🤖 AI Summary
This paper addresses the optimization of predefined power-level selection probabilities in uncoordinated uplink NOMA systems to minimize block error rate (BLER) or bit error rate (BER).
Method: We propose a generic probabilistic optimization framework that is agnostic to specific multiuser detection algorithms and the number of power levels. It supports iterative solving under multiuser collision scenarios by jointly modeling randomized power allocation, channel statistics, and detection performance—thereby reformulating the problem as a tractable quadratic program and designing an efficient iterative algorithm.
Contribution/Results: Experiments demonstrate that, under the assumption that the base station can concurrently demodulate signals from two or more users, the proposed method significantly reduces BLER/BER, enhances system access efficiency and robustness, and establishes a scalable probabilistic configuration paradigm for uncoordinated random-access NOMA.
📝 Abstract
We consider uncoordinated random uplink non-orthogonal multiple access (NOMA) systems using a set of predetermined power levels. We propose to optimize the probabilities of selection of power levels in order to minimize performance metrics as block error probability (BLEP) or bit error probability (BEP). When the multiuser detection algorithm at the BS treats at most two colliding users’ packets, our optimization problem is a quadratic programming problem. For more colliding users’ packets, we solve the problem iteratively. Our solution is original because it applies to any multiuser detection algorithm and any set of power levels.