We propose a deep Q-learning based algorithm that efficiently minimizes the overall energy consumption by taking into account both the energy consumption from transmission and overheads, and various network information such as channel conditions and causal association information. In this paper, we consider the problem of decision making in the context of a dense heterogeneous network with a macro base station and multiple small base stations. All the measurement resultsĪre evaluated using computer programs built on MATLAB platform. RSSI and RSRP, better is the RSRQ – each of which is consistent with theory. Yields some useful information such as: if the SINR is good for a measurement slot, higher throughput isĪchieved RSRP and SNR are proportional to each other on average and lesser is the difference between Handover events occurred during the test period within the test area are studied.

RSRP, RSSI and RSRQ as well as to evaluate the effects of SNR on throughput. Measurement tool namely NEMO Handy are analysed to verify the possible relationships among SINR, Some practical measurement results recorded from a live LTE network of Australia using a commercial Intra-eUTRAN (evolved Universal Terrestrial Random Access Network) handover in LTE. A measurement ofĬhannel quality represented by Signal to Interference plus Noise Ratio (SINR) is used for link adaptationĪlong with packet scheduling, whereas RSRP and RSRQ are needed for making handover decision during Received Quality (RSRQ), and Carrier Received Signal Strength Indicator (RSSI).

The four basic Radio Resource Management (RRM) measurements in Long Term Evolution (LTE) systemĪre Channel Quality Indicator (CQI), Reference Signal Received Power (RSRP), Reference Signal

Rsrp and rsrq