Speaker: Saishankar K.P ee09D025
Guide: Dr. Giridhar / Dr. sheetal Kalyani
In cellular broadband access systems such as 3GPP LTE, the User Equipment (UE) feeds back a
quantized rate metric to the Base-station (eNodeB), in order to perform rate adaptation. The periodicity of this rate feedback is fixed so as to minimize the overhead without eroding its benefits. However, between two feedback instants n and n + δ, the actual rate that the UE can correctly decode might change due to the (i) Doppler shift and (ii) change in the active set of interferers. Hence, to fully exploit the benefits of adaptation, an accurate prediction of the attainable rate is required. Since the selected rate is from a set of discrete values, the rate prediction problem is mapped by us onto a discrete sequence prediction problem and we construct higher order Markov models for the discrete sequences using source encoding algorithms. We then propose
two distinct rate-prediction algorithms. One of them is the Adaptive Maximum a Posteriori estimator, while the other is the Adaptive Bayesian risk based estimator. Both of these algorithms simultaneously estimate the best Markov model for each UE and then perform prediction based on the estimated model. Our simulation results using a 3GPP-LTE system simulator reveal a 6% increase in throughput and a 175 % packet loss (PL) reduction over the benchmark scheme, especially when the active set of interferers changes. The proposed prediction algorithms are extremely useful in applications where low PL is desired along with a high throughput.