Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks

By Sooyoung Hur, Taejoon Kim, D. J. Love, J. Krogmeier, Timothy A. Thomas, Amitava Ghosh. IEEE Transactions on Communications, 2013

1,028 citations recorded by the source corpus.

Recently, there has been considerable interest in new tiered network cellular architectures, which would likely use many more cell sites than found today. Two major challenges will be i) providing backhaul to all of these cells and ii) finding efficient techniques to leverage higher frequency bands for mobile access and backhaul. This paper proposes the use of outdoor millimeter wave communications for backhaul networking between cells and mobile access within a cell. To overcome the outdoor impairments found in millimeter wave propagation, this paper studies beamforming using large arrays. However, such systems will require narrow beams, increasing sensitivity to movement caused by pole sway and other environmental concerns. To overcome this, we propose an efficient beam alignment technique using adaptive subspace sampling and hierarchical beam codebooks. A wind sway analysis is presented to establish a notion of beam coherence time. This highlights a previously unexplored tradeoff between array size and wind-induced movement. Generally, it is not possible to use larger arrays without risking a corresponding performance loss from wind-induced beam misalignment. The performance of the proposed alignment technique is analyzed and compared with other search and alignment methods. The results show significant performance improvement with reduced search time.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7a6048d0cc5c6ab46a6482fccd24ea675d1621fc

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Citations in the corpus

Millimeter-wave massive MIMO: the next wireless revolution? (2014, positive): “Backhaul Transmissions — MmWave massive MIMO can be beneficial in providing very highthroughput backhaul in areas where it is too costly to install wire or fiber connections [11].”
Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays (2015, methodology): “M PSs [22] to perform the analog precoding, which can be denoted by the analog weighting vector ān ∈ CM×1, whose elements have the same amplitude 1/ √ M but different phases [22].”
Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems (2014, methodology): “Before delving into the details of the algorithm, we will focus in the following section on the design of a multi-resolution beamforming codebook which is essential for the proper operation of the adaptive channel estimation algorithm.”
Channel Estimation for Hybrid Architecture-Based Wideband Millimeter Wave Systems (2016, background): “To avoid the explicit estimation of the channel, analog beam training solutions were proposed [6]–[8].”
Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays (2016, methodology): “Analog or RF beamforming schemes implemented using analog circuitry are introduced in [16]–[19].”
Modeling and Analyzing Millimeter Wave Cellular Systems (2016, background): “best beamforming vector codeword from this codebook [18], [108], [110].”
Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems (2015, background): “Recent channel measurements have confirmed the feasibility of using mmWave not only for backhaul [8]–[10], but also for the access link [11].”
Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems (2020, neutral): “Also, we assume a Rician channel comprising a LOS path and a number of NLOS paths [22]–[24].”
A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, methodology): “Analog beamforming is an effective technique to reduce RF chain cost [42]–[44], where one RF chain is shared by all array”
Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems (2014, methodology): “In [8], [9], adaptive beamforming algorithms and multi-resolution codebooks were developed by which the transmitter and receiver jointly design their analog beamforming vectors.”
What Will 5G Be? (2014, background): “For very recent contributions on these and other aspects, the reader is referred to a companion special issue on massive MIMO [94].”
Coexistence of Wi-Fi and heterogeneous small cell networks sharing unlicensed spectrum (2015, background): “ for the dense deployment of small cells. In the meanwhile, inband wireless backhaul solutions using the licensed spectrum may not be feasible either, because of the scarcity of the licensed spectrum [7]. Recently, the millimeter-wave bands, such as the unlicensed 60 GHz band and the low interferenc”
Millimeter Wave Cellular Networks: A MAC Layer Perspective (2015, background): “The high attenuation mitigates interference, while directionality supports wireless backhauling among micro and macro base stations (BSs) [9]; hence mmWave communication is suitable for dense heterogeneous deployments.”
Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems (2018, methodology): “The other approach is to directly train the RF beamforming vectors through exhaustive or hierarchical search to find the best beams [7], [20], [21].”
A Survey on Spatial Modulation in Emerging Wireless Systems: Research Progresses and Applications (2019, background): “Fortunately, attributed to the significantly reduced wavelength in the mmWave frequency band, mmWave MIMO systems can be equipped with a large number of antennas at the transceiver in a highly compact manner to achieve high beamforming gains [54].”
Millimeter Wave Communication: A Comprehensive Survey (2018, background): “2) Backhaul: During the past few years, research pertaining to the application of mmWave communications for 5G network backhaul has been widely investigated [197]–[199].”
An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems (2015, background): “The case of a one-bit ADC is especially interesting as it has negligible power consumption compared to other components in the front-end (a one-bit ADC at ultra-high sampling rate of 240 GS/s consumes around 10 mW [135]).”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, background): “To overcome this issue, there have been signiﬁcant research efforts on developing cost-aware MIMO transceiver architectures, such as analog beamforming [64], hybrid anlog/digital precoding [65,66], and lens antenna array communications [67,68].”