Random neural network based cognitive engines for adaptive modulation and coding in LTE downlink systems

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This paper presents two random neural network (RNN) based context-aware decision making frameworks to improve adaptive modulation and coding (AMC) in long-term evolution (LTE) downlink systems. In the first framework, AMC is modelled as a traditional classification problem with the aim to maximize the probability of correct classification. The second framework seeks to optimize the throughput as opposed to simply maximizing the probability of the correct classification. To model the second framework, we developed a hybrid cognitive engine (CE) architecture by integrating an RNN based learning algorithm with genetic algorithm (GA) based reasoning. RNN inherent properties help CE to comply with the essential CE design requirement (i.e. concurrent long-term-learning, low computational complexity, and fast decision making). The performance of RNN is compared with artificial neural networks (ANN) and state-of-the-art effective exponential SINR mapping (EESM) algorithm. A comprehensive analysis of the proposed RNN based AMC scheme is presented by jointly incorporating the effect of different schedulers, feedback delays, and multi-antenna diversity on the throughput of an orthogonal frequency-division multiple access (OFDMA) system. The critical analysis of the first framework revealed that RNN based CE can achieve comparable results with faster adaptation, even in severe environment changes without the need of retraining compared to ANN. The analysis of the second approach demonstrated RNNs faster adaptation as compared to ANN and showed upto 253% gain in user throughput. RNN based CE efficiently exploited the channel quality information feedback delay to improve system throughput and helped cell-edge and cell-centre users to experience much better services in terms of achieved throughput as compared to EESM.