Spectrum Sensing (SS) is a critical operation in a Cognitive Radio (CR) network to identify spectrum hole thereby preventing licensed users from harmful interference for improving spectrum utilization. However, multipath effects in wireless channel such as multipath fading, shadowing and receiver uncertainty affect the sensing accuracy of CR resulting to high Probability of Missing (PM) that causes interference to Primary User (PU). Maximal Ratio Combining (MRC) technique which was one of the techniques used to solve this problem suffers hardware complexity resulting in high Sensing Time (ST). Therefore, in this paper, modification of MRC technique is carried out to reduce hardware complexity of conventional MRC thereby reducing ST. The modified technique consists of ‘L’ Secondary User (SU) antennas that received the multiple copies of Primary User (PU) signals over Nakagami-m fading channel. The received PU signals are made to passed through separate channel estimator and co-phased to avoid signal cancellation before been summed up. The resultant signa is then made to passed through single RF chain and MF. Output of MF is then used as input to Energy Detector (ED) to obtain the energy of the received signal. The obtained energy is compared with the set threshold to determine the status of spectrum. The modified MRC technique is incorporated with simulation model which consists of PU transmitter that processes the randomly generated data through some signal processing techniques for transmission. Mathematical expression of Probability of False Alarm (PFA) for the modified MRC technique is derived and used to set the thresholds at PFA of 0.01 and 0.05. The modified model is evaluated using PM, Probability of Detection (PD) and PT to determine the performance. The results obtained revealed that modified MRC gives higher PD, lower PM and PT values when compared with conventional MRC.

The prediction of wireless communication signals is of paramount importance for proper network planning. The existing prediction models such as Okumura-Hata, Co-operative for Scientific and Technical Research (COST-231) and free space are less accurate for predicting path-loss values of wireless signals due to differences in propagation environments. Hence, this paper develops a path-loss model using Adaptive Neuro-Fuzzy Inference System (ANFIS) for accurate prediction of wireless High Speed Packet Access (HSPA) network signal in Ibadan, Nigeria. This is achieved by measuring the Received Signal Strength (RSS) from three Base Transmitting Stations (BTS) operating at 2100 MHz frequency in Ojo (longitude E 3’ 53.1060’, latitude N 7’27.2558’), Dugbe (longitude E 3’50.4361’, latitude N 7’ 23.0678’) and Challenge (longitude E 3’ 53.1060’, latitude N 7’ 21.258’) areas of Ibadan using the Drive Test. Ericson Test Equipment for Mobile System (TEMS) phone, Global Positioning System (GPS) and Computer System are used to obtain RSS data at different distances. Base station parameters such as the transmitting antenna height, receiving antenna height, carrier frequency and distance are used as input variables to train ANFIS to develop a model. These base station parameters are also used to investigate the suitability of Okumura-Hata, COST-231 and free space model. A five layer ANFIS structure is developed and trained using Least Square Error (LSE) and Gradient Descent (GD) method to adjust the consequent and premise parameters. The performance of the developed ANFIS model is evaluated using Mean Square Error (MSE) and Root Mean Square Error (RMSE) and compared with Okumura-Hata, COST 231 and free space. The results obtained for ANFIS give lower RMSE and MSE indicating the suitability of ANFIS model for path-loss prediction. The developed ANFIS model can be used for network planning and budgeting in these environments.