DCD-based joint sparse channel estimation for OFDM in virtual angular domain

Massive Multiple Input Multiple Output (MIMO) is a promising technique for communications due to the high data transmission rate. To harvest the benefit from the massive MIMO, it is necessary to have accurate channel estimates. Such channels often exhibit sparsity in the virtual angular domain. This paper proposes a dichotomous coordinate descent (DCD) based algorithm for joint sparse channel estimation in the virtual angular domain for the orthogonal-frequency-division-multiplexing massive MIMO. We show that compared to the distributed sparsity adaptive matching pursuit algorithm previously proposed for this purpose, the DCD-based algorithm has significantly lower complexity and better channel estimation performance

LTDA-MAC v2.0 : Topology-Aware Unsynchronized Scheduling in Linear Multi-Hop UWA Networks

This paper investigates the use of underwater acoustic sensor networks (UASNs) for subsea asset monitoring. In particular, we focus on the use cases involving the deployment of networks with line topologies, e.g., for monitoring oil and gas pipelines. The Linear Transmit Delay Allocation MAC (LTDA-MAC) protocol facilitates efficient packet scheduling in linear UASNs without clock synchronization at the sensor nodes. It is based on the real-time optimization of a packet schedule for a given network deployment. In this paper, we present a novel greedy algorithm for real-time optimization of LTDA-MAC schedules. It produces collision-free schedules with significantly shorter frame duration, and is 2–3 orders of magnitude more computationally efficient than our previously proposed solution. Simulations of a subsea pipeline monitoring scenario show that, despite no clock synchronization, LTDA-MAC equipped with the proposed schedule optimization algorithm significantly outperforms Spatial TDMA