Maximizing System Sum Throughput in Wireless-Powered NOMA Networks with Movable Antennas

Concepts de base

The core message of this article is to maximize the system sum throughput in a wireless-powered NOMA network by jointly optimizing the positions of movable antennas at the hybrid access point and the wireless devices, the time allocation, and the uplink power allocation.

Résumé

This paper investigates a movable antenna (MA)-enabled wireless-powered communication network (WPCN), where multiple wireless devices (WDs) first harvest energy from the downlink (DL) signal broadcast by a hybrid access point (HAP) and then transmit information in the uplink (UL) using non-orthogonal multiple access (NOMA). Unlike conventional WPCNs with fixed-position antennas (FPAs), this MA-enabled WPCN allows the MAs at the HAP and the WDs to adjust their positions twice: once before DL wireless power transfer and once before DL wireless information transmission.

The key highlights and insights are:

1. The authors prove that using identical MA positions for both DL and UL is the optimal strategy in both continuous and discrete positioning designs, thereby greatly simplifying the problems and enabling easier practical implementation of the system.

2. For the continuous positioning design, the authors propose an alternating optimization-based algorithm to obtain suboptimal solutions, where each position variable is not explicitly exposed in the objective function but can be efficiently solved using the successive convex approximation technique.

3. For the discrete positioning design, the authors apply the alternating optimization method and leverage the special structure of the subproblems to streamline the exhaustive search process for the binary position variables, significantly reducing the computational complexity.

4. Simulation results show that the proposed continuous MA scheme can enhance the sum throughput by up to 395.71% compared to the benchmark with FPAs, even when additional compensation transmission time is provided to the latter. Moreover, a step size of one-quarter wavelength for the MA motion driver is generally sufficient for the proposed discrete MA scheme to achieve over 80% of the sum throughput performance of the continuous MA scheme.

5. When each moving region is large enough to include multiple optimal positions for the continuous MA scheme, the discrete MA scheme can achieve comparable sum throughput without requiring an excessively small step size.

Stats

The system sum throughput is given by Rcont_sum = τ3 log2(1 + Σ_k=1^K pk |hk,2(ω2, uk,2)|^2 / σ^2) for the continuous positioning design, and Rdisc_sum = τ3 log2(1 + Σ_k=1^K pk |hk,2({sm_2}, {tn_k,2})|^2 / σ^2) for the discrete positioning design.

Citations

"the optimum is achieved when the MA positions are identical for both DL WPT and UL WIT."
"a step size of one-quarter wavelength for the MA motion driver is generally sufficient for the proposed discrete MA scheme to achieve over 80% of the sum throughput performance of the continuous MA scheme."
"when each moving region is large enough to include multiple optimal positions for the continuous MA scheme, the discrete MA scheme can achieve comparable sum throughput without requiring an excessively small step size."

Idées clés tirées de

Movable Antennas Enabled Wireless-Powered NOMA: Continuous and Discrete Positioning Designs

by Ying Gao, Qi... à arxiv.org 10-01-2024

https://arxiv.org/pdf/2409.20485.pdf

Questions plus approfondies

How can the proposed movable antenna schemes be extended to multi-cell wireless-powered NOMA networks to further improve the system performance?

The proposed movable antenna (MA) schemes can be extended to multi-cell wireless-powered non-orthogonal multiple access (NOMA) networks by implementing a coordinated approach to antenna positioning and resource allocation across multiple cells. In a multi-cell environment, each hybrid access point (HAP) can utilize MAs to dynamically adjust their positions based on the channel conditions and user distributions in neighboring cells. This coordination can enhance the overall system performance by:

Inter-Cell Interference Management: By optimizing the positions of MAs in each cell, the system can mitigate inter-cell interference, which is a significant challenge in multi-cell NOMA networks. MAs can be repositioned to focus energy and information transmission towards users with favorable channel conditions, thereby improving signal quality and throughput.

Dynamic Resource Allocation: The joint optimization of MA positions and resource allocation (time and power) across multiple cells can lead to better energy harvesting and information transmission efficiency. By sharing information about user locations and channel states, cells can adaptively allocate resources to maximize the sum throughput of the entire network.

