspective, but the algorithms illustrated here can be readily applied to other areas. Although beamforming techniques have been studied for the past fifty years, modern beamforming systems still have difficulty in operating in noisy environments, especially in shallow water.

Communications over millimeter wave (mmWave) frequencies is a key component of the fifth generation (5G) cellular networks due to the large bandwidth available at mmWave bands. Thanks to the short wavelength of mmWave bands, large antenna arrays (32 to 256 elements are common) can be mounted at the transceivers. The array sizes are typical of a massive MIMO communication system, which makes fully digital beamforming difficult to implement due to high power consumption and hardware cost. This motivates the development of hybrid beamforming due to its versatile tradeoff between implementation cost (including hardware cost and power consumption) and system performance. However, due to the non-convex constraints on hardware (phase shifters), finding the global optima for hybrid beamforming design is often intractable. In this thesis, we focus on hybrid beamforming design for mmWave cellular communications both narrowband and wideband scenarios are considered. Starting from narrowband SU-MIMO mmWave communications, we propose a Gram-Schmidt orthogonalization (GSO) aided hybrid precoding algorithm to reduce computation complexity. GSO is a recursive process that depends on the order in which the matrix columns are selected. A heuristic solution to the order of column selection is suggested according to the array response vector along which the full digital precoder has the maximum projection. The proposed algorithm, not only constrained to uniform linear arrays (ULAs), can avoid the matrix inversion in designing the digital precoder compared to the orthogonal matching pursuit (OMP) algorithm. For the narrowband MU-MIMO mmWave communications, we propose an interference cancellation (IC) framework on hybrid beamforming design for downlink mmWave multi-user massive MIMO system. Based on the proposed framework, three successive interference cancellation (SIC) aided hybrid beamforming algorithms are proposed to deal with inter-user and intra-user interference. Furthermore, the optimal detection order of data streams is derived according to the post-detection signal-to-interference- plus-noise ratio (SINR). When considering wideband MU-MIMO mmWave communications, how to design a common RF beamformer across all subcarriers becomes the main challenge. Furthermore, the common RF beamformer in wideband channels leads to the need of more effective baseband schemes. By adopting a relaxation of the original mutual information and spectral efficiency maximization problems at the transceiver, we design the radio frequency (RF) precoder and combiner by leveraging the average of the covariance matrices of frequency domain channels, then a SIC aided baseband precoder and combiner are proposed to eliminate inter-user and intra-user interference / Graduate

By endowing the base station with hundreds of antennas and relying on spatial multiplexing, massive multiple-input-multiple-output (MIMO) allows impressive advantages in many fronts. To reduce this promising technology to reality, thorough performance analysis has to be conducted. Along this line, this work is focused on the convenient high-dimensionality of massive MIMO’s corresponding model. Indeed, the large number of antennas allows us to harness asymptotic results from Random Matrix Theory to provide accurate approximations of the main performance metrics. The derivations yield simple closed-form expressions that can be easily interpreted and manipulated in contrast to their alternative random equivalents. Accordingly, in this dissertation, we investigate and optimize the performance of massive MIMO in different contexts. First, we explore the spectral efficiency of massive MIMO in large-scale multi-tier heterogeneous networks that aim at network densification. This latter is epitomized by the joint implementation of massive MIMO and small cells to reap their benefits. Our interest is on the design of coordinated beamforming that mitigates cross-tier interference. Thus, we propose a regularized SLNR-based precoding in which the regularization factor is used to allow better resilience to channel estimation errors. Second, we move to studying massive MIMO under Line-of-Sight (LoS) propagation conditions. To this end, we carry out an analysis of the uplink (UL) of a massive MIMO system with per-user channel correlation and Rician factor. We start by analyzing conventional processing schemes such as LMMSE and MRC under training-based imperfect-channel-estimates, and then, propose a statistical combining technique that is more suitable in LoS-prevailing environments. Finally, we look into the interplay between LoS and the fundamental limitation of massive MIMO systems, namely, pilot contamination. We propose to analyze and compare the performance using single-cell and multi-cell detection methods. In this regard, the single-cell schemes are shown to produce higher SEs as the LoS strengthens, yet remain hindered by LoS-induced interference and pilot contamination. In contrast, for multi-cell combining, we analytically demonstrate that M-MMSE outperforms both single-cell detectors by generating a capacity that scales linearly with the number of antennas, and is further enhanced with LoS.

In this modern communication era, we are surrounded by unlimited electronic devices, the need to connect with everyone and everything increases dramatically. As the number of electronic devices increases, the amount of data to process increases and the need for higher data speeds occurs. After 1G and 2G, LTE (Long Term Evolution, 3G) come with the improvements in technology which allows reaching those new high data rates. Next comes the upgraded version of LTE called LTE-Advanced, which was launched to boost the speeds further. In this thesis, a 4G LTE environment has been created using (Graphic User Interface) in MATLAB. Many characteristics and parameters can be tuned such as type of modulation, number of UEs, type of channel, channel scenario, and some others to know how the system behaves and varied results outcome. Focus on the presence of a line of sight between the receiver and the transmitter helps to distinguish the Rayleigh and Rician scenario. In this thesis simulations on different channel models are simulated and various beamforming algorithms are tested to estimate that line of sight component (K-factor) and Error vector magnitude. The main aim of the thesis is to understand the communication channel behaviour in Static (Line of sight between transmitter and receiver) condition and High-Speed Train Condition along with EPA, EVA, ETU. The other aim of this report is to use the channel knowledge comprises of signal to noise ratio (SNR), bit error rate (BER) and error vector magnitude (EVM) helps to reduce the number of computations required while performing beamforming by varying the beam weight resolution.

No description available.

