Abstract
As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.
Highlights
Cellular standardization organizations such as 3GPP are actively working towards the stages of the long term evolution (LTE) standard in order to support machine-type communication (MTC), known as LTE-M
Adaptive modulation and coding scheme (MCS) selection and resource planning in LTE-M based Internet of things (IoT) sensing platform funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
We propose an adaptive MCS selection (AMCS) mechanism where the sensing node adaptively updates the channel quality indicator (CQI) and power headroom report (PHR) to the femto eNB (FeNB) with respect to the size of the sensing data in order to achieve an optimal pair of MCS and #PRBs
Summary
Cellular standardization organizations such as 3GPP (the third generation partnership project) are actively working towards the stages of the long term evolution (LTE) standard in order to support machine-type communication (MTC), known as LTE-M. Adaptive MCS selection and resource planning in LTE-M based IoT sensing platform these optimal MCS levels of sensing nodes as the criteria to re-plan available channels among the neighboring FeNBs instead of directly considering the channel interference among them. Based on a focused consideration on these optimal MCS levels, the quantity-oriented resource planning (QORP) algorithm better adapts to the sensing node requirements and achieves a higher channel reuse ratio among FeNBs.
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