On generalized optimal scheduling of high data-rate bursts in CDMA systems

In a code-division multiple access (CDMA)-based wireless communication system, forward link is power limited and reverse link is interference limited. With power control and statistical multiplexing, voice services can be supported reasonably well. However, for high data-rate services, a more comprehensive scheduling mechanism is needed in order to achieve a high capacity while satisfying the forward and reverse link constraints. In this paper, we formulate the high data-burst scheduling as a integer programming problem using a generic CDMA system model. We also suggest an optimal algorithm for generating scheduling solutions. With cdma2000 system details plugged in the proposed algorithm, it is found that our algorithm considerably outperforms several fast heuristics, including equal sharing, first-come-first-served, longest delay first, and shortest burst first.published_or_final_versio

Analytical modeling of HSUPA-enabled UMTS networks for capacity planning

In recent years, mobile communication networks have experienced significant evolution. The 3G mobile communication system, UMTS, employs WCDMA as the air interface standard, which leads to quite different mobile network planning and dimensioning processes compared with 2G systems. The UMTS system capacity is limited by the received interference at NodeBs due to the unique features of WCDMA, which is denoted as `soft capacity'. Consequently, the key challenge in UMTS radio network planning has been shifted from channel allocation in the channelized 2G systems to blocking and outage probabilities computation under the `cell breathing' effects which are due to the relationship between network coverage and capacity. The interference characterization, especially for the other-cell interference, is one of the most important components in 3G mobile networks planning. This monograph firstly investigates the system behavior in the operation of UMTS uplink, and develops the analytic techniques to model interference and system load as fully-characterized random variables, which can be directly applicable to the performance modeling of such networks. When the analysis progresses from single-cell scenario to multi-cell scenario, as the target SIR oriented power control mechanism is employed for maximum capacity, more sophisticated system operation, `feedback behavior', has emerged, as the interference levels at different cells depend on each other. Such behaviors are also captured into the constructed interference model by iterative and approximation approaches. The models are then extended to cater for the features of the newly introduced HSUPA, which provides enhanced dedicated channels for the packet switched data services such that much higher bandwidth can be achieved for best-effort elastic traffic, which allows network operators to cope with the coexistence of both circuit-switched and packet-switched traffic and guarantee the QoS requirements. During the derivation, we consider various propagation models, traffic models, resource allocation schemes for many possible scenarios, each of which may lead to different analytical models. All the suggested models are validated with either Monte-Carlo simulations or discrete event simulations, where excellent matches between results are always achieved. Furthermore, this monograph studies the optimization-based resource allocation strategies in the UMTS uplink with integrated QoS/best-effort traffic. Optimization techniques, both linear-programming based and non-linear-programming based, are used to determine how much resource should be assigned to each enhanced uplink user in the multi-cell environment where each NodeB possesses full knowledge of the whole network. The system performance under such resource allocation schemes are analyzed and compared via Monte-Carlo simulations, which verifies that the proposed framework may serve as a good estimation and optimal reference to study how systems perform for network operators

