Adaptive Compression and Joint Detection for Fronthaul Uplinks in Cloud Radio Access Networks

Cloud radio access network (C-RAN) has recently attracted much attention as a promising architecture for future mobile networks to sustain the exponential growth of data rate. In C-RAN, one data processing center or baseband unit (BBU) communicates with users via distributed remote radio heads (RRHs), which are connected to the BBU via high capacity, low latency fronthaul links. In this paper, we study the compression on fron- thaul uplinks and propose a joint decompression algorithm at the BBU. The central premise behind the proposed algorithm is to ex- ploit the correlation between RRHs. Our contribution is threefold. First, we propose a joint decompression and detection (JDD) algorithm which jointly performs decompressing and detecting. The JDD algorithm takes into consideration both the fading and compression effect in a single decoding step. Second, block error rate (BLER) of the proposed algorithm is analyzed in closed-form by using pair-wise error probability analysis. Third, based on the analyzed BLER, we propose adaptive compression schemes subject to quality of service (QoS) constraints to minimize the fronthaul transmission rate while satisfying the pre-defined target QoS. As a dual problem, we also propose a scheme to minimize the signal distortion subject to fronthaul rate constraint. Numerical re- sults demonstrate that the proposed adaptive compression schemes can achieve a compression ratio of 300% in experimental setups

Adaptive Cloud Radio Access Networks: Compression and Optimization

Future mobile networks are facing with exponential data growth due to the proliferation of diverse mobile equipment and data-hungry applications. Among promising technology candidates to overcome this problem, cloud radio access network (CRAN) has received much attention. In this work, we investigate the design of fronthaul in C-RAN uplink by focusing on the compression and optimization in fronthaul uplinks based on the statistics of wireless fading channels. First, we derive the system block error rate (BLER) under Rayleigh fading channels. In particular, upper and lower bounds of the BLER union bound are obtained in closed-form. From these bounds, we gain insight in terms of diversity order and limits of the BLER. Next, we propose adaptive compression schemes to minimize the fronthaul transmission rate subject to a BLER constraint. Furthermore, a fronthaul rate allocation is proposed to minimize the system BLER. It is shown that the uniform rate allocation approaches the optimal scheme as the total fronthauls’ bandwidth increases. Lastly, numerical results are presented to demonstrate the effectiveness of our proposed optimizations

Power Optimization With BLER Constraint for Wireless Fronthauls in C-RAN

Cloud radio access network (C-RAN) is a novel architecture for future mobile networks to sustain the exponential traffic growth thanks to the exploitation of centralized processing. In C-RAN, one data processing center or baseband unit (BBU) communicates with users via distributed remote radio heads (RRHs), which are connected to the BBU via high capacity, low latency fronthaul links. In this letter, we study C-RAN with wireless fronthauls due to their flexibility in deployment and management. First, a tight upper bound of the system block error rate (BLER) is derived in closed-form expression via union bound analysis. Based on the derived bound, adaptive transmission schemes are proposed. Particularly, two practical power optimizations based on the BLER and pair-wise error probability (PEP) are proposed to minimize the consumed energy at the RRHs while satisfying the predefined quality of service (QoS) constraint. The premise of the proposed schemes originates from practical scenarios where most applications tolerate a certain QoS, e.g., a nonzero BLER. The effectiveness of the proposed schemes is demonstrated via intensive simulations

A FEASIBILITY STUDY ON C-RAN

Now a days the number of users of mobile phone are increasing exponentially, so it will cause jamming in the network  and require  large bandwidth So among promising technology candidates to overcome this problem, cloud radio access network (C-RAN) used.C-RAN, having one baseband unit (BBU) communicates with users through distributed Remote Radio Heads (RRHs) .RRH  are connected to the BBU via high capability, low latency fronthaul links and performs soft relaying. The architecture of C-RAN imposes a shortage of fronthaul bandwidth because raw I/Q  samples are exchanged between the RRHs and the BBU.In BBU different algorithms are used to improve the capacity,joint decompression and decoding(JDD) and wynerziv coding

Joint Design of Wireless Fronthaul and Access Links in Massive MIMO CRANs

Cloud radio access network (CRAN) has emerged as a promising mobile network architecture for the current 5th generation (5G) and beyond networks. This thesis focuses on novel architectures and optimization approaches for CRAN systems with massive multiple-input multiple-output (MIMO) enabled in the wireless fronthaul link. In particular, we propose a joint design of wireless fronthaul and access links for CRANs and aim to maximize the network spectral efficiency (SE) and energy efficiency (EE). Regarding downlink transmission in massive MIMO CRANs, the precoding designs of the access link are optimized by accounting for both perfect instantaneous channel state information (CSI) and stochastic CSI of the access link separately. The system design adopts a decompress-and-forward (DCF) scheme at the remote radio heads (RRHs), with optimization of the multivariate compression covariance noise. Constrained by the maximum power budgets set for the central unit (CU) and RRHs, we aim to maximize the network sum-rate and minimize the total transmit power for all user equipments (UEs). Moreover, we present a separate optimization design and compare its performance, feasibility, and computational efficiency with the proposed joint design. Considering the uplink transmission, we utilize a compress-and-forward (CF) scheme at the RRHs. Assuming that perfect CSI is available at the CU, our objective is to optimize the precoding matrix of the access link while adopting conventional precoding methods for the fronthaul link. This thesis also proposes an unmanned aerial vehicle (UAV)-enabled CRAN architecture with a massive MIMO CU as a supplement system to the terrestrial communication networks. The locations of UAVs are optimized along with compression noise, precoding matrices, and transmit power. To tackle the non-convex optimization problems described above, we employ efficient iterative algorithms and conduct a thorough exploration of practical simulations, yielding promising results that outperform benchmark schemes. In summary, this thesis explores future wireless CRAN architectures, leveraging promising technologies including massive MIMO and UAV-enabled communications. Furthermore, this work presents comprehensive optimization designs aimed at further enhancing the network efficiency