Abstract
Machine-type communications (MTC) should account for half the connections to the internet by 2030. The use case massive MTC (mMTC) allows for applications to connect a massive number of low-power and low-complexity devices, leading to challenges in resource allocation. Not only that, mMTC networks suffer under rigid random access schemes due to mMTC ultra-dense nature resulting in poor performance. In this sense, this paper proposes a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -Learning-based random access method for massive machine-type communications, with device clustering and non-orthogonal multiple access (NOMA). The traditional NOMA implementation increases spectral efficiency, but at the same time, demands a larger <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -Table, thus slowing down convergence, which is known to be a highly detrimental effect on massive networks. We use pre-clustering through short-range device-to-device technology to mitigate this drawback, allowing devices to operate with a smaller <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -Table. Furthermore, the previous selection of partner devices allows us to implement a full-feedback-based reward mechanism so that clusters avoid time slots already successfully allocated. Additionally, to cope with the negative impact of system overload, we propose an adaptive frame size algorithm to run in the base station (BS). It allows adjusting the frame size to the network load, preventing idle slots in an underloaded scenario, and providing extra slots when the network is overloaded. The results show the great benefits in terms of throughput of the proposed method. In addition, the impact of the use of clustering and the size of the clusters, as well as the frame size adaptation, are analyzed.
Highlights
5G technology inherently supports critical and massive machine-type communications (MTC) [1]
NOVELTY AND CONTRIBUTION In this work, we propose using Q-Learning and non-orthogonal multiple access (NOMA) with clustering, alongside an adaptive frame size algorithm, to improve the throughput in massive MTC networks
We propose a distributed NOMA Q-Learning random access (RA) method with D2D clustering, a full-feedback-based reward mechanism, and an adaptive frame size algorithm
Summary
Assuming a stand-alone IoT network, we consider a setup with N synchronized devices distributed uniformly around a BS in a single circular cell. Every device has L data packets ready for transmission. Medium access is based on grant free slotted Aloha, where each device can transmit in one of K time slots within a frame. Note that every device has its own transmit power computed via channel inversion considering i) the estimation of its average pathloss using a control message broadcast by the BS between data frames assuming time division duplex reciprocity; and ii) the particular average power ωi which is the intended signal to be received at the BS. C}, share the same time slot and each device transmits at a different power as to yield one of the M possible receive powers at the BS. We exploit D2D communication within each cluster to set up NOMA transmission of up to M devices in the same time slot.
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