Fronthaul constrained problem

Fronthaul is defined as the link between BBUs and RRHs.

[1] “Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges”
The heterogeneous cloud radio access network (H-CRAN) as a 5G paradigm toward green and soft themes is briefly presented in [3] to enhance C-RAN.

To alleviate the capacity constraint on the fronthaul links, a multi-service small-cell wireless access architecture based on combining radio-over-fiber with optical wavelength division multiplexing (WDM) techniques is proposed in [4].

- C-RAN System Architectures:
  A. C-RAN components: RRH, BBU pool, Fronthaul.
    Non-ideal fronthaul: bandwidth, time latency and jitter constraints.
  B. C-RAN System Structures: Full centralization, Partial centralization, Hybrid centralization.
- Signal Compression and Quantization
  An interesting result shows that by simply setting the quantization noise power proportional to the background noise level at each RRH, the quantize-and-forward scheme can achieve a capacity within a constant gap to a throughput performance upper bound. [6]
  A. Compression and Quantization in the Uplink
    Distributed Wyner-Ziv lossy compression; independent compression.
  B. Compression and Quantization in the Downlink
    Hybrid compression and message-sharing strategy for DL transmission is presented in [10].
- Coordinated Signal Processing and Clustering
  A. Precoding Techniques
    Two types of IQ-data transfer methods: after-precoding & before-precoding.
    Sparsity: individual sparsity, group sparsity.
  B. Clustering Techniques ？
    Two types of RRH clustering schemes: disjoint clustering and user-centric clustering.
    An explicit expression for the successful access probability (SAP) for clustered RRHs is derived by applying stochastic geometry in [12].
- Radio Resource Allocation and Optimization
  There are mainly three approaches to deal with the delay-aware RRAO problem: equivalent rate constraint, Lyapunov optimization, and Markov decision processes (MDPs).
  In [13], a hybrid coordinated multi-point transmission (H-CoMP) scheme is presented for downlink transmission in frontal constrained C-RANs, which fulfills the flexible tradeoff between large-scale cooperation processing gain and frontal consumption.
- Challenging Work and Open Issues
  A. C-RANs with SDN
  B. C-RANs with NFV
  C. C-RANs with Inter-Connected RRHs

[2] “Joint Power Control and Fronthaul Rate Allocation for Throughput Maximization in OFDMA-based Cloud Radio Access Network”

[3] “Joint Precoding and Multivariate Backhaul Compression for the Downlink of Cloud Radio Access Networks”

[4] “Robust and Efficient Distributed Compression for Cloud Radio Access Networks”

[5] “Joint Decompression and Decoding for Cloud Radio Access Networks”

[6] “Performance Evaluation of Multiterminal Backhaul Compression for Cloud Radio”

[7] “Inter-Cluster Design of Precoding and Fronthaul Compression for Cloud Radio Access Networks”
Compared inter-cluster with intra-cluster.

[8] “Hybrid Compression and Message-Sharing Strategy for the Downlink Cloud Radio-Access Network”

Reading List - 2015.3.18

“Fronthaul Compression for Cloud Radio Access Networks”

A survey of the work in the area of fronthaul compression with emphasis on advanced signal processing solutions based on network information theoretic concepts.

- Multiterminal compression
  Uplink: The key technique is distributed compression or Wyner-Ziv coding[10].
  Downlink: multivariate compression [9 Ch.9].
- Structured coding
  compute-and-forward[11]

• Uplink:

- Point-to-Point Fronthaul Compression

- Distributed Fronthaul Compression
  First proposed in [17].
  sequential decompression [9, Ch. 10] [18] [19].
  Wyner-Ziv compression.
  channel decoding algorithms: message passing or trellis search[10].
  optimization problem of this scheme: block-coordinate optimization approach and leverages a key result in [20].

- Compute-and-forword [11]
- Multihop Fronthaul Topology [22]

• Downlink

- Point-to-Point Fronthaul Compression
- Multivariate Fronthaul Compression
- Compute-and-forward [24]
- "dirty paper" nonlinear precoding [25]
- Performance Evaluation
  cell-edge throughput versus the average per-UE spectral efficiency [8, Fig.5].