Collaborative Management of Resource Allocation and Precoding for Dual-Mode Networks

A sub-6 GHz (
$\mu$
Wave) and millimeter wave (mmWave) dual-mode network can deliver high data rates while maintaining dependable coverage. This article proposes a novel resource allocation and precoding algorithm based on collaborative sensing in the dual-mode network. We formulated the collaborative optimization problem of resource allocation and precoding, which maximizes the sum of data rates while meeting minimum acceptable rate requirements and ensuring proportional fairness. Considering the spatial similarity between mmWave channel and
$\mu$
Wave channel, the prior information of mmWave channel predicted by
$\mu$
Wave band is generated to process sensing weight (i.e., subchannel allocation weight of Markov algorithm), which realizes fast dual-mode network resource allocation. Furthermore, a low complexity two-stage precoding scheme for dual-mode networks is proposed based on
$\mu$
Wave spatial information. Low-complexity analog precoding is realized in the first stage based on
$\mu$
Wave spatial information. In the second stage, inspired by the correlation between minimum mean square error (MMSE) and mutual information, this paper proposes an alternating optimization algorithm to find a local weighted sum-rate (WSR) optimum with low complexity. Compared with other heuristic algorithms, simulation results demonstrate the apparent advantages of the proposed collaborative sensing optimization algorithm. Most importantly, it can be seen that we can achieve a performance that approaches an upper bound.