MmWave massive-MIMO-based wireless backhaul for the 5G ultra-dense network

By Zhen Gao, Linglong Dai, D. Mi, Zhaocheng Wang, Muhammad Ali Imran, M. Z. Shakir. IEEE wireless communications, 2015

411 citations recorded by the source corpus.

The ultra-dense network (UDN) has been considered as a promising candidate for future 5G networks to meet the explosive data demand. To realize UDN, a reliable, gigahertz bandwidth, and cost-effective backhaul connecting ultradense small-cell BSs and macrocell BS are prerequisite. Millimeter-wave can provide the potential gigabit-per-second traffic for wireless backhaul. Moreover, mmWave can easily be integrated with massive MIMO for improved link reliability. In this article, we discuss the feasibility of mmWave massive-MIMO-based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. In particular, we propose a digitally controlled phase shifter network (DPSN)-based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of the mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of a transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macrocell BS can simultaneously support multiple small-cell BSs with multiple streams for each small-cell BS, which is essentially different from conventional hybrid precoding/combining schemes, typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospects to enable mmWave massive-MIMO-based wireless backhaul for 5G UDN are discussed.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6d58015432775b16236e91ca322696f81d48be1c

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Citations in the corpus

Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding (2019, methodology): “As reported in [24], H has lowrank characteristic since the limited scattering feature in the mmWave massive MIMO channel, indicating that near-optimal throughput is achieved by leveraging limited amounts of RF chains.”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “However, wired connections can be expensive and infeasible due to geographical constraints, especially when dealing with ultra dense cellular networks [95]–[97].”
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking (2021, background): “The authors in [14] discussed the feasibility of mmWave massive-MIMO-based wireless backhaul for 5G UDN.”
Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach (2020, background): “mmWave communication enables 5Gwireless communication systems to utilize the spectrum ranging from3 to 300GHz [15].”
A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives (2018, methodology): “In [149], a digitally controlled phase shifter network based hybrid beamforming and the associated compressive sensing based channel estimation is proposed.”
Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications (2016, methodology): “1A closed-loop technique that performs coarse channel estimation by beam training and fine channel estimation by CS is introduced in [15].”
Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research (2018, methodology): “[266] have presented a mmWave based backhaul for 5G using massive-MIMO to support a high number of radio nodes, i.”
Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems (2017, methodology): “To reduce the cost of hardware implementation, hybrid analog-digital beamforming techniques have been developed for mmWave-band massive MIMO systems [18]–[22].”
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions (2017, methodology): “[128] A digitally-controlled phase-shifter network based hybrid precoding/combining scheme for mmWave massive MIMO.”
Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey (2018, positive): “CE schemes such as the super-resolution sparse channel estimator and its multi-user multi-stream hybrid beamforming/combining counterpart (Heuristic algorithm) [59] use a limited set of channel parameters (path gains, AoAs and angles of departure (AoDs)) to fully estimate the channel.”
Millimeter Wave Communication: A Comprehensive Survey (2018, background): “Gao et al. [88] evaluate the feasibility and identify the challenges of mmWave massive-MIMO-based backhaul for UDN.”