Adaptive Energy Efficiency Maximization for Cognitive Underwater Acoustic Network under Spectrum Sensing Errors and CSI Uncertainties

Due to the unpredictable presence of Non-Cognitive Users (NCUs) in the time and frequency domains, the number of available channels (i.e., channels where no NCUs exist) and corresponding channel indices per Cognitive User (CU) may differ. In this paper, we propose a heuristic channel allocation method referred to as Enhanced Multi-Round Resource Allocation (EMRRA), which employs the asymmetry of available channels in existing MRRA to randomly allocate a CU to a channel in each round. EMRRA is designed to enhance the overall spectral efficiency and fairness of channel allocation. To do this, the available channel with the lowest redundancy is primarily selected upon allocating a channel to a CU. In addition, when there are multiple CUs with the same allocation priority, the CU with the smallest number of available channels is chosen. We execute extensive simulations in order to investigate the effect of the asymmetry of available channels on CUs and compare the performance of EMRRA to that of MRRA. As a result, in addition to the asymmetry of available channels, it is confirmed that most of the channels are simultaneously available to multiple CUs. Furthermore, EMRRA outperforms MRRA in terms of the channel allocation rate, fairness, and drop rate and has a slightly higher collision rate. In particular, EMRRA can remarkably reduce the drop rate compared to MRRA.

Section: System Model and Asymmetry Of Available Channelsmentioning

confidence: 99%

A Channel Allocation Method Considering the Asymmetry of Available Channels for Centralized Underwater Cognitive Acoustic Networks

Yun

2023

“…In this study, the joint relay and power selection problem is solved by considering the limited feedback of quantized CSI information to obtain the maximum sum rate; In [ 30 ], another joint relay selection and power allocation method was proposed for a UCAN, which considers a trust parameter to overcome imperfect spectrum sensing. In this study, selecting a relay CU and allocating power are determined to maximize the network throughput, and this optimization problem is reduced to the proposed sub-optimal approach; In [ 31 ], the joint parameter optimization of cooperative spectrum sensing time, channel allocation, and power for a UCAN was proposed in order to maximize spectral efficiency and energy efficiency at the same time. The optimal solutions are obtained by alternating direction optimization and Dinkelbach’s optimization; In [ 32 ], a QoS-driven power allocation method for a UCAN was proposed, which helps to allocate a CU into an optimal power by considering the statistical QoS constraints (i.e., delay bounds).…”

Section: Previous Work On Spectrum Sharing For a Ucanmentioning

confidence: 99%

“…In [ 31 ], the joint parameter optimization of cooperative spectrum sensing time, channel allocation, and power for a UCAN was proposed in order to maximize spectral efficiency and energy efficiency at the same time. The optimal solutions are obtained by alternating direction optimization and Dinkelbach’s optimization;…”

Section: Previous Work On Spectrum Sharing For a Ucanmentioning

confidence: 99%

An Underwater Cooperative Spectrum Sharing Protocol for a Centralized Underwater Cognitive Acoustic Network

Yun

2022

To efficiently utilize nonexclusive underwater acoustic frequencies, we propose an Underwater Cooperative Spectrum Sharing (UCSS) protocol for a centralized underwater cognitive acoustic network that mainly consists of two parts. In the first part, to check the random occurrence of interferers periodically, the time domain is divided into frames that consist of a sensing and a non-sensing sub-frame. Then, we set the ratio of the two sub-frames to enhance the sensing rate via simulations. As a result, there exists the upper limit of the ratio, which can be used for determining the proportion of the sensing time within a frame. The second part is to design two heuristic resource allocation (RA) algorithms. One is a multiround RA (MRRA), where a central entity allocates a data channel (i.e., resource) to a CU each round so that multiple rounds are executed until no CUs need to be allocated or there is a lack of data channels. The other is a single-round RA (SRRA), where a CU is allocated to as many data channels as its QoS within a round. We also specify four rules to determine the allocation order of the CUs: random, fixed, high-QoS-based, and low-channel allocation-rate-based. In this study, we investigate the best RA allocation order pair supporting the highest channel allocation rate and fairness index via extensive simulations. It is shown that the MRRA outperformed the SRRA, regardless of allocation orders at any conditions, and the random and low-channel allocation-rate-based allocation orders with MRRA supported the best performance. In particular, even without the optimization process, the MRRA guarantees more than 95% fairness.

“…In the literature, UCAN technologies primarily focus on the efficient allocation of resources such as frequency (or channel), power, or data rate heuristically or optimally [15][16][17][18][19]. However, research on resource allocation or channel access for CUs in a distributed topology has been limited.…”

Section: Introductionmentioning

confidence: 99%

Underwater Multi-Channel MAC with Cognitive Acoustics for Distributed Underwater Acoustic Networks

Yun

2024

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture.