Analysis and evaluation of decentralized multiaccess Mac for ad-hoc networks

In mobile ad-hoc radio networks, terminals are mobile and heterogeneous, the architecture of the network is continuously changing, communication links are packet oriented and radio resources are scarce. Therefore, mechanisms on how to access the radio channel are extremely important in order to improve network efficiency and, when needed, to guarantee QoS. However, due to these network harsh conditions, decentralized Medium Access Control (MAC) protocols designed specifically for ad hoc networks are scarce. In this paper we present a novel decentralized multiaccess MAC protocol for Ad Hoc networks. This MAC protocol is an hybrid CDMATDMA in which a cross layer approach has been followed in order to maximize network throughput. A theoretical analysis of the system is presented ending up with closed expressions for the throughput and delay of the network and some simulations are presented to evaluate the performance of the system.Postprint (published version

Efficient implementation of channel estimation algorithm for beamforming

Abstract. The future 5G mobile network technology is expected to offer significantly better performance than its predecessors. Improved data rates in conjunction with low latency is believed to enable technological revolutions such as self-driving cars. To achieve faster data rates, MIMO systems can be utilized. These systems enable the use of spatial filtering technique known as beamforming. Beamforming that is based on the preacquired channel matrix is computationally very demanding causing challenges in achieving low latency. By acquiring the channel matrix as efficiently as possible, we can facilitate this challenge. In this thesis we examined the implementation of channel estimation algorithm for beamforming with a digital signal processor specialized in vector computation. We present implementations for different antenna configurations based on three different approaches. The results show that the best performance is achieved by applying the algorithm according to the limitations given by the system and the processor architecture. Although the exploitation of the parallel architecture was proved to be challenging, the implementation of the algorithm would have benefitted from the greater amount of parallelism. The current parallel resources will be a challenge especially in the future as the size of antenna configurations is expected to grow.Keilanmuodostuksen tarvitseman kanavaestimointialgoritmin tehokas toteutus. Tiivistelmä. Tulevan viidennen sukupolven mobiiliverkkoteknologian odotetaan tarjoavan merkittävästi edeltäjäänsä parempaa suorituskykyä. Tämän suorituskyvyn tarjoamat suuret datanopeudet yhdistettynä pieneen latenssiin uskotaan mahdollistavan esimerkiksi itsestään ajavat autot. Suurempien datanopeuksien saavuttamiseksi voidaan hyödyntää monitiekanavassa käytettävää MIMO-systeemiä, joka mahdollistaa keilanmuodostuksena tunnetun spatiaalisen suodatusmenetelmän käytön. Etukäteen hankittuun kanavatilatietoon perustuva keilanmuodostus on laskennallisesti erittäin kallista. Tämä aiheuttaa haasteita verkon pienen latenssivaatimuksen saavuttamisessa. Tässä työssä tutkittiin keilanmuodostukselle tarkoitetun kanavaestimointialgoritmin tehokasta toteutusta hyödyntäen vektorilaskentaan erikoistunutta prosessoriarkkitehtuuria. Työssä esitellään kolmea eri lähestymistapaa hyödyntävät toteutukset eri kokoisille antennikonfiguraatioille. Tuloksista nähdään, että paras suorituskyky saavutetaan sovittamalla algoritmi järjestelmän ja arkkitehtuurin asettamien rajoitusten mukaisesti. Vaikka rinnakkaisarkkitehtuurin hyödyntäminen asetti omat haasteensa, olisi algoritmin toteutus hyötynyt suuremmasta rinnakkaisuuden määrästä. Nykyinen rinnakkaisuuden määrä tulee olemaan haaste erityisesti tulevaisuudessa, sillä antennikonfiguraatioiden koon odotetaan kasvavan

