Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33\% compared with the one attained without optimizing power sharing factors

Compression and Recovery in Cell-free Cloud Radio Access Network

Cloud radio access network (C-RAN) is an evolved network architecture for future mobile communication systems. It aims to provide higher spectral efficiency, lower energy consumption and reduced cost of operations and maintenance for the network, which will enable the operators to not only satisfy growing user demands, but provide new services and applications. However, the huge load on the fronthaul network which connects the baseband unit (BBU) and a large number of remote radio heads (RRHs) is a significant challenge. To improve the fronthaul performance, a data compression and recovery scheme based on compressive sensing is proposed in this thesis. First, the theory of compressive sensing is studied, including the essential principles, standard compressive sensing model, potential measurement matrices, etc. Several popular recovery algorithms in compressive sensing are demonstrated in detail. Secondly, a compression and recovery scheme is proposed for the uplink of a cell-free C-RAN system. In the proposed scheme, compressive sensing is applied by exploiting the sparsity of user data. In particular, the multi-access fading in this system is incorporated into the formulation of the compressive sensing model. The aggregated measurement matrix which contains both the channel matrix and the fronthaul compression matrix is shown to satisfy the restricted isometry property (RIP) condition. Furthermore, two different recovery algorithms, basis pursuit denoising (BPDN) and sparsity adaptive matching pursuit (SAMP), are used respectively for estimating the sparse signals. The major advantage is that they do not require the sparsity of user data as a prior information during the process of signal recovery. It allows easy applications in many practical scenarios where the number of non-zero elements of the signals is not available. The simulation results show that the proposed scheme can efficiently alleviate the heavy burden on the fronthaul network, and meanwhile provide stable signal recovery for this system