MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network

Ultra-dense network (UDN) has been considered as a promising candidate for future 5G network to meet the explosive data demand. To realize UDN, a reliable, Gigahertz bandwidth, and cost-effective backhaul connecting ultra-dense small-cell base stations (BSs) and macro-cell BS is prerequisite. Millimeter-wave (mmWave) can provide the potential Gbps traffic for wireless backhaul. Moreover, mmWave can be easily integrated with massive MIMO for the 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. Especially, we propose a digitally-controlled phase-shifter network (DPSN) based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macro-cell BS can simultaneously support multiple small-cell BSs with multiple streams for each smallcell 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 prospect to enable the mmWave massive MIMO based wireless backhaul for 5G UDN are discussed.Comment: This paper has been accepted by IEEE Wireless Communications Magazine. This paper is related to 5G, ultra-dense network (UDN), millimeter waves (mmWave) fronthaul/backhaul, massive MIMO, sparsity/low-rank property of mmWave massive MIMO channels, sparse channel estimation, compressive sensing (CS), hybrid digital/analog precoding/combining, and hybrid beamforming. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=730653

Complexity-Aware Scheduling for an LDPC Encoded C-RAN Uplink

Centralized Radio Access Network (C-RAN) is a new paradigm for wireless networks that centralizes the signal processing in a computing cloud, allowing commodity computational resources to be pooled. While C-RAN improves utilization and efficiency, the computational load occasionally exceeds the available resources, creating a computational outage. This paper provides a mathematical characterization of the computational outage probability for low-density parity check (LDPC) codes, a common class of error-correcting codes. For tractability, a binary erasures channel is assumed. Using the concept of density evolution, the computational demand is determined for a given ensemble of codes as a function of the erasure probability. The analysis reveals a trade-off: aggressively signaling at a high rate stresses the computing pool, while conservatively backing-off the rate can avoid computational outages. Motivated by this trade-off, an effective computationally aware scheduling algorithm is developed that balances demands for high throughput and low outage rates.Comment: Conference on Information Sciences and Systems (CISS) 2017, to appea