Optimal User and Beam Selection in Millimeter Wave Networks

Основные понятия

Designing algorithms for optimal UE and beam selection in millimeter wave networks is NP-complete, with proposed solutions achieving near-optimal results.

Аннотация

The article discusses the challenges of selecting user equipment (UE) and beams for concurrent transmissions in millimeter wave networks. It introduces algorithms like MCMC-based and LIG-based UE and beam selection, proving their asymptotic optimality. Greedy algorithms NGUB1 and NGUB2 are also proposed, outperforming prior works. The paper analyzes computational complexity, proposes novel strategies to handle blockage and interference, and presents simulation results showing algorithm superiority.

Статистика

We prove that this problem is NP-complete.
Through simulations, we show that our proposed greedy algorithms outperform the most relevant algorithms proposed in prior work.
Our aim is to design an algorithm for the central controller that uses RSS information and the weights of UEs to maximize the weighted sum rate of UEs.
We propose two asymptotically optimal algorithms to solve this problem.
The volume of data traffic exchanged using wireless networks is increasing rapidly.
To meet this demand, millimeter wave (mmWave) bands can be used.

Цитаты

"We propose two asymptotically optimal algorithms to solve this problem."
"Our proposed greedy algorithms outperform the most relevant algorithms proposed in prior work."
"The volume of data traffic exchanged using wireless networks is increasing rapidly."

Ключевые выводы из

Joint User and Beam Selection in Millimeter Wave Networks

by Santosh Kuma... в arxiv.org 03-19-2024

https://arxiv.org/pdf/2402.07563.pdf

Дополнительные вопросы

How can these UE and beam selection algorithms be implemented practically

The UE and beam selection algorithms proposed in the context can be implemented practically by translating the algorithmic steps into code that can run on the network's central controller. This implementation would involve collecting RSS information from UEs, calculating weighted rates, updating strategies based on MCMC or LIG principles, and making decisions on UE-beam pairs for concurrent transmissions. The practical implementation would require efficient data processing capabilities to handle the large amount of information exchanged between APs, UEs, and the central controller.

What are the potential drawbacks or limitations of these proposed solutions

One potential drawback of these solutions is their computational complexity. The NP-completeness of the problem indicates that finding an optimal solution may require significant computational resources and time. As a result, real-time decision-making in dynamic mmWave networks with changing channel conditions could pose challenges for these algorithms. Additionally, there may be limitations in scalability when dealing with a large number of APs and UEs simultaneously due to increased computation requirements.

How might advancements in mmWave technology impact the effectiveness of these algorithms

Advancements in mmWave technology could impact the effectiveness of these algorithms by providing more accurate and timely data for decision-making. Improved beamforming techniques, better channel estimation methods, and enhanced interference management capabilities could enhance the performance of UE and beam selection algorithms in mmWave networks. Furthermore, advancements in hardware components such as phased array antennas could enable faster beam steering operations leading to quicker adaptation to changing network conditions. These technological advancements could potentially improve the overall efficiency and reliability of these algorithms in future implementations.