Evaluation of SINR Prediction in Cellular Networks

Handover based on a predictive approach of signal‐to‐interference‐plus‐noise ratio for heterogeneous cellular networks

“…The RLS algorithm is applied in [2] to select the mobile user's destination node based on projected received signal strength and SINR values. Also, as we reported in [15], the predictive technique based on the RLS algorithm produces the smallest SINR prediction error in comparison with other predictive techniques, such as Taylor series or Markov model.…”

“…The adaptation process is based on the SINR forecast. Therefore, as presented in [15], a predictive technique has to be selected considering the nature of the SINR as follows: • The predictive technique must predict a random process with a continuous value output variable because the SINR is dependant on the randomness of the propagation environment and can be any value. • The predictive technique should function under random processes in discrete time. This is because the SINR is measured at regular intervals. • The predictive technique should take into account the current SINR value for the prediction, but also several past values of SINR. …”

“…In the forecast stage, a sequence of observations, but in this case up to current time n , is used by another FIR filter to predict the observation at the future time : [15]. The prediction is obtained from (2) …”

“…Therefore, in this study, we present a method to adapt to the start of the handover based on SINR prediction. The SINR forecast is carried out by a predictive technique based on the recursive least squares (RLS) algorithm [2, 15].…”

Large Deviation Principle for Empirical SINR Measure of Critical Telecommunication Networks

“…In cellular networks,sinr is a quantity that indicates if a given frequency resource is suitable to properly maintain a communication link. This is the rationale behind the usage insinr in network to monitor the occurrence of radio link and handover failures, see Bastidas-Puga, Galaviz and Andrade [4]. An accuratesinr estimation provides for both a more efficient system and a higher userperceived quality of service.…”