Joint machine-type device selection and power allocation for buffer-aided cognitive M2M communication

Darabi, Mostafa; Maham, Behrouz; Saad, Walid; Mehbodniya, Abolfazl; Adachi, Fumiyuki

doi:10.1109/pimrc.2015.7343679

Cited by 3 publications

(1 citation statement)

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“…In literature, various techniques have been discussed for the spectrum sharing in cellular networks, such as cognitive radio [20], [21], Wi-Fi offloading [22], [23], LTE-U [24]- [26], and LTE-Licensed Assisted Access (LTE-LAA) [27], [28]. In [20], [21], a buffer-aided cognitive M2M communication network was considered, where the M2M communications share the spectrum with the cellular network in a cognitive manner. In [22], [23], Wi-Fi access points (APs) was deployed for cellular networks to offload the mobile data traffic to the unlicensed spectrum.…”

Section: Introductionmentioning

confidence: 99%

IoT-U: Cellular Internet-of-Things Networks Over Unlicensed Spectrum

Zhang

Bian

et al. 2019

IEEE Trans. Wireless Commun.

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In this paper, we consider an uplink cellular Internet-of-Things (IoT) network, where a cellular user (CU) can serve as the mobile data aggregator for a cluster of IoT devices. To be specific, the IoT devices can either transmit the sensory data to the base station (BS) directly by cellular communications, or first aggregate the data to a CU through Machine-to-Machine (M2M) communications before the CU uploads the aggregated data to the BS. To support massive connections, the IoT devices can leverage the unlicensed spectrum for M2M communications, referred to as IoT Unlicensed (IoT-U). Aiming to maximize the number of scheduled IoT devices and meanwhile associate each IoT devices with the right CU or BS with the minimum transmit power, we first introduce a single-stage formulation that captures these objectives simultaneously. To tackle the NP-hard problem efficiently, we decouple the problem into two subproblems, which are solved by successive linear programming and convex optimization techniques, respectively. Simulation results show that the proposed IoT-U scheme can support more IoT devices than that only using the licensed spectrum. Index TermsInternet-of-Things Unlicensed, Carrier aggregation, Machine-to-Machine Communication, Non-convex optimization The authors are with National ). 3 systems. Different from the long-range communication techniques in unlicensed bands for IoT networks such as LoRa [13], which builds the network upon the IEEE 802.15.4 infrastructure with a mesh topology [14], IoT-U system requires the assist and control from the central BS. However, the spectrum utilization brings new challenges to the scheduling of IoT devices in the IoT-U network. First, a suitable coexistence mechanism of the IoT-U and Wi-Fi systems is required due to the opportunistic feature of unlicensed channel access [15]. Second, the interference management among CUs, IoT devices and Wi-Fi users (WUs) becomes more complicated. To tackle the first challenge, we utilize a duty cycle [16] based protocol to share the unlicensed spectrum fairly. To cope with the second one, we optimize the association, scheduling, and power allocation to maximize the weighted scheduled number of IoT devices with the lowest power consumption. This problem is a mixed-integer non-linear programming (MINLP) problem, which is generally NP-hard. To solve this problem efficiently, we decouple it into two subproblems, i.e., IoT devices association and scheduling subproblem, and power allocation subproblem. For the first subproblem, we convert the non-linear constraints into linear ones and solved the transformed problem by the branch-and-bound algorithm [17]. For the second subproblem, we approximate the non-convex functions into a series of convex ones by successive convex approximation (SCA) [18] and solve them by existing convex techniques [19]. In literature, various techniques have been discussed for the spectrum sharing in cellular networks, such as cognitive radio [20], [21], Wi-Fi offloading [22], [23], LTE-U [24]-[26], and LTE-Licensed A...

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