Robust Channel Estimation in Multiuser Downlink 5G Systems Under Channel Uncertainties

In wireless communication, the performance of the network highly relies on the accuracy of channel state information (CSI). On the other hand, the channel statistics are usually unknown, and the measurement information is lost due to the fading phenomenon. Therefore, we propose a channel estimation approach for downlink communication under channel uncertainty. We apply the Tobit Kalman filter (TKF) method to estimate the hidden state vectors of wireless channels. To minimize the maximum estimation error, a robust minimax minimum estimation error (MSE) estimation approach is developed while the QoS requirements of wireless users is taken into account. We then formulate the minimax problem as a non-cooperative game to find an optimal filter and adjust the best behavior for the worst-case channel uncertainty. We also investigate a scenario in which the actual operating point is not exactly known under model uncertainty. Finally, we investigate the existence and characterization of a saddle point as the solution of the game. Theoretical analysis verifies that our work is robust against the uncertainty of the channel statistics and able to track the true values of the channel states. Additionally, simulation results demonstrate the superiority of the model in terms of MSE value over related techniques

Cooperative spectrum sensing: performance analysis and algorithms

The employment of cognitive (intelligent) radios presents an opportunity to efficiently use the scarce spectrum with the condition that it causes a minimal disturbance to the primary user. So the cognitive or secondary users use spectrum sensing to detect the presence of primary user. In this thesis, different aspects related to spectrum sensing and cognitive radio performance are theoretically studied for the discussion and in most cases, closedform expressions are derived. Simulations results are also provided to verify the derivations. Firstly, robust spectrum sensing techniques are proposed considering some realistic conditions, such as carrier frequency offset (CFO) and phase noise (PN). These techniques are called the block-coherent detector (N2 -BLCD), the secondorder matched filter-I (SOMF-I) and the second-order matched filter-II (SOMF-II). The effect of CFO on N2 -BLCD and SOMF-I is evaluated theoretically and by simulation for SOMF-II. However, the effect of PN is only evaluated by simulation for all proposed techniques. Secondly, the detection performance of an energy detector (ED) is analytically investigated over a Nakagami-m frequency-selective (NFS) channel. Thirdly, the energy efficiency aspect of cooperative spectrum sensing is addressed, whereby the energy expenditure is reduced when secondary users report their test statistics to the fusion center (FC). To alleviate the energy consumption overhead, a censored selection combining based power censoring (CSCPC) is proposed. The accomplishment of energy saving is conducted by not sending the test statistic that does not contain robust information or it requires a lot of transmit power. The detection performance of the CSCPC is analytically derived using stochastic geometry tools and verified by simulation. Simulation results show that that the CSCPC technique can reduce the energy consumption compared with the conventional techniques while a detection performance distortion remains negligible. Finally, an analytical evaluation for the cognitive radio performance is presented while taking into consideration realistic issues, such as noise uncertainty (NU) and NFS channel. In the evaluation, sensing-throughput tradeoff is used as an examination metric. The results illustrate the NU badly affects the performance, but the performance may improve when the number of multipath increases