Abstract
5G is expected to deal with high data rates for different types of wireless traffic. To enable high data rates, 5G employs beam searching operation to align the best beam pairs. Beam searching operation along with high order modulation techniques in 5G, exhausts the battery power of user equipment (UE). LTE network uses discontinuous reception (DRX) with fixed sleep cycles to save UE energy. LTE-DRX in current form cannot work in 5G network, as it does not consider multiple beam communication and the length of sleep cycle is fixed. On the other hand, artificial intelligence (AI) has a tendency to learn and predict the packet arrival-time values from real wireless traffic traces. In this paper, we present AI based DRX (AI-DRX) mechanism for energy efficiency in 5G enabled devices. We propose AI-DRX algorithm for multiple beam communications, to enable dynamic short and long sleep cycles in DRX. AI-DRX saves the energy of UE while considering delay requirements of different services. We train a recurrent neural network (RNN) on two real wireless traces with minimum root mean square error (RMSE) of 5 ms for trace 1 and 6 ms for trace 2. Then, we utilize the trained RNN model in AI-DRX algorithm to make dynamic short or long sleep cycles. As compared to LTE-DRX, AI-DRX achieves 69 % higher energy efficiency on trace 1 and 55 % more energy efficiency on trace 2, respectively. The AI-DRX attains 70 % improvement in energy efficiency for trace 2 compared with Poisson packet arrival model for λ = 1 / 20 .
Highlights
The use of cellular gadgets, like smartphones, notebooks, and tablets has comforted our life.The Ericsson mobility report predicts the rise of cellular traffic to 8.8 billion by 2024 [1]
If no new packet arrives before the completion of short sleep timer, the user equipment (UE) switches to the long sleep cycle and remains there until intimation of the new packet is received
We have suggested an artificial intelligence (AI)-based DRX mechanism for energy saving in multiple beams communications scenario
Summary
The use of cellular gadgets, like smartphones, notebooks, and tablets has comforted our life. Authors in [3] propose DRX for 5G network, which requires UE to search for best beam pairs after completion of each sleep cycle in order to serve the packets. Their approach considers the beam training process only in case of beam misalignment and after completion of long sleep duration. Based on the prediction results, we propose an AI-DRX algorithm that works on a ten-state DRX model to enable energy saving. AI-DRX for multiple beam communications in 5G network saves the UE energy by enabling dynamic short and long sleep cycles, respectively.
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