http://ir.haramaya.edu.et//hru/handle/123456789/244 2026-06-19T09:43:22Z 2026-06-19T09:43:22Z Moata Haile Ritesh Pratap Singh (PhD) Mr. Eyob Mersha http://ir.haramaya.edu.et//hru/handle/123456789/8666 2026-06-18T06:19:38Z 2024-12-01T00:00:00Z

INVESTIGATION OF CHANNEL ESTIMATION METHODS FOR MASSIVE MIMO SYSTEM WITH PILOT DECONTAMINATION IN RICIAN FADING Moata Haile; Ritesh Pratap Singh (PhD); Mr. Eyob Mersha Massive multiple input multiple output (MIMO) systems are essential to meet the growing need for enhanced capacity and faster data rates in modern wireless communication networks, particularly in the fifth generation (5G). These systems serve many users at once on the same frequency band by using a lot of antennas at the base station (BS). The computational complexity of many antenna numbers and pilot contamination are the main obstacles to estimating the channel of the massive MIMO system. To overcome pilot contamination challenges, this thesis aims to investigate pilot-based channel estimate methods with pilot decontamination in Rician fading. In this thesis, soft pilot reuse is combined with the weighted graph coloring pilot decontamination mechanism to overcome the impact of pilot contamination in Rician fading scenarios, which is the main weakness in massive MIMO systems. In both Rayleigh and Rician fading, the accuracy of the channel estimate is increased by mitigating the effects of pilot contamination. MATLAB simulations are used to examine how well the minimum mean squared error (MMSE), element-wise minimum mean squared error (EW-MMSE), and least squares (LS) techniques work with the Rayleigh and Rician fading channels model. The simulation result shows that the better method for channel estimation in both uplink and downlink situations is MMSE with Rician fading. In the uplink system, spectral efficiency (SE) of the channel estimate is increased from 20.9 b/s/Hz/cell to 24.7 b/s/Hz/cell with pilot decontamination at BS antenna M = 90. For downlink, it increased from 21.8 b/s/Hz/cell to 27.5 b/s/Hz/cell. In Rayleigh fading, for the uplink system, SE increased from 14.0 b/s/Hz/cell to 16.5 b/s/Hz/cell at M = 90 with pilot decontamination,whereas in the downlink it improved from 14.2 b/s/Hz/cell to 17.5 b/s/Hz/cell. The results show that the addition of decontamination techniques increases the accuracy of channel estimates, hence improving system performance for massive MIMO networks. So, MMSE are useful for utilizing channel characteristics, while the proposed method (soft pilot reuse combined with weighted graph coloring) is a reliable way to reduce the impact of pilot contamination on Rayleigh and Rician fading. 103p.

2024-12-01T00:00:00Z Atomsa Megersa Mamo Ritesh Pratap Singh (PhD) Atli Lemma http://ir.haramaya.edu.et//hru/handle/123456789/8493 2026-06-03T06:13:58Z 2025-02-01T00:00:00Z

DESIGN AND ANALYSIS OF A MICROSTRIP PATCH ANTENNA ARRAY FOR 5G APPLICATIONS Atomsa Megersa Mamo; Ritesh Pratap Singh (PhD); Atli Lemma Recent advancements in wireless communication systems have underscored the importance of antennas, with a growing demand for high-performance, compact, cost-effective, multiband, and wideband solutions in both commercial and military sectors. Microstrip patch antennas (MPAs) have emerged as a compact option that meets these requirements effectively. The study focused on designing and analyzing a microstrip patch antenna array for 5G applications, aiming to enhance performance and efficiency in next-generation communication networks. Utilizing ANSYS HFSS software, the research sought to optimize design parameters to achieve high gain, wide bandwidth, and low cross-polarization levels suitable for 5G systems. The proposed antenna designs on RT duroid 5880, FR4, and Mica substrates at 28 GHz demonstrated promising characteristics in radiation pattern, impedance matching, and efficiency for 5G applications. These antennas exhibited low reflection coefficients and good impedance matching, with VSWR values approaching 1. Operating within the frequency range of 26.1 GHz to 28.0 GHz, bandwidths varied from 900 MHz to 3700 MHz, with FR4 configurations offering wider bandwidths. Observed gain values ranged between 7.49 dB and 12.82 dB, with higher gains seen in RT material configurations with Ellipse modification. Directivity values between 9.78 dB and 12.79 dB indicated the antennas' focused directional radiation. Efficiency levels ranged from 76.58% to 99.86%, with RT material and Reflector modification antennas showing superior efficiency. The RT material with Ellipse modification design outperformed others in terms of efficiency, gain, directivity, return loss, and VSWR. Conversely, FR4 with Reflector modification provided broader bandwidth but compromised on gain and directivity. 81p.