Cell search algorithms for WCDMA systems

Wideband Code Division Multiple Access (WCDMA) system uses orthogonal channelization codes to distinguish physical channels in a base station, while base stations are identified by different downlink scrambling codes. User equipments (UEs) must achieve synchronization to the downlink scrambling code before decoding any messages from base stations. The process of searching for a base station and synchronization to the downlink scrambling code is often referred to as cell search. The performance of cell search has a significant impact on a UE's switch-on delay, and thus it is very important to UE design. The goal of designing a cell search algorithm is to achieve a balance between speed, accuracy and complexity. A basic three-stage cell search procedure has been defined by 3GPP. It employs synchronization channels and the common pilot channel to facilitate a fast cell search. This cell search scheme only works well if there is no frequency offset between a base station's transmitter and a UE's receiver and if sampling timing is perfect on a UE. In practice, however, imperfection of oscillator in a UE may cause a big frequency error as well as clock error. It usually results in phase rotations and sampling timing drifts, which may degrade cell search performance significantly. Some advanced cell search algorithms have been proposed for mitigating impacts of frequency error or clock error. However, there is no much discussion on comprehensive solutions that can deal with the two negative impacts at the same time. In this thesis, we propose an algorithm that considers both frequency error and clock error. A fast and accurate cell search with a relatively low level of complexity is achieved. The algorithms are based on a combination of four existing enhanced cell search algorithms that are designed for a toleration of either frequency error or clock error. We first introduce the 3GPP-defined cell search algorithm as a basis. Then the four existing enhanced algorithms, PSD (partial symbol de-spreading), DDCC (differential detection with coherent combining), STS-1 (serial test in stage-1) and RSPT (random sampling per trial) are presented. Next, we propose four possible combinations of the existing algorithms: PSD+STS-1, PSD+RSPT, DDCC+STS-1 and DDCC+RSPT. Through extensive computer simulations, we find the DDCC+RSPT algorithm to be the best one. It is superior to other combinations and also outperforms any existing algorithm in terms of acquisition time, detection probability and complexity. Therefore, it is highly recommended for practical uses

Network configuration improvement and design aid using artificial intelligence

This dissertation investigates the development of new Global system for mobile communications (GSM) improvement algorithms used to solve the nondeterministic polynomial-time hard (NP-hard) problem of assigning cells to switches. The departure of this project from previous projects is in the area of the GSM network being optimised. Most previous projects tried minimising the signalling load on the network. The main aim in this project is to reduce the operational expenditure as much as possible while still adhering to network element constraints. This is achieved by generating new network configurations with a reduced transmission cost. Since assigning cells to switches in cellular mobile networks is a NP-hard problem, exact methods cannot be used to solve it for real-size networks. In this context, heuristic approaches, evolutionary search algorithms and clustering techniques can, however, be used. This dissertation presents a comprehensive and comparative study of the above-mentioned categories of search techniques adopted specifically for GSM network improvement. The evolutionary search technique evaluated is a genetic algorithm (GA) while the unsupervised learning technique is a Gaussian mixture model (GMM). A number of custom-developed heuristic search techniques with differing goals were also experimented with. The implementation of these algorithms was tested in order to measure the quality of the solutions. Results obtained confirmed the ability of the search techniques to produce network configurations with a reduced operational expenditure while still adhering to network element constraints. The best results found were using the Gaussian mixture model where savings of up to 17% were achieved. The heuristic searches produced promising results in the form of the characteristics they portray, for example, load-balancing. Due to the massive problem space and a suboptimal chromosome representation, the genetic algorithm struggled to find high quality viable solutions. The objective of reducing network cost was achieved by performing cell-to-switch optimisation taking traffic distributions, transmission costs and network element constraints into account. These criteria cannot be divorced from each other since they are all interdependent, omitting any one of them will lead to inefficient and infeasible configurations. Results obtained further indicated that the search space consists out of two components namely, traffic and transmission cost. When optimising, it is very important to consider both components simultaneously, if not, infeasible or suboptimum solutions are generated. It was also found that pre-processing has a major impact on the cluster-forming ability of the GMM. Depending on how the pre-processing technique is set up, it is possible to bias the cluster-formation process in such a way that either transmission cost savings or a reduction in inter base station controller/switching centre traffic volume is given preference. Two of the difficult questions to answer when performing network capacity expansions are where to install the remote base station controllers (BSCs) and how to alter the existing BSC boundaries to accommodate the new BSCs being introduced. Using the techniques developed in this dissertation, these questions can now be answered with confidence.Dissertation (MEng)--University of Pretoria, 2008.Electrical, Electronic and Computer Engineeringunrestricte