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Abstract Multiple-input multiple-output (MIMO) concept has been around for decades since it can improves capacity and reliability of the wireless systems. The initial work in MIMO focused on point-to-point MIMO system, where we have two devices with multiple antennas communicate together. The direction of research has shifted to multi-user MIMO (MU-MIMO) which oers big advantages over point-to-point MIMO system. In MU-MIMO system, the base station (BS) with multiple antennas simultaneously serves a set of single-antenna users. In this way, expensive equipment is only needed at the BS side, while single-antenna users are relatively cheap. Moreover, in MU-MIMO the users are typically separated in space by many wavelengths. As a result, the MU-MIMO system is commonly used in most communication standards such as 802.11 (WiFi), 802.16 (WiMAX) and LTE. For most MU-MIMO systems the number of BS antennas is few (i.e. fewer than 10 antennas), which limits the improvement in spectral eciency. Massive MIMO (also known as ”Very Large MIMO”, ”Hyper MIMO” and ”Large Scale Antenna Systems”) is a new trend of research in which the BS is equipped with a very large number of antennas (e.g., 100 or more) can serve a large number of users sharing the same system resources (time, frequency and space, etc.). This is possible as the BS is able to decrease (asymptotically eliminate) the user interference, as well as the eect of the small-scale fading and the random additive noise. Massive MIMO can theoretically oer advantages in terms of energy and spectral eciencies. In order to achieve these promised gains, the channel state information (CSI) is required for signal detection and/or precoding. Training sequences (pilot signals) are used to acquire these CSI, however, the number of pilot signals is limited as it depends on coherence time and coherence bandwidth (where channel i coherency depends on the propagation environment, the carrier frequency, and user mobility). In order to serve a large number of users simultaneously, the pilot sequences are reused through the cellular system causing the well-known problem of ”pilot contamination”. In this thesis, we propose two methods to decrease pilot contamination. 1. A structure, i.e. non-random pilot hopping and a weighted moving average channel estimation is proposed for time-varying massive MIMO systems. The potential spectral eciency gain is obtained by combining pilot reuse and a structure pilot hopping strategy. The proposed algorithm allows the users to experience a dierent set of contaminating users across time and therefore make it possible to the BS to average out the interference eect. The performance of multi-cell network where the BS performs matched ltering (MF) or zero forcing (ZF) for data detection and least square (LS) or minimum mean square error (MMSE) for channel estimation is analyzed. Using polynomial expansion approximations and massive MIMO properties, we obtain achievable rates that are tight even at nite (not very large) number of antennas. Moreover, the proposed algorithm involves a weighted moving average that incorporates the past channel estimation by a correlation factor to improve the channel estimation of the current one. Due to the massive MIMO properties, the algorithm is robust to channel time-variations and can be used with any previously proposed channel estimation method. Our analytical results are nally illuminated by means of Monte Carlo simulations that show the eect of the main system variables in various cases, e.g. small/large number of antennas, number of users, Doppler frequencies and the signal-to-noise ratio (SNR). 2. Increasing the number of antennas at user side (two-antenna users) and proposing a simple data-aided channel estimation ii method for the extra antenna. We show that increasing the number of antennas at the user side is possible without increasing the channel estimation overhead linearly with the number of extra antennas. A semi-blind channel estimation for multiantenna users is proposed where a pilot sequence is used for only one antenna whereas the other antenna(s) use a data-aided channel estimation so there is no overhead in terms of pilot symbols. The extra antenna may be exploited in dierent schemes: data multiplexing (i.e. each user transmits two independent signals from its two antennas), spatial repetition (i.e. the second antenna transmits the same signal as the rst antenna) and interference mitigation using interference-alignment (IA) precoding. It is shown that data multiplexing scheme increases the achievable rate. However, spatial repetition case has the eect of virtually increasing (doubling) the number of antennas at the base station compared with the conventional single-antenna users, the third precoding (IA) scheme outperforms the second scheme in terms of spectral eciency as it eectively decreases the size of the interference space. Using polynomial expansion approximations and new lower bounds, we obtain achievable rates when matched ltering detection is used in uplink multi-cell MIMO system. The rate bounds are tight even at nite (not very large) number of antennas. The simulation results show the gain in achievable rate and the number of the base station antennas that can be saved by adding additional antenna at each user compared to the single-antenna user system. The proposed algorithm is applicable for more than two antennas. |