2025-02-01T00:00:00Z ABDURO GUYE Ritesh Pratap Singh (Assistant Prof) Asmamaw Getu (Msc) http://ir.haramaya.edu.et//hru/handle/123456789/8332 2025-03-21T06:36:06Z 2021-05-01T00:00:00Z

COMPARISON OF LINEAR CHANNEL ESTIMATION TECHNIQUES FOR 5G NETWORKS ABDURO GUYE; Ritesh Pratap Singh (Assistant Prof); Asmamaw Getu (Msc) We are observing a revolution in wireless technology, where the society is demanding new services, such as smart cities, autonomous vehicles, augmented reality, etc. These challenging services not only are demanding a vast increase of data rates in the range of 1000 times higher, but also they are real-time applications with an important delay constraint. Furthermore, an extraordinary number of different machine-type devices will be connected to the network, known as Internet of Things (IoT), where they will be transmitting real-time measurements from different sensors. In this context, the Third Generation Partnership Project (3GPP) has already developed the new Fifth Generation (5G) of mobile communication systems, which should be capable of satisfying all the requirements. Hence, 5G will provide three key aspects, such as: enhanced mobile broad-band (eMBB) services, massive Area of interest in this work focus on transmitter and receiver RF propagation Channel estimation best techniques impact analysis with respect to achievable sum rates in Massive MIMO systems. In addition to study the massive MIMO RF propagation channels estimation system, the interested in the Channel estimation among different type techniques: Minimum Mean Square Error (MMSE), Zero Forcing (ZF) and Maximum Ratio Transmission (MRT) precoding. Theoretically, the precoding is known as Space Division Multiple Access. Each linear precoding shows the best performance with each signal power regime. For the comparison between MRT and ZF, MRT gives better performance at low signal to noise ratio (SNR) while ZF performs better at high SNR. MMSE gives the best channel estimation across the entire SNR. 70

2021-05-01T00:00:00Z Raey Abebe Dr. Ritesh Pratap Singh Mr. Atli Lemma. http://ir.haramaya.edu.et//hru/handle/123456789/7774 2024-05-22T06:32:00Z 2024-05-01T00:00:00Z

PERFORMANCE EVALUATION OF SPECTRUM SENSING TECHNIQUES USING DEEP LEARNING FOR COGNITIVE RADIO NETWORKS Raey Abebe; Dr. Ritesh Pratap Singh; Mr. Atli Lemma. In recent years, the spectrum demand for wireless communication services and applications has been increasing drastically besides spectrum resource management and allocation have become a hot issue. Cognitive Radio (CR) is designed and implemented to overcome this existing problem by allocating a spectrum band to Primary and Secondary users dynamically. One of the key features to decide over spectrum utilization for a CR is the spectrum sensing (SS) unit which detects and identifies spectral data from the environment. Conventional SS schemes such as Energy detection (ED), Cyclo-stationary and matched filters were first developed and employed on CRs. Their drawbacks such as the inability to exploit both spatial and temporal features of data, high false alarms and less detection probability over noisy data lead to further studies to develop AI, particularly Machine learning (ML) and Deep learning (DL) integrated models. This thesis work is mainly focused on the performance of DL-based models to sense, predict and classify a spectral dataset. Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) models are adapted and their performance in spectrum classification has been evaluated. One of the major contributions of this thesis work was to adapt a hybrid Convolutional Recurrent neural network (CRNN) and to compare its performance with the above existing Neural Network models. CNN is a good performing model in extracting spatial features whereas RNN performs well in extracting temporal features of spectral data. The performance of these DL models has been evaluated using metrics such as classification accuracy, probability of detection (Pd), probability of false alarm (Pfa), Sensing error (SE) and confusion matrix metric formulations. The signal samples were generated with GNU for SNRs from -20 dB to 18 dB with step size of 2 dB over flat fading channel and AWGN. This reliable synthetic dataset consists of 11 modulations with varying SNR levels to train, validate and test our DL models. The simulation experiment was carried out in Python Notebook and virtual Google- Colab environment. The results show our proposed hybrid model outperforms the other DL models in terms of high classification accuracy, high probability of detection and less SE. The LSTM model also performed better than CNN and RNN models with its less probability of false alarm in identifying a signal feature. Although all the DL models proved their better performances, CNN was less accurate in identifying the signal feature particularly in low SNR ranges. 101p.

2024-05-01T00:00:00Z