User Clustering: MAs can facilitate the formation of user clusters within and across cells, allowing for more effective NOMA strategies. By adjusting their positions, MAs can help group users with similar channel conditions, enabling more efficient power allocation and enhancing user fairness.

Enhanced Energy Harvesting: In a multi-cell setup, MAs can be strategically positioned to optimize energy harvesting from the HAPs in adjacent cells. This can lead to improved energy availability for users, allowing them to transmit information more reliably.

Scalability and Flexibility: The flexibility of MAs allows for scalable solutions in multi-cell networks. As the number of users or cells increases, MAs can be repositioned to adapt to changing network conditions, ensuring sustained performance improvements.

What are the potential challenges and practical considerations in implementing the continuous and discrete movable antenna positioning designs in real-world wireless-powered communication systems?

Implementing continuous and discrete movable antenna positioning designs in real-world wireless-powered communication systems presents several challenges and practical considerations:

Hardware Limitations: Continuous positioning requires advanced mechanical systems capable of precise movements, which can be costly and complex to maintain. Discrete positioning, while simpler, may not achieve the same performance levels as continuous systems, especially if the step size is not adequately small.

Control Algorithms: Developing robust control algorithms for real-time positioning of MAs is critical. These algorithms must account for dynamic channel conditions, user mobility, and interference, which can complicate the implementation process.

Latency and Timing: The time required for MAs to reposition can introduce latency in the communication process. In scenarios where rapid adjustments are necessary, such as in high-mobility environments, this latency can negatively impact system performance.

Energy Consumption: The energy consumed by the motors or actuators used to reposition the antennas must be considered. Continuous movement may lead to higher energy consumption, potentially offsetting the benefits gained from improved communication performance.

Environmental Factors: Real-world environments can introduce variability in channel conditions due to obstacles, reflections, and other factors. MAs must be able to adapt to these changes dynamically, which may require sophisticated sensing and feedback mechanisms.

Cost and Complexity: The integration of MAs into existing wireless-powered communication systems can increase the overall system complexity and cost. This includes not only the physical hardware but also the necessary software and algorithms for effective operation.

Regulatory and Safety Concerns: Depending on the application, there may be regulatory considerations regarding the use of movable antennas, particularly in terms of safety and compliance with communication standards.

Can the insights gained from this work on the performance tradeoffs between continuous and discrete movable antenna positioning be applied to other wireless communication scenarios beyond wireless-powered NOMA networks?

Yes, the insights gained from the performance tradeoffs between continuous and discrete movable antenna positioning can be applied to various other wireless communication scenarios beyond wireless-powered NOMA networks. These insights can enhance system performance in several ways:

General Wireless Networks: The principles of optimizing antenna positioning to improve signal quality and throughput can be applied to traditional wireless networks, including cellular, Wi-Fi, and satellite communications. Continuous and discrete positioning strategies can help mitigate interference and enhance user experience.

Massive MIMO Systems: In massive multiple-input multiple-output (MIMO) systems, the ability to dynamically adjust antenna positions can significantly improve channel capacity and energy efficiency. The findings from movable antenna research can inform strategies for antenna selection and positioning in these systems.

Internet of Things (IoT): In IoT applications, where numerous devices may have varying channel conditions, movable antennas can be used to optimize communication links. The insights on resource allocation and positioning can help improve energy harvesting and data transmission efficiency in IoT networks.

Vehicular Networks: In vehicular ad-hoc networks (VANETs), where vehicles are in constant motion, the ability to reposition antennas can enhance communication reliability and reduce latency. The tradeoffs between continuous and discrete positioning can guide the design of adaptive communication systems in such dynamic environments.

Smart Cities: In smart city applications, where multiple communication technologies coexist, the insights from movable antenna positioning can help optimize resource allocation and improve overall network performance, ensuring efficient communication among various devices and systems.

Emergency Response Systems: In scenarios requiring rapid deployment of communication systems, such as disaster recovery, movable antennas can be crucial for establishing reliable links. The findings can inform the design of flexible communication solutions that adapt to changing conditions.

In summary, the research on movable antennas provides valuable insights that can be leveraged across a wide range of wireless communication scenarios, enhancing performance and efficiency in diverse applications.