This research addresses the concept of radio resource allocation for cellular communications systems operating in congested and contested environments with an emphasis on end-to-end quality of service (QoS). The radio resource allocation is cast under a proportional fairness formulation which translates to a convex optimization problem. Moreover, the resource allocation scheme considers subscription-based and traffic differentiation in order to meet the QoS requirements of the applications running on the user equipment in the system. The devised resource allocation scheme is realized through a centralized and a distributed architecture and solution algorithms for the aforementioned architectures is derived and implemented in the mobile devices and the base stations. The sensitivity of the resource allocation scheme to the temporal dynamics of the quantity of the users in the system is investigated. Furthermore, the sensitivity of the resource allocation scheme to the temporal dynamics in the application usage percentages is accounted for. In addition, a transmission overhead of the centralized and distributed architectures for the resource allocation schemes is performed. Furthermore, the resource allocation scheme is modified to account for a possible additive bandwidth done through spectrum sharing in congested and contested environments, in particular spectrally coexistent radar systems. The radar-spectrum additive portion is devised in a way to ensure fairness of the allocation, high bandwidth utilization, and interference avoidance. In order to justify the aforesaid modification, the interference from radar systems into the Long Term Evolution (LTE) as the predominant 4G technology is studies to confirm the possibility of the spectrum sharing. The preceding interference analysis contains a detailed simulation of radar systems, propagation path loss models, and a third generation partnership project compliant LTE system. The propagation models are Free Space Path Loss (FSPL) and Irregular Terrain Model (ITM). The LTE systems under consideration are macro cell, outdoor small cells, and indoor small cells. Furthermore, the resource allocation under channel consideration is formalized such that the resources are allocated under a congested environment and based on the quality of channel the users have in the network as well as the quality of service requirements of the applications running on the mobile devices. / Ph. D.

No / A simple method for dynamically adjusting the number of taps of linear equalisers operating in unknown channel conditions is presented. Simulations with various scenarios show that the technique successfully predicts the optimum equaliser length and is capable of adjusting it as the environment changes

During the race towards the4th generation (4G) cellular-based digital communication systems, a growth in the demand for high capacity, multi-media capable, improved Quality-of-Service (QoS) mobile communication systems have caused the developing mobile communications world to turn towards betterMultiple Access (MA) techniques, like Code Division Multiple Access (CDMA) [5]. The demand for higher throughput and better QoS in future 4G systems have also given rise to a scheme that is becoming ever more popular for use in these so-called ‘bandwidth-on-demand’ systems. This scheme is known as adaptive channel coding, and gives a system the ability to firstly sense changes in conditions, and secondly, to adapt to these changes, exploiting the fact that under good channel conditions, a very simple or even no channel coding scheme can be used for Forward Error Correction(FEC). This will ultimately result in better system throughput utilization. One such scheme, known as incremental redundancy, is already implemented in the Enhanced Data Rates for GSM Evolution (EDGE) standard. This study presents an extensive simulation study of a Multi-User (MU), adaptive channel coded Direct Sequence Spread Spectrum Multiple Access (DS/SSMA) communication system. This study firstly presents and utilizes a complex Base Band(BB) DS/SSMA transmitter model, aimed at user data diversity [6] in order to realize the MU input data to the system. This transmitter employs sophisticated double-sideband (DSB)Constant-Envelope Linearly Interpolated Root-of-Unity (CE-LI-RU) filtered General Chirp-Like (GCL) sequences [34, 37, 38] to band limit and spread user data. It then utilizes a fully user-definable, complex Multipath Fading Channel Simulator(MFCS), first presented by Staphorst [3], which is capable of reproducing all of the physical attributes of realistic mobile fading channels. Next, this study presents a matching DS/SSMA receiver structure that aims to optimally recover user data from the channel, ensuring the achievement of data diversity. In order to provide the basic channel coding functionality needed by the system of this study, three simple, but well-known channel coding schemes are investigated and employed. These are: binary Hamming (7,4,3) block code, (15,7,5) binary Bose-Chadhuri-Hocquenghem (BCH) block code and a rate 1/3 <i.Non-Systematic (NS) binary convolutional code [6]. The first step towards the realization of any adaptive channel coded system is the ability to measure channel conditions as fast as possible, without the loss of accuracy or inclusion of known data. In 1965, Gooding presented a paper in which he described a technique that measures communication conditions at the receiving end of a system through a device called a Performance Monitoring Unit (PMU) [12, 13]. This device accelerates the system’sBit Error Rate (BER) to a so-called Pseudo Error Rate(PER) through a process known as threshold modification. It then uses a simple PER extrapolation algorithm to estimate the system’s true BER with moderate accuracy and without the need for known data. This study extends the work of Gooding by applying his technique to the DS/SSMA system that utilizes a generic Soft-Output Viterbi Algorithm(SOVA) decoder [39] structure for the trellis decoding of the binary linear block codes [3, 41-50], as well as binary convolutional codes mentioned, over realistic MU frequency selective channel conditions. This application will grant the system the ability to sense changes in communication conditions through real-time BER measurement and, ultimately, to adapt to these changes by switching to different channel codes. Because no previous literature exists on this application, this work is considered novel. Extensive simulation results also investigate the linearity of the PER vs. modified threshold relationship for uncoded, as well as all coded cases. These simulations are all done for single, as well as multiple user systems. This study also provides extensive simulation results that investigate the calculation accuracy and speed advantages that Gooding’s technique possesses over that of the classic Monte-Carlo technique for BER estimation. These simulations also consider uncoded and coded cases, as well as single and multiple users. Finally, this study investigates the experimental real-time performance of the fully functional MU, adaptive coded, DS/SSMA communication system over varying channel conditions. During this part of the study, the channel conditions are varied over time, and the system’s adaptation (channel code switching) performance is observed through a real-time observation of the system’s estimated BER. This study also extends into cases with multiple system users. Since the adaptive coded system of this study does not require known data sequences (training sequences), inclusion of Gooding’s technique for real-time BER estimation through threshold modification and PER extrapolation in future 4G adaptive systems will enable better Quality-of-Service (QoS) management without sacrificing throughput. Furthermore, this study proves that when Gooding’s technique is applied to a coded system with a soft-output, it can be an effective technique for QoS monitoring, and should be considered in 4G systems of the future. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / MEng / unrestricted