UC Irvine

Multi Input Multi Output (MIMO) technology has seen prolific use to achieve higher data rates and an improved communication experience for cellular systems. However, one

of the challenging problems in MIMO systems is interference. Interference limits the system performance in terms of rate and reliability. In this thesis, we analyze methods that provide high performance over interference-limited wireless networks such as Long Term Evolution (LTE) and WiFi. In this thesis, we tackle different sources of interference. One of the interference sources is the neighbouring interference, we propose methods that include an optimized solution that models the interference as correlated noise, and uses its statistical information to jointly optimize the base station precoding and user receiver design of LTE systems. We study the benefits of exploiting interference in terms of both probability of error and signal-to-noise ratio (SNR). In addition, we compare the proposed method with the conventional beamforming and maximum ratio combining (MRC).

One of the key challenges to enable high data rates in the downlink of LTE is the precoding and receiver design. We focus primarily on the UE and the base station (BS) processing, particularly on estimating and using the interference resulting from neighboring stations. We propose a receiver design that performs well in the presence of interference. Furthermore, we present a precoding scheme that the BS can use to maximize the signal-to-interference plus noise-ratio (SINR). The proposed algorithm performs well under high speed channels. The limitations of the Minimum Mean Square Error (MMSE) receiver are discussed and it is used for comparison purposes with the proposed approach. An interference free scenario is used as a benchmark to evaluate the proposed

system performance.

Performance of LTE is optimized by tackling practical considerations that affect system performance. We present a suboptimal practical way of estimating the interference and utilizing this information on the processing techniques used at both the UE and the eNodeB sides. We focus on managing both MU-MIMO interference and other cell interference. The proposed study improves system performance even under non-perfect channel knowledge, enabling the throughput gains promised by MU-MIMO.

Along the theme of enhancing spectral efficiency, we In-Band Full-Duplex (IBFD) when used in conjuction with Mu-MIMO. IBFD is very promising in enhancing wireless LANs, where full-duplex access

points (APs) can support simultaneous uplink (UL) and downlink (DL) flows over the same frequency channel. One of the key challenges limiting IBFD benefits is interference. We propose a scheduling technique to manage interference in wireless LANs with full-duplex capability. We focus primarily on scheduling UL and DL stations (STAs) that can be efficiently served simultaneously.

Finally, we take a holistic view of performance by considering practical issues related to system performance, namely, a) Interference resulting from the non-linearity of power amplifiers, and b)the trade-offs between system performance and power consumption.

An important topic for practical communication systems is handling the interference due to the power amplifier nonlinearities, especially in Orthogonal Frequency-Division Multiple Access (OFDMA) based communication systems, due to the high peak to average power ratio. This problem becomes more compounded when a large number of PAs is required, as in Massive MIMO for example. In this thesis, we discuss the impact of PAs on cellular systems. We show the constraints that PAs introduce, and we take these constraints into consideration while searching for the optimum set of transmitter and receiver filters. Moreover, we highlight how Massive MIMO cellular networks can relax PAs constraints resulting in low cost PAs, while maintaining high performance. The performance is evaluated by showing the probability of error curves and signal-to-noise-ratio curves for different transmit powers and different number of transmit antennas.

In terms of power consumption we investigate the use of emerging technologies (such as memristors) to enable highly efficient computation kernels for wireless communication systems. Specifically, we investigate the use of Associative processors (APs) to perform in-memory computation in the context of an FFT processor. To reduce power and power density, we investigate approximate computing in memristive based associative processors. A promising approach to save energy is through reducing the bit width, however reducing the bit width introduces errors that may affect the performance. In this thesis, our goal is to adjust the bit width based on the channel SNR, aiming at achieving good performance at reduced energy consumption. The mathematical approach that analytically describes the system performance under the reduced bit width noise is presented. Based on this model, an adaptive bit width adjustment algorithm is presented that utilizes the received SNR estimates to find the optimal bit width that achieves performance goals at reduced energy consumption. Simulation results show that the proposed algorithms can achieve up to 45\% energy savings as compared to wireless communication systems with conventional FFT.