Energy Efficient Massive MIMO 5G System with ZF Receiver

Hossain, Tamanna; Ali, Md. Yeakub; Mowla, Md. Munjure

doi:10.1109/icecte48615.2019.9303583

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Since the earliest experimental system of MIMO was proposed by researchers in the Bell laboratory in the 19th century, it has received widespread attention from researchers all over the world. In addition, in terms of its ability to accommodate plenty of users at the same time, the advantages of effectively improving the spectral efficiency (SE) and the capacity of the system have been proven [7], while with the explosive growth of global traffic data [8], researchers are no longer satisfied with the performance improvement brought by MIMO, and have instead focused on large-scale MIMO systems [9], which require more antennas to be deployed at the base station (BS). Massive MIMO (mMIMO) technology can effectively utilize space resources to cope with the current severe situation of spectrum resource shortage.…”

Section: Introductionmentioning

confidence: 99%

Multi-Access Edge Computing (MEC) Based on MIMO: A Survey

Zhu

Gao

et al. 2023

Sensors

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With the rapid development of wireless communication technology and the emergence of intelligent applications, higher requirements have been put forward for data communication and computing capacity. Multi-access edge computing (MEC) can handle highly demanding applications by users by sinking the services and computing capabilities of the cloud to the edge of the cell. Meanwhile, the multiple input multiple output (MIMO) technology based on large-scale antenna arrays can achieve an order-of-magnitude improvement in system capacity. The introduction of MIMO into MEC takes full advantage of the energy and spectral efficiency of MIMO technology, providing a new computing paradigm for time-sensitive applications. In parallel, it can accommodate more users and cope with the inevitable trend of continuous data traffic explosion. In this paper, the state-of-the-art research status in this field is investigated, summarized and analyzed. Specifically, we first summarize a multi-base station cooperative mMIMO-MEC model that can easily be expanded to adapt to different MIMO-MEC application scenarios. Subsequently, we comprehensively analyze the current works, compare them to each other and summarize them, mainly from four aspects: research scenarios, application scenarios, evaluation indicators and research issues, and research algorithms. Finally, some open research challenges are identified and discussed, and these indicate the direction for future research on MIMO-MEC.

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Section: Introductionmentioning

confidence: 99%

Multi-Access Edge Computing (MEC) Based on MIMO: A Survey

Zhu

Gao

et al. 2023

Sensors

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“…For simplicity, the RSUs adopt the indirect transmission mode. Significant interference caused by the multi-antenna BS and RSUs will increase the computation complexity of the multi-input multi-output (MIMO) system [31]. Thus, we adopt the linear receivers for the BS and RSUs to reduce the computation complexity [32].…”

Section: System Modelmentioning

confidence: 99%

Multi-Agent-Deep-Reinforcement-Learning-Enabled Offloading Scheme for Energy Minimization in Vehicle-to-Everything Communication Systems

Duan,

Li,

Huang

et al. 2024

Electronics

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Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers.

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“…This problem is solved in 5G networks, where antennas are reduced in size in the medium and high frequency bands. The advantage of large-scale MIMO system is that 3D signals can be aimed in 3d space in 5G system, and largescale MIMO system can increase network capacity and coverage [6]. Beamforming capabilities can also be enhanced by large-scale MIMO technology, which can be very helpful in sending designated user beams into space.…”

Section: Bmultiple Input Multiple Output Systems and Beamformingmentioning

confidence: 99%