Code division multiple access

This dissertation investigates the performance of different space diversity combining techniques for the wireless mobile communication systems employing Direct Sequence-Code Division Multiple Access (DS-CDMA). It covers two research topics, all falling under the umbrella of diversity combining techniques. The first part deals with diversity reception of wideband DS-CDMA signals in which the diversity branches experience some correlation. This analysis is performed without the usual assumption that diversity branches are independent and hence uncorrelated. In this case, the analysis is limited to the conventional diversity techniques such as maximal ratio combining, equal gain combining and selection diversity. In particular, the effect of correlation on system performance using two correlation profiles namely, the constant and the exponential correlation profiles is explored. In the constant correlation, the correlation between adjacent antenna are equal regardless of the separation between them, while in exponential correlation, it is assumed that the level of correlation between adjacent antennas decreases as their separation increases. The second topic deals with the development of new combining techniques. Two new techniques--the Generalized Selection Diversity Combining (GSDC) and the Maximal Ratio-Selection Diversity Combining (MR-SDC)--are introduced and analyzed in this dissertation. Analytical models which can be used to evaluate the performance of these new techniques are indicated. Results show that GSDC perform significantly better than the conventional selection diversity when two or more larger signals are selected. The MR-SDC technique accounts for the possibility of using a large array of antennas and for occasions when the receiving antennas may not be collocated. Also, it is shown that for the large array of antennas, better performance is achieved when the MR-SDC is employed with the maximum number of subgroups. The figures of merit used in this dissertation are the average Bit Error Rate (BER) and the probability of outage for a given threshold probability of error. Exact and approximate expressions are derived for the average bit error probability as well as for the outage probability while accounting for the effect of multipath fading, multiple access interference and background noise.

Wireless devices in wireless networks are powered typically by small batteries that are not replaceable nor recharged in a convenient way. To prolong the operating lifetime of networks, energy efficiency is indicated as a critical issue and energy-efficient resource allocation designs have been extensively developed. We investigated energy-efficient schemes that prolong network operating lifetime in wireless sensor networks and in wireless relay networks. In Chapter 2, the energy-efficient resource allocation that minimizes a general cost function of average user powers for small- or medium-scale wireless sensor networks, where the simple time-division multiple-access (TDMA) is adopted as the multiple access scheme. A class of Ç-fair cost-functions is derived to balance the tradeoff between efficiency and fairness in energy-efficient designs. Based on such cost functions, optimal channel-adaptive resource allocation schemes are developed for both single-hop and multi-hop TDMA sensor networks. In Chapter 3, optimal power control methods to balance the tradeoff between energy efficiency and fairness for wireless cooperative networks are developed. It is important to maximize power efficiency by minimizing power consumption for a given quality of service, such as the data rate; it is also equally important to evenly or fairly distribute power consumption to all nodes to maximize the network life. The optimal power control policy proposed is derived in a quasi-closed form by solving a convex optimization problem with a properly chosen cost-function. To further optimize a wireless relay network performance, an orthogonal frequency division multiplexing (OFDM) based multi-user wireless relay network is considered in Chapter 4.

H.264/AVC encoder complexity is mainly due to variable size in Intra and Inter frames. This makes H.264/AVC very difficult to implement, especially for real time applications and mobile devices. The current technological challenge is to conserve the compression capacity and quality that H.264 offers but reduce the encoding time and, therefore, the processing complexity. This thesis applies machine learning technique for video encoding mode decisions and investigates ways to improve the process of generating more general low complexity H.264/AVC video encoders. The proposed H.264 encoding method decreases the complexity in the mode decision inside the Inter frames. Results show, at least, a 150% average reduction of complexity and, at most, 0.6 average increases in PSNR for different kinds of videos and formats.