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In cellular networks, interference is identified as the major performance bottleneck. Attempts are made in 4G and 5G systems to address this by pooling base stations together to form a network multiple-input multiple-output (MIMO) system. Global coordination in network MIMO systems is known to be highly complex and costly. In this thesis, a scalable solution is proposed by clustering the network into groups of base stations. Interference within the cluster is mitigated by performing network MIMO based signal processing in each cluster independently. Interference between clusters is then cancelled by applying fractional frequency reuse (FFR) on a cluster scale. In FFR systems, a greater reuse factor is used for users near the cell or cluster edge since they are more prone to interference. An important problem in FFR systems is classifying the user location as either being in the centre or near the edge. The conventional technique of using a one-dimensional signal-to-interference-and-noise ratio (SINR) threshold is highly inaccurate and an improved machine learning approach based on logistic regression is proposed. It is shown to improve the accuracy to at least 80% and the cell sum rate performance is shown to be comparable to that of a system with 100% accurate classification.

Long Term Evolution (LTE) is the next major step in mobile radio communications and is currently introduced as Release 8 in the 3rd Generation Partnership Project (3GPP). The new evolution aims to reduce delay, improve spectrum flexibility and further reduce the cost for operators and end users. In order to fulfil the target of achieving high throughput, LTE employs Orthogonal Frequency Division Multiplexing (OFDM) as the physical layer transmission technique in the downlink. OFDM is suitable for high data rate transmission in wideband wireless systems due to its spectral efficiency and good immunity to multipath fading. LTE enables multiple user access through an Orthogonal Frequency Division Multiple Access (OFDMA) scheme. In OFDMA, multiple user access is enabled by sharing the subcarriers among different users. The division of the system bandwidth between multiple users opens up an additional dimension in the frequency domain which can be exploited for diversity purposes. In addition to that, two MIMO techniques are adopted in LTE, namely space frequency block coding (SFBC) and Spatial Multiplexing (SM). The adaptation of the spatial multiplexing technique in LTE creates additional freedom in the resource allocation in the downlink. By employing a transmit precoding technique, spatial layers can be scheduled to different users and an additional layer of diversity in the space domain can be achieved. The additional layer of diversity can further increase the diversity gain that be achieved in the frequency and time domains. This thesis mainly addresses the gain that can be achieved by resource allocation in the time, frequency and spatial domains in LTE. A detailed performance analysis on the physical layer of LTE downlink will be presented for both SISO and MIMO scenarios. A unitary precoding technique is also considered. Some well known and commonly used scheduling and resource allocations algorithms are investigated and presented. In addition to that, several novel scheduling and resource allocation algorithms are proposed and presented. Simulation results have shown that considerable improvement in terms of throughput performance or complexity can be observed. In particular, a diversity gain of up to 10dB can be achieved in the downlink when resource allocation algorithms are employed for a MU-MIMO scenario. In short, this thesis aims to give a detailed analysis on the diversity gain that can be achieved in the OFDMA system and the possible tradeoff between the diversity gain in three dimensions as well as the signalling overhead.