Future machine-to-machine (M2M) communications need to support a massive number of devices communicating with each other with little or no human intervention. Random access techniques were originally proposed to enable M2M multiple access, but suffer from severe congestion and access delay in an M2M system with a large number of devices. In this paper, we propose a novel multiple access scheme for M2M communications based on the capacity-approaching analog fountain code to efficiently minimize the access delay and satisfy the delay requirement for each device. This is achieved by allowing M2M devices to transmit at the same time on the same channel in an optimal probabilistic manner based on their individual delay requirements. Simulation results show that the proposed scheme achieves a near optimal rate performance and at the same time guarantees the delay requirements of the devices. We further propose a simple random access strategy and characterize the required overhead. Simulation results show that the proposed approach significantly outperforms the existing random access schemes currently used in long term evolution advanced (LTE-A) standard in terms of the access delay.
Recently, a systematic framework has been developed in @cite_22 @cite_30 to understand the fundamental limits of M2M communications in terms of power efficiency and throughput. However, they did not provide a systematic approach to develop an efficient communication protocol to approach these limits. Here, we consider a realistic model for M2M communications, which supports both regular and random traffics with different delay and service requirements. We develop a practical transmission scheme for M2M communications based on recently proposed analog fountain codes (AFCs) @cite_16 to enable massive number of devices communicating with a common base station (BS) while satisfying QoS requirements of all devices. We further show that the proposed scheme can closely approach the fundamental limits of M2M communications in terms of throughput and can satisfy the delay requirements of all devices at the same time. The main contributions of this paper are summarized next.
@cite_30 @cite_16 @cite_22
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Future machine-to-machine (M2M) communications need to support a massive number of devices communicating with each other with little or no human intervention. Random access techniques were originally proposed to enable M2M multiple access, but suffer from severe congestion and access delay in an M2M system with a large number of devices. In this paper, we propose a novel multiple access scheme for M2M communications based on the capacity-approaching analog fountain code to efficiently minimize the access delay and satisfy the delay requirement for each device. This is achieved by allowing M2M devices to transmit at the same time on the same channel in an optimal probabilistic manner based on their individual delay requirements. Simulation results show that the proposed scheme achieves a near optimal rate performance and at the same time guarantees the delay requirements of the devices. We further propose a simple random access strategy and characterize the required overhead. Simulation results show that the proposed approach significantly outperforms the existing random access schemes currently used in long term evolution advanced (LTE-A) standard in terms of the access delay. Supporting massive device transmission is challenging in machine-to-machine (M2M) communications. Particularly, in event-driven M2M communications, a large number of devices become activated within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for random access control of M2M communication systems with bursty traffic. Instead of the observation in a single slot, the statistics of consecutive idle and collision slots are used in FASA to accelerate the tracking process of network status that is critical for optimizing S-ALOHA systems. With a design based on drift analysis, the estimate of the number of the active devices under FASA converges fast to the true value. Furthermore, by examining the T-slot drifts, we prove that the proposed FASA scheme is stable as long as the average arrival rate is smaller than e-1, in the sense that the Markov chain derived from the scheme is geometrically ergodic. Simulation results demonstrate that under highly bursty traffic, the proposed FASA scheme outperforms traditional additive schemes such as PB-ALOHA and achieves near-optimal performance in reducing access delays. Moreover, compared to multiplicative schemes, FASA shows its robustness under heavy traffic load in addition to better delay performance. Machine-to-machine (M2M) communications have many applications, such as in smart grid, health monitoring alerting, and intelligent transportation. Due to unique characteristics that are different from the traditional human-to-human communications, numerous design challenges have to be tackled in M2M. A critical issue is that of dealing with the massive access of machine-type communication (MTC) devices while guaranteeing the desired quality of service (QoS). In this paper, we group MTC devices into clusters based on their QoS requirement. By utilizing the time-controlled M2M feature, we formulate the problem of massive access with QoS guarantee as a queuing problem. We derive an overflow probability estimation model by applying a large deviation principle and then propose an online-measurement-based adaptive massive access management for deciding the amount of resources allocated for each cluster. The proposed method bases the decision on the observed traffic workload with no prior knowledge of its statistics. We conduct the simulations in multicluster scenarios with distinct maximum tolerable delay and corresponding acceptable probability in a long-term evolution system. The simulation results show the adaptability of the proposed massive access management regime to the variations in the traffic workload; thus, it is capable of accommodating dynamic propagation conditions and unknown traffic characteristics, particularly in burst-by-burst communications. The growing popularity of Machine-to-Machine (M2M) communications in cellular networks is driving the need to optimize networks based on the characteristics of M2M, which are significantly different from the requirements that current networks are designed to meet. First, M2M requires large number of short sessions as opposed to small number of long lived sessions required by the human generated traffic. Second, M2M constitutes a number of battery operated devices that are static in locations such as basements and tunnels, and need to transmit at elevated powers compared to the traditional devices. Third, replacing or recharging batteries of such devices may not be feasible. All these differences highlight the importance of a systematic framework to study the power and energy optimal system design in the regime of interest for M2M, which is the main focus of this paper. For a variety of coordinated and uncoordinated transmission strategies, we derive results for the optimal transmit power, energy per bit, and the maximum load supported by the base station, leading to the following design guidelines: (i) frequency division multiple access (FDMA), including equal bandwidth allocation, is sum-power optimal in the asymptotically low spectral efficiency regime, (ii) while FDMA is the best practical strategy overall, uncoordinated code division multiple access (CDMA) is almost as good when the base station is lightly loaded, (iii) the value of optimization within FDMA is not significant in the regime of interest for M2M.