A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems

Han, Huimei; Fang, Lushun; Lu, Weidang; Zhai, Wenchao; Liu, Ying; Zhao, Jun

doi:10.1109/jiot.2021.3110793

Cited by 24 publications

(10 citation statements)

References 38 publications

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“…We assume that all of the IoT devices and MEC servers are equipped with one single antenna. By adopting multi-antenna technologies, our work can be extended to multi-antenna scenarios [7,9,22,23]. It is assumed that the system is operated in a time-slotted manner with time-slot length ∆.…”

Section: Network and Channel Modelmentioning

confidence: 99%

Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things

et al. 2022

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Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices’ offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. An optimization problem is formulated to minimize the weighted sum of the computing pressure on the primary MEC server (PMS), the sum of energy consumption of the network, and the task dropping cost. The formulated problem is a mixed integer nonlinear program (MINLP) problem, which is difficult to solve since it contains strongly coupled constraints and discrete integer variables. Taking the dynamic of the environment into account, a deep reinforcement learning (DRL)-based optimization algorithm is developed to solve the nonconvex problem. The simulation results demonstrate the correctness and the effectiveness of the proposed algorithm.

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Section: Network and Channel Modelmentioning

confidence: 99%

Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things

et al. 2022

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“…In MEC systems, edge devices are allowed to offload some or all of the computation tasks to edge servers which releases the limited resources of mobile edge devices. However, due to the time-varying nature of wireless channels [18,19], the mobility of edge devices, and the heterogeneity of computation intensive application tasks, the computation offloading and resource allocation problems remain challenging.…”

Section: Related Workmentioning

confidence: 99%

Offloading Cost Optimization in Multiserver Mobile Edge Computing Systems with Energy Harvesting Devices

Liu

Jiang

et al. 2022

Wireless Communications and Mobile Computing

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In mobile edge computing (MEC) systems with energy harvesting, edge devices are powered by unstable energy harvested from the environment. To prolong the lifetime of edge devices, some computing tasks should be offloaded to MEC servers. However, computing services offered by MEC servers may be very costly. In this work, we aim to minimize total costs caused by computing services and dropping tasks while avoiding the devices running out energy. With the consideration of the unpredictability of the harvestable energy, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make task execution decisions (i.e., local executing, offloading, or dropping tasks) and develop an online algorithm which could help asymptotically obtain the optimal results for the whole system. The algorithm does not require any knowledge of the harvestable energy and the statistics of task arriving processes and can be easily implemented in a distributed manner. Numerical results corroborate that the proposed algorithm can try its best to push battery energy of edge devices to a preset parameter and effectively reduce the service costs and task drops.

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“…With the fast development of Internet of Things (IoT), a huge amount of data is generated everyday [1][2][3], and the increasing pressure on the spectrum deficit and energy consumption problems has attracted a large amount of attention [4,5]. Researchers are exploring novel access schemes for the wireless communications, such as multiple-input-multiple-output (MIMO) [6] and cognitive radio (CR). To improve spectral efficiency under the fixed spectrum assignment policy, CR is proposed as a promising technology by allowing secondary users (SUs) to opportunistically access the spectrum licensed to primary users (PUs) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Time Allocation for Energy Harvesting Cognitive Radio Networks with Multichannel Spectrum Sensing

Liu

Xia

et al. 2022

Wireless Communications and Mobile Computing

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From the perspective of time domain and frequency domain, we investigate the energy harvesting cognitive radio networks (EH-CRNs) with multichannel, where the secondary transmitter (ST) opportunistically accesses the licensed subchannels to transmit packets by consuming the harvested energy. To explore the spectrum holes and improve the lifetime of the EH-CRNs, the ST scavenges energy from the radio-frequency (RF) signal in the wide band during the energy harvesting (EH) phase and exploits the harvested energy for sequential sensing and packet transmission during the rest of the time slot. Under the energy constraint, the secondary throughput is improved by optimizing the time allocation among the EH phase, sensing phase, and transmission phase. We formulate the secondary throughput with respect to the durations of the three phases, prove the existence of the optimal time allocation, and discuss the secondary throughput in three cases of the EH-CRNs. Finally, numerical results validate the theoretical results about the secondary throughput and explore the impacts of key system parameters.

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A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems

Cited by 24 publications

References 38 publications

Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things

Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things

Offloading Cost Optimization in Multiserver Mobile Edge Computing Systems with Energy Harvesting Devices

Optimal Time Allocation for Energy Harvesting Cognitive Radio Networks with Multichannel Spectrum Sensing

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