Cross-Layer optimization for LDPC-coded multirate multiuser systems with QoS constraints

Abstract

In this paper, we propose a new multirate multiple-access wireless system implemented by variable spreading gain and chip-level random interleaving. The receiver employs a flexible chip-level iterative multiuser detection scheme where the variable spreading gain affects only the despreading parameters. Optimization across the physical and network layers in the uplink of such a system is treated. It is assumed that each user employs an low-density parity-check (LDPC) code to protect its data. At the physical layer, the quality of service (QoS) requirement is specified in terms of the target bit error rate (BER) of each user. Optimal user transmit powers are dynamically adjusted according to the current system load and the corresponding rate requirements. At the network layer, the QoS requirements include the call blocking probabilities, call connection delays, packet congestion probabilities and packet loss rates. To maximize the average revenue of the network subject to both call-level and packet-level QoS constraints, a multicriterion reinforcement learning (MCRL)-based adaptive call admission control (CAC) method is proposed that can easily handle multiple average QoS requirements. Unlike existing model-based approaches, the MCRL-based technique does not require the explicit knowledge of the state transition probabilities to derive the optimal policy. This feature is important when the number of states is so large that model-based optimization algorithms become infeasible, which is typically the case for a large integrated service network supporting a number of different service types