Ka-band link optimization with rate adaptation

On-going development of Ka-band capability for the deep space networks (DSN) will radically increase the bandwidth available to support advanced mission concepts envisioned for future robotic as well as human exploration of Mars and beyond. While Ka-band links can operate at much higher data rate than X-band, they are much more susceptible to fluctuating weather conditions and manifest a significant trade-off between throughput and availability. If the operating point is fixed, the maximum average throughput for deep space Ka-band link is achieved at about 80 percent availability, i.e., weather-related outages will occur about 20 percent of the time. Low availability increases the complexity of space mission operation, while higher availability would require additional link margins that lowers the overall throughput. To improve this fundamental throughput-availability tradeoff, data rate adaptation based on real-time observation of the channel condition is necessary. In this paper, we model the Ka-band channel using a Markov process to capture the impact of the temporal correlation in weather conditions. We then develop a rate adaptation algorithm to optimize the data rate based on real time feedback on the measured channel conditions. Our algorithm achieves both higher throughput and link availability as compared to the constant rate scheme presently in use