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The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit-beamforming used is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase-synchronization is required. By exploiting the classic successive interference cancellation (SIC) technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l1-norm technique to approximate the l0-norm in the fronthaul capacity constraints as the weighted power constraints. A low-complexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the non-coherent one in the low fronthaul capacity regime, which is the case in ultra-dense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed. Index Terms-Ultra-dense networks (UDN), C-RAN, fronthaul capacity, pilot reuse, robust design.
Cloud radio access networking (C-RAN) constitutes a promising architecture for next-generation systems. Beneficial centralized signal processing techniques can be realized under the C-RAN architecture. Furthermore, given the recent rapid development of cloud computing, the C-RAN architecture is an ideal platform for supporting network function virtualization (NFV), software-defined networking (SDN) and artificial intelligence (AI). However, most of the existing contributions on C-RAN are mainly focused on the physical layer issues. The next-generation networks are expected to support compelling wireless applications satisfying diverse delay requirements, such as ultra-reliable and low-latency communications (URLLC), etc. Hence, we invoke the effective capacity theory for statistical delay-bounded QoS provision in C-RAN architectures, where the delay is taken into account. Based on the system model proposed, we conceive sophisticated power allocation schemes for maximizing the effective capacity of both single-user and multiuser scenarios. Our simulation results show that a low delay outage probability can be guaranteed by appropriately choosing the delay exponent. Furthermore, our simulation results demonstrate that the proposed algorithm significantly outperforms the existing algorithms in terms of the achievable effective capacity. Finally, some open research challenges are highlighted. H. Ren was with the Southeast University,
Ultra-dense networks (UDN) constitute one of the most promising techniques of supporting the fifth generation (5G) mobile system. By deploying more small cells in a fixed area, the average distance between users and access points can be significantly reduced, hence a dense spatial frequency reuse can be exploited. However, severe interference is the major obstacle in UDN. Most of the contributions deal with the interference by relying on cooperative game theory. This paper advocates the application of dense user-centric cloud radio access network (C-RAN) philosophy to UDN, thanks to the recent development of cloud computing techniques. Under dense C-RAN, centralized signal processing can be invoked for supporting Coordinated Multiple Points Transmission/Reception (CoMP) transmission. We summarize the main challenges in dense user-centric C-RANs. One of the most challenging issues is the requirement of the global CSI for the sake of cooperative transmission. We investigate this requirement by only relying on partial channel state information (CSI), namely, on inter-cluster large-scale CSI. Furthermore, the estimation of the intra-cluster CSI is considered, including the pilot allocation and robust transmission. Finally, we highlight several promising research directions to make the dense user-centric C-RAN become a reality, with special emphasis on the application of the 'big data' techniques. Index Terms Ultra-dense networks (UDN), user-centric C-RAN, virtual cells, DAS, imperfect CSI, pilot allocation.
This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fron-thaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), etc. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower-bound. Then, based on the successive convex approximation (SCA) technique and the relationship between the data rate and the mean square error (MSE), we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed. Index Terms—Cloud radio access network (C-RAN), 5G ultra-dense networks, limited fronthaul capacity, incomplete CSI.
We propose a power-and rate-adaptation scheme for cloud radio access networks (C-RANs), where each radio remote head (RRH) is connected to the baseband unit (BBU) pool through optical links. The RRHs jointly support the users by efficiently exploiting the enhanced spatial degrees of freedom. Our proposed scheme aims for maximizing the effective capacity (EC) of the user subject to both per-RRH average-and peak-power constraints, where the EC is defined as the maximum arrival rate that can be supported by the C-RAN under the statistical delay requirement. We first transform the EC maximization problem into an equivalent convex optimization problem. By using the Lagrange dual decomposition method and solving the Karush-Kuhn-Tucker (KKT) equations, the optimal transmission power of each RRH can be obtained in closed-form. Furthermore, an online tracking method is provided for approximating the average power of each RRH for the sake of updating the Lagrange dual variables. For the special case of two RRHs, the expression of the average power of each RRH can be calculated in explicit form. Hence, the Lagrange dual variables can be computed in advance in this special case. Furthermore, we derive the power allocation for two important extreme cases: 1) no delay constraint; 2) extremely stringent delay-requirements. Our simulation results show that the proposed scheme significantly outperforms the conventional algorithm without considering the delay requirements. Furthermore, when appropriately tuning the value of the delay exponent, our proposed algorithm is capable of guaranteeing a delay outage probability below 10−9 when the maximum tolerable delay is 1 ms. This is suitable for the future ultra-reliable low latency communications (URLLC).