Optimizing Beamforming, User Admission, and Discrete/Continuous Rates for Rate-Splitting Multiple Access with Imperfect Successive Interference Cancellation

Core Concepts

The core message of this article is to develop efficient radio resource management (RRM) algorithms for rate-splitting multiple access (RSMA) systems that account for practical characteristics such as discrete transmission rates, user admission, and imperfect successive interference cancellation (SIC). The proposed algorithms jointly optimize the beamforming, user admission, and discrete/continuous rates to maximize the weighted sum rate and weighted energy efficiency of RSMA.

Abstract

This paper investigates the RRM design for RSMA, considering various practical characteristics of wireless systems. Specifically, the authors formulate optimization problems to maximize the weighted sum rate (WSR) and weighted energy efficiency (WEE) of RSMA, jointly optimizing the beamforming, user admission, and discrete/continuous rates, while accounting for imperfect SIC.
The key highlights and insights are:

Formulation of nonconvex mixed-integer nonlinear programs (MINLPs) for WSR and WEE maximization, denoted as P'DWSR and P'DWEE, respectively, which account for discrete rates, user admission, and imperfect SIC.

Proposal of an optimal mixed-integer second-order cone program (OPT-MISOCP) algorithm to solve the nonconvex MINLPs P'DWSR and P'DWEE by transforming them into convex MISOCPs PDWSR and PDWEE, which can be solved globally optimal.

Formulation of continuous-rate counterparts Q'CWSR and Q'CWEE of the discrete-rate problems, and development of a binary enumeration and convex transformation-based algorithm to solve them.

Simulation results show that accounting for discrete rates, user admission, and imperfect SIC can lead to significant gains of up to 89.7% in WSR and 21.5% in WEE compared to the case of continuous rates and perfect SIC.

The proposed algorithms feature customized cutting planes that reduce the runtime by a factor of 3-20 without impacting performance.

Overall, the paper demonstrates the importance of considering practical characteristics in the RRM design of RSMA to fully exploit the potential of this promising multiple access technology.

Stats

The authors report the following key figures:

Gains of up to 89.7% in WSR and 21.5% in WEE by accounting for discrete rates, user admission, and imperfect SIC, compared to continuous rates and perfect SIC.
Additional gains of up to 15.3% in WSR and 11.4% in WEE by optimizing user admission, compared to random user admission.
Acceleration of the optimization runtime by a factor of 3-20 using customized cutting planes.

Quotes

"Failure to consider these characteristics in RRM design may lead to inefficient use of radio resources."
"By considering that transmission rates are discrete, the transmit power can be utilized more intelligently, allocating just enough power to guarantee a given discrete rate."
"User admission plays a crucial role in RSMA, enabling additional gains compared to random admission by facilitating the servicing of selected users with mutually beneficial channel characteristics."
"Provisioning for possibly imperfect SIC makes RSMA more robust and reliable."

Key Insights Distilled From

Radio Resource Management Design for RSMA: Optimization of Beamforming, User Admission, and Discrete/Continuous Rates with Imperfect SIC

by L. F. Abanto... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19611.pdf

Deeper Inquiries

How can the proposed RRM algorithms be extended to incorporate additional practical constraints, such as limited backhaul capacity or quality-of-service requirements

To extend the proposed RRM algorithms to incorporate additional practical constraints, such as limited backhaul capacity or quality-of-service requirements, several adjustments can be made to the optimization framework.

Limited Backhaul Capacity:

Introduce constraints on the total amount of data that can be transmitted over the backhaul link, considering the bandwidth and latency limitations.
Include backhaul capacity constraints in the optimization problem to ensure that the total data rate of all admitted users does not exceed the available backhaul capacity.
Modify the objective function to account for minimizing backhaul usage while maximizing the overall system performance.

Quality-of-Service Requirements:

Incorporate QoS metrics such as latency, packet loss, and jitter into the optimization problem as constraints to guarantee a certain level of service quality for each user.
Define QoS thresholds for different users and include them in the objective function to optimize the resource allocation while meeting the QoS requirements.

By integrating these practical constraints into the RRM design, the algorithms can be tailored to meet specific operational needs and ensure efficient resource utilization while maintaining service quality standards.

What are the potential implications of the discrete-rate and user admission optimizations on the complexity and implementation of RSMA receivers

The optimization of discrete rates and user admission in RSMA can have significant implications on the complexity and implementation of RSMA receivers:

Complexity:

Discrete Rate Optimization: Implementing discrete rates requires additional processing to select the most suitable rate for each user based on channel conditions and system constraints. This adds computational complexity to the receiver design.
User Admission Optimization: Selecting the optimal set of users to serve based on channel characteristics and mutual interference considerations increases the decision-making complexity at the receiver.

Implementation:

Discrete Rate Optimization: Implementing discrete rates in RSMA receivers involves mapping the continuous channel conditions to the closest feasible discrete rate, which requires efficient algorithms for rate selection and adaptation.
User Admission Optimization: The receiver needs to dynamically adjust the user set based on changing channel conditions and system requirements, requiring robust admission control mechanisms.

Overall, while the optimizations enhance the performance of RSMA systems, they also introduce additional computational overhead and decision-making complexity to the receiver design.

Can the insights from this work be applied to other non-orthogonal multiple access schemes, such as NOMA, to improve their performance in practical deployments

The insights gained from the RRM design for RSMA, particularly in optimizing discrete rates, user admission, and imperfect SIC, can be applied to other non-orthogonal multiple access schemes like NOMA to improve their performance in practical deployments:

Discrete Rate Optimization:

Applying the concept of discrete rate optimization to NOMA can help in selecting the most suitable modulation and coding schemes for users based on their channel conditions, leading to improved spectral efficiency.
By considering predefined MCSs and discrete rates, NOMA systems can allocate resources more efficiently and adaptively, enhancing overall system performance.

User Admission Optimization:

User admission strategies developed for RSMA can be adapted to NOMA to select users with mutually beneficial channel characteristics, maximizing the system capacity and throughput.
By intelligently admitting users based on their channel conditions and interference levels, NOMA systems can achieve better resource utilization and interference management.

By leveraging the optimization techniques and insights from RSMA RRM design, NOMA systems can enhance their performance, robustness, and efficiency in real-world deployments.