In wireless systems, radio signals are corrupted due to fading, interference and noise. In order to handle the effects of fading and interference, modern systems employ various techniques including multi-antenna transceivers. Initially, multi-antenna systems were proposed only for point-point communication. More recently, multi-antenna transceivers have been proposed for multiuser (MU) wireless systems. There are various topologies in which multi-antenna transceivers can be used in a multiuser wireless environment. Among them, macrodiversity is an important concept driven by many scenarios, including base station cooperation, coordinated multipoint (CoMP) transmission and network multiple input multiple output (MIMO). A communication system where antenna elements at both source and receiver are widely (geographically) separated is described as a macrodiversity communication system. For these macrodiversity systems, every link may have a different average signal to noise ratio (SNR) since the sources and the receive antennas are all in different locations. This variation in average SNR across the links makes the performance analysis of such systems more complex. For this reason, most of the results currently available are based on simulation. However, the value of analytical results can be immense for efficient computation and optimized operation. Therefore, in this thesis we present a comprehensive, and rigorous analytical investigation of various aspects of multiuser macrodiversity MIMO systems. Two main aspects of macrodiversity MIMO systems are considered: the multiple access channel (MAC) and uplink user scheduling. In the earlier chapters of the thesis, we investigate the performance of uplink transmission employing multi-antenna transmitters and receivers. We analyze the signal-to-interference plus noise ratio (SINR) performance, symbol error rate (SER) and ergodic sum capacity etc. In a later chapter, we consider multiuser scheduling issues in macrodiversity multiuser MIMO systems. The primary emphasis is on the MIMO-MAC where we present some systematic performance metrics and approaches to multiuser scheduling which only require the long term channel state information (CSI). These methods provide a double advantage over scheduling using instantaneous CSI. First, the computational burden is lower and secondly, the delay between obtaining and using channel estimation is reduced.

Macrodiversity system is a communication architecture where base stations (BS) act as distributed nodes of a multiple-input multiple-output (MIMO) antennas. It has many promising features that can improve system performance from a network perspective, such as improving the weak signals of users affected by shadow fading, or users at the cell-edge. They also allow multiple users to share the same resource in time and frequency, improving the overall user capacity. Traditionally, evaluating the link quality of resource-sharing users requires instantaneous channel state information (CSI). However, finding compatible users to share resource in macrodiversity systems is a challenging task. For macrodiversity systems, instantaneous CSI could be passed to the backhaul processing unit (BPU) through the network backhaul. This creates a delay in the signal, and makes instantaneous CSI a less accurate reflection of the channel environment at the time. Passing instantaneous CSI of all users also creates a significant amount of network overheads, reducing the overall efficiency of the network. Compared to MIMO systems with co-located antennas, macrodiversity systems cover a larger geographical area and more users. For this reason, the number of user selection combinations can become extremely large, making scheduling decisions in real time an even more challenging task. These problems limit the realisation of the user capacity potential of macrodiversity systems. This thesis presents a low complexity method of resource allocation for up-link macrodiversity systems. In particular, it uses long-term power to estimate the link quality of resource-sharing users. Using long-term power bypasses the issue of channel estimation error introduced by the network delay, and it also reduces the communication overhead on the network backhaul. In this thesis, we use Symbol-Error Rate (SER) as the measure for link quality. Using the method developed by Basnayaka [1], we are able to estimate SER of resource-sharing users using long-term power. Using the SER estimation method, we further proposed a user compatibility check (UCC), which evaluates the compatibility of users sharing the same resource. Users are only considered compatible with each other if all of them meet a pre-defined SER threshold. We attempt to reduce the complexity of user selection by using heuristic solution-finding methods. In our research, we found that greedy algorithms have the least complexity. We propose four low-complexity user selection algorithms based on a greedy algorithm. These algorithms are simulated under different environment parameters. We evaluate the system performance in terms of utilisation and complexity. Utilisation refers to the percentage of allocated users compared to the theoretical user capacity. Complexity refers to the number of SER calculations required to find a resource allocation solution. From the simulation results, we observed that with the proposed user selection algorithms, we can achieve moderately high utilisation with much lower complexity, compared to finding user selections via an exhaustive search method. Out of the proposed user selection algorithms, the Priority Order with Sequential Removal (PO+SR) and the First-Fit (FF) algorithm have the best overall performance, in terms of the trade-off between utilisation performance, and complexity performance. The final choice of the algorithm will depend on the processing power and the system performance requirement of the macrodiversity system.

<p>Three of the most important factors with regards to wireless multi-hop networks, namely reachability, energy consumption and network stability are considered in our transmitter macrodiversity supported broadcasting routing algorithms. Broadcasting applications are not only used to send routing table, queries, programming logic, any specific request etc. to all the nodes from access point but are also capable of playing a vital role in wireless TV distributions and visual sensor networks. All the algorithms are simulated in the MATLAB environment in which the nodes are random and are battery driven on a multi-hop randomized topology. Four new single frequency network (SFN) based algorithms (SFN-A, SFN-B, SFN-C and SFN-D) are formed in order to work over multi-hopping and where three of the algorithms SFN-A, SFN-B and SFN-D bear more or less the same amount of reachability. These three algorithms are able to reach more than 90% of reachability in only Tx power -8dBm whereas non-SFN requires -4dBm and SFN-C requires -2dBm and, in addition can achieve a maximum of 29 percentage points more reachability than the non-SFN algorithm. However, the best algorithm SFN-D consumes a maximum of 58.76% less energy than the SFN-A and a maximum of 14.28% less energy than the SFN-B. The SFN-D algorithm achieves a maximum 3.43 dB diversity gain together with the maximum 37.33% energy consumption gain in comparison to the non-SFN algorithm.</p>

Three of the most important factors with regards to wireless multi-hop networks, namely reachability, energy consumption and network stability are considered in our transmitter macrodiversity supported broadcasting routing algorithms. Broadcasting applications are not only used to send routing table, queries, programming logic, any specific request etc. to all the nodes from access point but are also capable of playing a vital role in wireless TV distributions and visual sensor networks. All the algorithms are simulated in the MATLAB environment in which the nodes are random and are battery driven on a multi-hop randomized topology. Four new single frequency network (SFN) based algorithms (SFN-A, SFN-B, SFN-C and SFN-D) are formed in order to work over multi-hopping and where three of the algorithms SFN-A, SFN-B and SFN-D bear more or less the same amount of reachability. These three algorithms are able to reach more than 90% of reachability in only Tx power -8dBm whereas non-SFN requires -4dBm and SFN-C requires -2dBm and, in addition can achieve a maximum of 29 percentage points more reachability than the non-SFN algorithm. However, the best algorithm SFN-D consumes a maximum of 58.76% less energy than the SFN-A and a maximum of 14.28% less energy than the SFN-B. The SFN-D algorithm achieves a maximum 3.43 dB diversity gain together with the maximum 37.33% energy consumption gain in comparison to the non-SFN algorithm.