Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems with Partial CSIT: A Rate-Splitting Approach (1602.09028v2)

Abstract: This paper considers the Sum-Rate (SR) maximization problem in downlink MU-MISO systems under imperfect Channel State Information at the Transmitter (CSIT). Contrary to existing works, we consider a rather unorthodox transmission scheme. In particular, the message intended to one of the users is split into two parts: a common part which can be recovered by all users, and a private part recovered by the corresponding user. On the other hand, the rest of users receive their information through private messages. This Rate-Splitting (RS) approach was shown to boost the achievable Degrees of Freedom (DoF) when CSIT errors decay with increased SNR. In this work, the RS strategy is married with linear precoder design and optimization techniques to achieve a maximized Ergodic SR (ESR) performance over the entire range of SNRs. Precoders are designed based on partial CSIT knowledge by solving a stochastic rate optimization problem using means of Sample Average Approximation (SAA) coupled with the Weighted Minimum Mean Square Error (WMMSE) approach. Numerical results show that in addition to the ESR gains, the benefits of RS also include relaxed CSIT quality requirements and enhanced achievable rate regions compared to conventional transmission with NoRS.

• The paper introduces a rate-splitting strategy that splits messages into common and private parts to enhance robustness against CSIT errors.
• It combines SAA and WMMSE techniques to optimize non-convex sum-rate functions, showing significant ESR gains over conventional methods.
• Numerical results demonstrate improved achievable rates and Degrees of Freedom, underlining RS's capability to mitigate imperfect CSIT effects.

Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems with Partial CSIT: A Rate-Splitting Approach

The paper by Hamdi Joudeh and Bruno Clerckx presents a comprehensive investigation into optimizing sum-rate performance in multiuser Multiple-Input Single-Output (MISO) systems under partial Channel State Information at the Transmitter (CSIT). The paper notably departs from conventional approaches by employing a Rate-Splitting (RS) strategy that divides a user’s message into a common part broadcasted to all users and a private part aimed at the intended recipient. This transmission methodology is shown to buffer against the imperfections in CSIT, resulting in robust Ergodic Sum-Rate (ESR) performance that exceeds traditional methods.

Key highlights of the paper include:

1. Transmission Strategy and Implications: The paper contrasts the RS approach with conventional transmission devoid of rate splitting (NoRS), demonstrating a complete integration of RS with linear precoding and optimization. This is particularly beneficial for combating CSIT uncertainties that typically impair performance.
2. Optimization Techniques: The paper proposes marrying RS strategy with precoder design, leveraging the Sample Average Approximation (SAA) and the Weighted Minimum Mean Square Error (WMMSE) techniques to optimize stochastic rate functions. The paper effectively addresses the non-convexity challenges in sum-rate maximization problems by transforming them into more tractable forms using these methods.
3. Numerical Analysis and Results: Numerical simulations substantiate the robust performance of the RS approach, illustrating consistent ESR gains over NoRS. The paper quantitatively shows that RS reduces CSIT quality requirements and enhances achievable rate regions, even under high Signal to Noise Ratio (SNR) conditions and significant CSIT errors. Notably, it demonstrates a sum Degrees of Freedom (DoF) achieving strategy that capitalizes on RS’s ability to reassign common message power optimally.
4. Algorithmic Development and Convergence: The authors construct a novel algorithm combining SAA with WMMSE, ensuring a convergent solution space for the precoder optimization problem. Furthermore, the paper investigates a conservatively approximated WMMSE method to bypass extensive sampling requirements, recognizing the trade-offs between computational complexity and performance efficacy.
5. Theoretical Contributions and Future Directions: The theoretical advances in characterizing achievable DoF with imperfect CSIT lay foundational insights for exploring RS in more complex systems, such as those involving robust transmission strategies or even massive MIMO contexts.

Overall, the implications of this research move the needle forward in understanding how RS can concretely contribute to enhancing sum-rate performance amid imperfect CSIT. By demonstrating the adaptability and efficacy of RS strategies, this paper not only cements RS's viability but also invites further exploration into its broader applications across various network settings. This research could inspire subsequent advancements in robust and efficient communication strategies in wireless networks, particularly as they transition into more dynamic and error-prone environments.