Analytical modeling of HSUPA-enabled UMTS networks for capacity planning

In recent years, mobile communication networks have experienced significant evolution. The 3G mobile communication system, UMTS, employs WCDMA as the air interface standard, which leads to quite different mobile network planning and dimensioning processes compared with 2G systems. The UMTS system capacity is limited by the received interference at NodeBs due to the unique features of WCDMA, which is denoted as `soft capacity'. Consequently, the key challenge in UMTS radio network planning has been shifted from channel allocation in the channelized 2G systems to blocking and outage probabilities computation under the `cell breathing' effects which are due to the relationship between network coverage and capacity. The interference characterization, especially for the other-cell interference, is one of the most important components in 3G mobile networks planning. This monograph firstly investigates the system behavior in the operation of UMTS uplink, and develops the analytic techniques to model interference and system load as fully-characterized random variables, which can be directly applicable to the performance modeling of such networks. When the analysis progresses from single-cell scenario to multi-cell scenario, as the target SIR oriented power control mechanism is employed for maximum capacity, more sophisticated system operation, `feedback behavior', has emerged, as the interference levels at different cells depend on each other. Such behaviors are also captured into the constructed interference model by iterative and approximation approaches. The models are then extended to cater for the features of the newly introduced HSUPA, which provides enhanced dedicated channels for the packet switched data services such that much higher bandwidth can be achieved for best-effort elastic traffic, which allows network operators to cope with the coexistence of both circuit-switched and packet-switched traffic and guarantee the QoS requirements. During the derivation, we consider various propagation models, traffic models, resource allocation schemes for many possible scenarios, each of which may lead to different analytical models. All the suggested models are validated with either Monte-Carlo simulations or discrete event simulations, where excellent matches between results are always achieved. Furthermore, this monograph studies the optimization-based resource allocation strategies in the UMTS uplink with integrated QoS/best-effort traffic. Optimization techniques, both linear-programming based and non-linear-programming based, are used to determine how much resource should be assigned to each enhanced uplink user in the multi-cell environment where each NodeB possesses full knowledge of the whole network. The system performance under such resource allocation schemes are analyzed and compared via Monte-Carlo simulations, which verifies that the proposed framework may serve as a good estimation and optimal reference to study how systems perform for network operators

Analysis and Optimization of Random Sensing Order in Cognitive Radio Networks

Developing an efficient spectrum access policy enables cognitive radios to dramatically increase spectrum utilization while ensuring predetermined quality of service levels for primary users. In this paper, modeling, performance analysis, and optimization of a distributed secondary network with random sensing order policy are studied. Specifically, the secondary users create a random order of available channels upon primary users return, and then find optimal transmission and handoff opportunities in a distributed manner. By a Markov chain analysis, the average throughputs of the secondary users and average interference level among the secondary and primary users are investigated. A maximization of the secondary network performance in terms of the throughput while keeping under control the average interference is proposed. It is shown that despite of traditional view, non-zero false alarm in the channel sensing can increase channel utilization, especially in a dense secondary network where the contention is too high. Then, two simple and practical adaptive algorithms are established to optimize the network. The second algorithm follows the variations of the wireless channels in non-stationary conditions and outperforms even static brute force optimization, while demanding few computations. The convergence of the distributed algorithms are theoretically investigated based on the analytical performance indicators established by the Markov chain analysis. Finally, numerical results validate the analytical derivations and demonstrate the efficiency of the proposed schemes. It is concluded that fully distributed sensing order algorithms can lead to substantial performance improvements in cognitive radio networks without the need of centralized management or message passing among the users.Comment: 16 pages, 12 figures, 7 tables, accepted in Journal of Selected Areas in Communications (J-SAC) CR series and will be published in Apr'1

Exploiting Opponent Modeling For Learning In Multi-agent Adversarial Games

An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our approach for both offline play generation and real-time team response in the Rush 2008 American football simulator. Simultaneously predicting the movement trajectories, future reward, and play strategies of multiple players in real-time is a daunting task but we illustrate how it is possible to divide and conquer this problem with an assortment of data-driven models. By leveraging spatio-temporal traces of player movements, we learn discriminative models of defensive play for opponent modeling. With the reward information from previous play matchups, we use a modified version of UCT (Upper Conference Bounds applied to Trees) to create new offensive plays and to learn play repairs to counter predicted opponent actions. iii In team games, players must coordinate effectively to accomplish tasks while foiling their opponents either in a preplanned or emergent manner. An effective team policy must generate the necessary coordination, yet considering all possibilities for creating coordinating subgroups is computationally infeasible. Automatically identifying and preserving the coordination between key subgroups of teammates can make search more productive by pruning policies that disrupt these relationships. We demonstrate that combining opponent modeling with automatic subgroup identification can be used to create team policies with a higher average yardage than either the baseline game or domain-specific heuristics

Application of TOS/AMS to TDRS E and F

The development of the Transfer Orbit Stage (TOS) and the study of an Apogee and Maneuvering Stage (AMS) to be used in conjunction with the TOS are presented. A definition of the TOS/AMS configuration is provided along with a detailed design analysis including layout drawings, component definition, performance, sts and spacecraft interface definition, schedules, cost estimates, and specifications documents