Sum Rate Optimization Problem Research Articles

Optimal Resource Allocation Scheme for Satisfying the Data Rate Requirement in Hybrid Network of D2D-Cellular

In this paper, an optimal wireless resources allocation scheme is put forward to improve the performance for both cellular and Device-to-Device (D2D) users in a hybrid network. The resource unit and power allocation can be modeled into a sum-rate optimization problem with the constraint of power limitation and rate ratio. The rate ratio is used to measure the data rate requirements from both cellular and D2D users. By using the Lagrange Multiplier Method, we derived out the optimal formula about allocating the power and resource unit for both cellular and D2D users. According to the optimal formula, the relationship between the transmission power and resource unit allocation is mutually conditional. Then the resource allocation scheme algorithm was given. And the power allocation can be resolved by the water-filling algorithm. Simulation results show that the proposed scheme improves the sum-throughput significantly as well as the fairness.

Near-Optimal Waveform Design for Sum Rate Optimization in Time-Reversal Multiuser Downlink Systems

Utilizing channel reciprocity, the traditional time-reversal technique boosts the signal-to-noise ratio at the receiver with very low transmitter complexity. However, the large delay spread gives rise to severe inter-symbol interference (ISI) when the data rate is high, and the achievable transmission rate is further degraded in the multiuser downlink due to the inter-user interference (IUI). In this work, we study the weighted sum rate optimization problem by means of waveform design in the time-reversal multiuser downlink where the receiver processing is based on a single sample. Power allocation has a significant impact on the waveform design problem. We propose a new power allocation algorithm named Iterative SINR Waterfilling, which is able to achieve comparable sum rate performance to that of globally optimal power allocation. We further propose another approach called Iterative Power Waterfilling for multiple data streams. Iterative SINR Waterfilling provides better performance than Iterative Power Waterfilling in the scenario of high interference, while Iterative Power Waterfilling can work under multiple data streams. Simulation results show the superior performance of the proposed algorithms in comparison with other waveform designs such as zero-forcing and conventional time-reversal waveform.