Underwater Acoustic Networks (UANs) play a critical role in marine monitoring and exploration. The integration of mobile nodes can extend the monitoring range and enhance network flexibility and real-time performance. UANs that combine both fixed and mobile nodes typically employ geographic routing protocols and dynamic communication techniques. However, in geographic routing protocols, the uncertainty of the next-hop node leads to several challenges, including access conflicts in the underwater acoustic medium, data collisions, and resource allocation difficulties. Non-Orthogonal Multiple Access (NOMA) technology allows for more users to access the network under limited frequency spectrum, thereby improving network performance. However, due to the complexity of underwater acoustic channels, existing NOMA solutions are difficult to be directly applied in marine environments. Moreover, many existing NOMA techniques rely on centralized control and require real-time global network information, which is challenging to be implemented in UANs. Currently, research on physical-layer and network-layer integrated Medium Access Control (MAC) protocols to address resource allocation and data collision issues remains limited. To address these challenges, this paper proposes the GNC-MAC protocol, compatible with geographic routing protocols and NOMA access techniques. First, we propose a dynamic programming-based grouping algorithm to address the user grouping problem in the uplink of underwater NOMA. Next, to tackle the resource allocation issue, we transform it into a problem of maximizing the sum of the system data transmission rates, and design a distributed dynamic power selection algorithm considering node constraints to improve system throughput. Finally, considering six dynamic factors, including channel quality, energy status, link stability, queue load, node mobility, and interruption probability, we design an adaptive backoff algorithm in the GNC-MAC protocol to optimize next-hop selection in geographic routing protocols and reduce packet collisions. The simulation results indicate that the overall performance of GNC-MAC outperforms existing advanced protocols and offers higher reliability.

Bursty data links can benefit directly from the removal of pilot symbol transmission for channel estimation by improving the spectral efficiency. For such networking scenarios including data or paging signals, blind equalization for joint data detection and channel estimation with few or no pilot can improve spectrum efficiency. Though some existing works typically have attempted to take advantage of the sparsity of multipath channels, substantial performance improvement remains elusive. In this work, we develop an iterative Markov chain Monte Carlo algorithm based on Gibbs sampling designed for sparse channels. We incorporate the channel sparsity in the form of an l1type prior probability distribution, and derive the posterior channel distribution via stochastic sampling. Furthermore, we propose transmitter and receiver structures that could resolve unknown phase ambiguity in frequency-selective channels. This algorithm is also generalizable to non-sparse channels.

This study investigates the blind data detection in two‐way relay networks (TWRN) that employ amplify‐and‐forward (AF) relay strategy. To blindly detect the data in TWRN in the presence of uncertain time‐frequency offsets and phase noise, the authors develop a new Bayesian‐based approximate Gibbs algorithm based on truncated Taylor series expansion approximation. In addition, the authors exploit available constraint information on parameters of interest. The authors present three receivers based on three different parameter estimation approaches. The authors further discuss the implementation issue and present diagnostic convergence analysis. The authors’ numerical results demonstrate the performance and efficacy of their proposed algorithm.

This work investigates joint estimation of symbol timing synchronization and channel response in two-way relay networks (TWRN) that utilize amplify-and-forward (AF) relay strategy. With unknown relay channel gains and unknown timing offset, the optimum maximum likelihood (ML) algorithm for joint timing recovery and channel estimation can be overly complex. We develop a new Bayesian based Markov chain Monte Carlo (MCMC) algorithm in order to facilitate joint symbol timing recovery and effective channel estimation. In particular, we present a basic Metropolis-Hastings algorithm (BMH) and a Metropolis-Hastings-ML (MH-ML) algorithm for this purpose. We also derive the Cramer-Rao lower bound (CRLB) to establish a performance benchmark. Our test results of ML, BMH, and MH-ML estimation illustrate near-optimum performance in terms of mean-square errors (MSE) and estimation bias. We further present bit error rate (BER) performance results.

We present investigation results on joint estimation of symbol timing synchronization and channel response in two-way multiple antenna relay networks that utilize amplify-and-forward (AF) relay strategy. With practically unknown relay channel gains and timing offset, optimum maximum likelihood (ML) estimation for joint timing recovery and channel estimation can be prohibitively complex. We develop a new Bayesian based Markov chain Monte Carlo (MCMC) algorithm and generalize our previous principle to a multiple antenna relay network to accomplish joint symbol timing recovery and effective channel estimation. Simulation results are provided to demonstrate the performance of the proposed algorithm.