Resource Allocation in Cellular Communication Systems

这是麻省理工学院的Dimitri Bertsekas和加州理工学院的Andrea金匠的联合项目。 信道容量的稀缺，以支持新兴的多媒体无线应用需要有效的策略，使用可用的带宽。 任何使用固定频率复用和基站分配的系统都必须相对于最坏情况的干扰假设进行设计。 动态资源分配是一种更有效的策略，其中频率、基站、数据速率和功率电平相对于当前和潜在的系统条件被动态地分配。 在这项工作中，我们建议制定的一般资源分配问题作为干扰约束的优化问题。 成本函数非常一般。 它可以将大多数的实际系统的复杂性的优化框架，包括由于用户的移动性，多类业务，和信道变化的小区切换。 我们认为，FDMA和CDMA的方法划分了可用的系统带宽。 我们将应用线性规划来最小化瞬时成本函数，并应用动态规划技术来最小化无限范围内的成本函数。 动态问题的制定将利用神经动态编程技术的最新发展。

This is a joint project between Dimitri Bertsekas, MIT and Andrea Goldsmith, Caltech. The scarcity of channel capacity to support emerging multimedia wireless applications requires efficient strategies for using the available bandwidth. Any system using fixed frequency reuse and base station assignment must be designed relative to worst-case interference assumptions. Dynamic resource allocation is a more efficient strategy, where frequencies, base stations, data rates, and power levels are dynamically assigned relative to current and potential system conditions. In this work, we propose to formulate the general resource allocation problem as an optimization problem subject to interference constraints. The cost function is very general. It can incorporate most of the practical systems complexity in the optimization framework, including cell handoff due to user mobility, multiple classes of traffic, and channel variation. We consider both FDMA and CDMA methods of dividing up the available systems band width. We will apply linear programming to minimize the instantaneous cost function, and dynamic programming techniques to minimize the cost function over the infinite horizon. The dynamic problem formulation will utilize recent developments in neuro-dynamic programming techniques.