Satellite communication (SatCom) is an essential component of next-generation wireless communications. The existing terrestrial network will be overwhelmed due to the rapid growth of demand for data and serving remote areas by using only terrestrial networks is demanding. In addition, terrestrial communications are susceptible to natural disasters such as earthquakes and floods. In order to overcome these disadvantages of the terrestrial communication systems, SatCom systems are being deployed and covering remote or sparsely populated areas. However, research on SatCom is still not enough and it has not been studied as much as on terrestrial communication. In this thesis, we investigate and analyze system models using SatCom. Furthermore, we present to improve the performance of SatCom by applying the proposed techniques in terrestrial communications. First of all, we analyze the outage probability and symbol error rate of the SatCom system. In single-beam and multi-beam situations, all factors that can be considered in the SatCom channel model are considered. Since the beam coverage of the satellite is broad, a number of users are randomly distributed within the beam. We investigate the performance of the SatCom system according to the user selection methods considering the user's location as well. Secondly, a system in which multiple gateways transmit signals over multiple beams is taken into account. Many users are placed in each beam, and inter-beam interference exists due to full-frequency reuse. To simultaneously cover the multiple users in the beam, signals are transmitted using non-orthogonal multiple access. In order to maximize system throughput, precoding for interference mitigation and NOMA optimization techniques are proposed. Finally, a cognitive radio network in which high-altitude platforms (HAPs) are applied to conventional SatCom systems and share frequency bands with SatCom is taken into consideration. HAPs are used to cover user-dense areas within satellite beam coverage. In order to properly handle interference caused by spectrum sharing and improve the system data rate, precoding and frequency band allocation schemes are proposed.

This work introduces new binaural beamforming algorithms for hearing aids, with a robustness to errors in the estimate of the target speaker direction of arrival (DOA) and a good trade-off between noise reduction and preservation of the noise/interferers spatial impression. Three robust designs are proposed, and their robustness is confirmed by simulation results. These robust designs are a combination of binaural and monaural beamformers using two different microphone configurations: one for low frequency components and one for high frequency components. The robust designs are also found to be robust to mismatch between the anechoic propagation models used for the beamformers designs and the reverberant propagation models used to generate the signals at the microphones in the simulations. To preserve the binaural cues of the noise/interferers in the binaural beamformer outputs, a method based on a mixing/selection of different available binaural signals is proposed, using a classification from the phase and magnitude of a complex coherence function. This method is added as a post processor to the beamforming designs robust to target DOA mismatch. Simulation results show that the resulting mixed binaural output signals have a good binaural cues preservation level that outperform the benchmark design, with significant noise reduction and low target distortion. Since knowledge of source DOAs is important for beamforming noise reduction, a beamformer-based broadband multi-source DOA detection system is also developed in the thesis, using information from different frequencies or sub‐bands to obtain global estimates of sources DOAs. Simulation results shows that using one beamformer on each side is capable of detecting the DOAs of active sources under several acoustic scenarios, including scenarios with one, two, or three sources, and with or without the presence of some level of diffuse noise.

Sound generation and radiation properties are studied of full-scale tactical jet engine noise. This is motivated by the high sound exposure levels from jet noise, particularly for tactical engines. Acoustic source reconstruction methods are implemented computationally on existing jet noise data. A comparative study is performed using numerical simulations to understand the capabilities of more advanced beamforming methods to successfully estimate the source properties of a distributed, partially correlated source distribution. The properties and limitations of each beamforming method are described. Having validated the methods, beamforming with regularization—via the Hybrid Method—is implemented on linear array measurements near an installed tactical engine. A detailed analysis of the correlation and coherence properties associated with the phased array measurements guides the implementation of the beamforming. When the measurements are used as inputs to the beamforming, they produce partially correlated, distributed sources in a full-order model representation. A processing technique is also implemented that increases the usable bandwidth of the array measurements to almost an order of magnitude above the array design frequency. To more appropriately study the equivalent sources, a decomposition technique is designed and implemented to create a reduced-order wavepacket model of the jet noise. The wavepacket model is modular and scalable to allow for the efficient characterization of similar jet noise measurements. It is also appropriate for its physical significance, as wavepackets are attributed to the turbulent flow as well as the hydrodynamic and acoustic properties of the radiation. The reduced order model can estimate the levels and coherence properties of the acoustic radiation and represents a significant step towards a complete jet noise prediction model.

Sound generation and radiation properties are studied of full-scale tactical jet engine noise. This is motivated by the high sound exposure levels from jet noise, particularly for tactical engines. Acoustic source reconstruction methods are implemented computationally on existing jet noise data. A comparative study is performed using numerical simulations to understand the capabilities of more advanced beamforming methods to successfully estimate the source properties of a distributed, partially correlated source distribution. The properties and limitations of each beamforming method are described. Having validated the methods, beamforming with regularization”via the Hybrid Method”is implemented on linear array measurements near an installed tactical engine. A detailed analysis of the correlation and coherence properties associated with the phased array measurements guides the implementation of the beamforming. When the measurements are used as inputs to the beamforming, they produce partially correlated, distributed sources in a full-order model representation. A processing technique is also implemented that increases the usable bandwidth of the array measurements to almost an order of magnitude above the array design frequency. To more appropriately study the equivalent sources, a decomposition technique is designed and implemented to create a reduced-order wavepacket model of the jet noise. The wavepacket model is modular and scalable to allow for the efficient characterization of similar jet noise measurements. It is also appropriate for its physical significance, as wavepackets are attributed to the turbulent flow as well as the hydrodynamic and acoustic properties of the radiation. The reduced order model can estimate the levels and coherence properties of the acoustic radiation and represents a significant step towards a complete jet noise prediction model.