With the proliferation of LEO satellite communications (SatCom) serving rural areas, there is a strong interest on exploring the performance limit (e.g., throughput) with such a service. This problem is challenging due to highly dynamic satellite positions, limited satellite beams and spectrum bandwidth, and wide disparity in number of subscribers across a vast area. Most existing analytical models fail to capture real-world characteristics of operational satellite network, such as long time interval between satellite handover and polarization in transmission. This paper makes a major step in advancing this research area by formalizing an analytical framework for LEO SatCom based on real-world satellite network. Our analytical framework addresses architectural issues such as gateway service region (GSR) and scheduling problems such as satellite/beam/channel-to-cell allocation, interference issues such as co-channel interference avoidance, polarization, and performance issues such as throughput fairness. Simulation results on a real-world satellite ephemeris (Starlink) show that the optimal scheduling solution based on our analytical framework can offer 93% scheduling efficiency while satisfying all design requirements and system constraints.

The O-RAN architecture introduces unprecedented flexibility and openness into modern cellular networks, allowing mix-and-match of components from different vendors and the rapid deployment of innovative solutions across the RAN vertical. Despite its openness, some fundamental technical challenges associated with 5G/Next-G still remain in O-RAN. A well known example is joint optimization of Resource Block (RB) allocation, Modulation and Coding Scheme (MCS) selection, and Beamforming (BF) design. In this paper, we present Savitar—an O-RAN scheduler that jointly optimizes these components, with the objective of minimizing spectrum usage while meeting per-UE probabilistic data rate requirements. Following the multi-timescale design principle in O-RAN, we present three components (each at a different time scale) of Savitar that can be seamlessly integrated with O-RAN RICs: (i) hyperparameter tuning in the Non-Real-Time (Non-RT) RIC, (ii) parallel RB Group (RBG) allocation and MCS selection in the Near-RT RIC, and (iii) BF vector design in the RT Open Distributed Unit (O-DU). A unique design in these components is our handling of CSI uncertainty with limited data samples. Experimental results show that Savitar achieves competitive spectrum efficiency performance while meeting our design requirements (i.e., per-UE probabilistic data rate requirement and real-time requirement in O-DU).

The scarcity of spectrum and the proliferation of data-intensive applications in 5G/Next-G networks call for innovations of new techniques that are capable of offering UE-level data rate guarantee with minimum required spectrum usage. This is a challenging problem due to the complexity of mechanisms involved in the process, such as Resource Block (RB) allocation, modulation and coding scheme (MCS) selection, and MU-MIMO beamforming (BF) design. Further complicating the problem is the random, unknown nature of Channel State Information (CSI) and the errors involved in its estimation. In this paper, we present Rudra, which offers a comprehensive solution to these challenges. Rudra formulates the bandwidth minimization problem by incorporating probabilistic data rate guarantee through a chance constraint, which embeds RB allocation, MCS selection, and MU-MIMO BF mechanisms. The CSI uncertainty problem is addressed through a novel error-embedded (EE)-Wasserstein ambiguity set based on a small set of data samples. We show that the solution by Rudra meets our design objective and outperforms a modified state-of-the-art algorithm.

MU-MIMO beamforming is a key technology for 5G networks, relying on Channel State Information (CSI). However, in practice, the estimated CSI in reality is prone to uncertainty. Further, a MU-MIMO beamforming solution must be derived within a millisecond to be useful for real-time 5G applications. We present ReDBeam—a real-time data-driven beamforming solution for MU-MIMO using limited CSI data samples. The main novelties of ReDBeam are a parallel algorithm and an optimized GPU implementation. ReDBeam delivers a MU-MIMO beamforming solution within 1 millisecond to meet the probabilistic data rate requirements from the users, and minimize a base station’s power consumption. Through extensive experiments, we show that ReDBeam consistently meets the stringent 1-millisecond real-time requirement and is orders of magnitude faster than other state-of-the-art algorithms. ReDBeam conclusively demonstrates that MU-MIMO beamforming with data rate requirements can be achieved in real-time using only limited CSI data samples.

Multiplexing Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) traffic on the same 5G New Radio (NR) air interface poses significant challenges due to extreme latency requirement of URLLC packets. This paper investigates the direct puncturing of URLLC traffic over eMBB transmissions, a method that, while guaranteeing immediate URLLC packet delivery, can severely degrade eMBB performance. To alleviate the adverse impact on eMBB, we present Cyrus—a deep reinforcement learning (DRL)-based puncturing solution for eMBB and URLLC multiplexing. Cyrus is tailored for the Open RAN (O-RAN) architecture and unifies the three control loops of O-RAN synergistically in its design of DRL-based solution. Not only does Cyrus meet the real-time requirements for URLLC but also it continuously updates and improves its scheduling policy based on changing network conditions. The effectiveness of Cyrus is demonstrated through link-level simulations for 5G NR, showing significant improvement in eMBB performance over the state-of-the-art, particularly as URLLC traffic increases.