This thesis presents a comprehensive investigation into improving wireless transmission rates and reliability through the implementation of Space Time Trellis Codes(STTC). The study focuses on enhancing the coding gain, diversity gain, and flexible transmission capabilities, which are crucial for Error-Correcting Codes (ECC)in wireless communications. Spectral efficiency, a primary concern, is achieved byincorporating Multiple-Input Multiple-Output (MIMO) techniques. This thesis discusses the encoding method and decoding algorithm for STTC systems. Then, the performance of MIMO using the Alamouti scheme is determined.Further, the benefits of Cognitive Radio (CR), an intelligent radio technology capable of automatically detecting the best available wireless channel in the spectrumat any given time, are utilized. Energy Detection (ED) is employed for SpectrumSensing (SS) to detect the presence of Primary Users (PUs). The thesis analyzes theperformance of Probability of Detection (PD) and Probability of False Alarm (PFA). Furthermore, the thesis evaluates the Bit Error Rate (BER) performance of STTCover an additive white Gaussian noise (AWGN) channel, considering different modulation schemes generated in MATLAB. Specifically, it investigates the performance ofSTTC-MIMO in terms of BER over Rayleigh channels with Quadrature AmplitudeModulation (QAM) for different combinations of transmitting and receiving antennas. The findings from this research contribute to advancing wireless communicationsystems by addressing the challenges of error correction, spectral efficiency, and reliable data transmission. The integration of STTC with MIMO and CR presents aprogressive approach towards maximizing the potential of wireless communicationtechnologies to cater to the increasing demands for seamless connectivity and superior performance. This research adds to the ongoing efforts of enhancing wirelesscommunication systems’ performance and efficiency, fostering advancements in wireless technology.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-25592 |
Date | January 2023 |
Creators | Kankantee, Sanjana, Ganta, Sai Praneeth Reddy |
Publisher | Blekinge Tekniska Högskola, Institutionen för datavetenskap |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
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