Tractable Resource Management With Uplink Decoupled Millimeter-Wave Overlay in Ultra-Dense Cellular Networks

By Jihong Park, Seong-Lyun Kim, Jens Zander. 2015

107 citations recorded by the source corpus.

The forthcoming 5G cellular network is expected to overlay millimeter-wave (mmW) transmissions with the incumbent micro-wave (μW) architecture. The overall mm-μW resource management should, therefore, harmonize with each other. This paper aims at maximizing the overall downlink (DL) rate with a minimum uplink (UL) rate constraint, and concludes: mmW tends to focus more on DL transmissions while μW has high priority for complementing UL, under time-division duplex (TDD) mmW operations. Such UL dedication of μW results from the limited use of mmW UL bandwidth due to excessive power consumption and/or high peak-to-average power ratio (PAPR) at mobile users. To further relieve this UL bottleneck, we propose mmW UL decoupling that allows each legacy μW base station (BS) to receive mmW signals. Its impact on mm-μW resource management is provided in a tractable way by virtue of a novel closed-form mm-μW spectral efficiency (SE) derivation. In an ultra-dense cellular network (UDN), our derivation verifies mmW (or μW) SE is a logarithmic function of BS-to-user density ratio. This strikingly simple yet practically valid analysis is enabled by exploiting stochastic geometry in conjunction with real three-dimensional (3-D) building blockage statistics in Seoul, South Korea.

Subfields: Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “3) Uplink–Downlink Asymmetric MSI Exchange: Due to the device-limited transmission power, the uplink communication is likely to be slower than the downlink communication speed [124].”
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data (2018, methodology): “As an example, one can exploit FD in the uplink and FL in the downlink, on the ground that the downlink wireless communication links are commonly faster than the uplink [13].”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “In mobile communication systems, uplink data rates are often much lower than downlink rates due to the limited transmission power of mobile devices [185].”
One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction (2019, background): “ing task [1]. These signals are sensitive to physical blockages, and the received power levels drop significantly once the signals are blocked, i.e., in non-line-of-sight (non-LoS) channel conditions [5]. When either LoS or non-LoS conditions are consistent, the sudden variation of power levels give”
Stochastic Geometric Coverage Analysis in mmWave Cellular Networks With Realistic Channel and Antenna Radiation Models (2018, background): “Multiple UEs can be associated with a single BS, while the BS serves only a single UE per unit time slot according to a uniformly random scheduler, as assumed in [1], [8], and [11] under stochastic geometric settings.”
Ultra-dense edge caching under spatio-temporal demand and network dynamics (2017, methodology): “We set λb λu, according to the definition of UDN [16].”
Federated Knowledge Distillation (2020, background): “der communicationlimited scenarios. In a typical wireless communication network, the uplink communication is more limited by lower transmission power and smaller available bandwidth than the downlink [29], which we refer to as uplink-downlink channel asymmetry. Thus, for FD based distributed learnin”
URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems (2018, background): “As done in [5], [18], [20], inter-cell interference is treated as noise, and is assumed to be large such that the maximum noise power N is negligible.”
Wireless Edge Computing With Latency and Reliability Guarantees (2019, background): “Recently, the idea of DC for mmWave and microwave bands has been proposed [36], [61] as an effective approach to facilitate cellular mmWave IA [62] as well as mmWave handover [63].”
Distilling On-Device Intelligence at the Network Edge (2019, background): “1) Uplink-Downlink Capacity Asymmetry : Uplink capacity is often limited by the low transmission power budget of each device [12].”