We consider training-based channel estimation for a cloud radio access network (CRAN), in which a large amount of remote radio heads (RRHs) and users are randomly scattered over the service area. In this model, assigning orthogonal training sequences to all users will incur a substantial overhead to the overall network, and is even impossible when the number of users is large. Therefore, in this paper, we introduce the notion of local orthogonality, under which the training sequence of a user is orthogonal to those of the other users in its neighborhood. We model the design of locally orthogonal training sequences as a graph coloring problem. Then, based on the theory of random geometric graph, we show that the minimum training length scales in the order of lnK, where K is the number of users covered by a CRAN. This indicates that the proposed training design yields a scalable solution to sustain the need of large-scale cooperation in CRANs. Numerical results show that the proposed scheme outperforms other reference schemes.
It is also worth mentioning that training-based CRAN has been previously studied in the literature @cite_18 @cite_25 . In @cite_18 , the authors proposed a coded pilot design where RRHs can be turned on or off to avoid pilot collisions, which may degrade the system performance. In @cite_25 , in each transmission block, only a portion of users is allowed to transmit pilots for channel training, and the channels of the other users are not updated. This scheme can only accommodate a relatively small number of users to avoid an unaffordable training overhead. Therefore, training design for CRAN deserves further endeavor, which is the main focus of this work.
@cite_18 @cite_25
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A key problem in the design of cloud radio access networks (CRANs) is that of devising effective baseband compression strategies for transmission on the fronthaul links connecting a remote radio head (RRH) to the managing central unit (CU). Most theoretical works on the subject implicitly assume that the RRHs, and hence the CU, are able to perfectly recover time synchronization from the baseband signals received in the uplink, and focus on the compression of the data fields. This paper instead dose not assume a priori synchronization of RRHs and CU, and considers the problem of fronthaul compression design at the RRHs with the aim of enhancing the performance of time and phase synchronization at the CU. The problem is tackled by analyzing the impact of the synchronization error on the performance of the link and by adopting information and estimationtheoretic performance metrics such as the rate-distortion function and the Cramer-Rao bound (CRB). The proposed algorithm is based on the Charnes-Cooper transformation and on the Difference of Convex (DC) approach, and is shown via numerical results to outperform conventional solutions. Cloud radio access networks (C-RANs) have been regarded as a promising architecture for energy-efficient fifth generation systems. In this paper, a new joint remote radio head (RRH) activation, user-RRH pairing and resource allocation strategy is proposed for heterogeneous C-RANs (H-CRANs). We first formulate an optimization problem to maximize the energy efficiency of H-CRANs. Then, a low-complexity suboptimal solution is developed. Our proposed mechanism consists of three key procedures: 1) RRH activation is performed based on greedy RRH selection, 2) user-RRH pairing is performed based on the channel quality, 3) the resource allocation problem is solved by dual decomposition. Simulation results show that the proposed strategy can improve energy efficiency significantly. Cloud Radio Access Network (CRAN) is proposed as a promising network architecture for future mobile communications. In this paper, we consider the topic of active user detection (AUD) and channel estimation (CE) in uplink CRAN systems with sparse active users. Different from conventional AUD and CE approaches which require the length of uplink pilots to scale with the number of users times the number of antennas per user, a novel algorithm will be proposed to substantially reduce the uplink training overhead by leveraging the technique of compressive sensing (CS). To achieve this goal, we first transform the problem of AUD and CE into standard CS problems. We then propose a modified Bayesian compressive sensing (BCS) algorithm to conduct AUD and CE in CRAN, which exploits not only the active user sparsity, but also the innate heterogeneous path loss effects and the joint sparsity structures in multi-antenna uplink CRAN systems. Dense large-scale antenna deployments are one of the most promising technologies for delivering very large throughputs per unit area in the downlink (DL) of cellular networks. We consider such a dense deployment involving a distributed system formed by multi-antenna remote radio head (RRH) units connected to the same fronthaul serving a geographical area. Knowledge of the DL channel between each active user and its nearby RRH antennas is most efficiently obtained at the RRHs via reciprocity based training, that is, by estimating a user's channel using uplink (UL) pilots transmitted by the user, and exploiting the UL DL channel reciprocity. We consider aggressive pilot reuse across an RRH system, whereby a single pilot dimension is simultaneously assigned to multiple active users. We introduce a novel coded pilot approach, which allows each RRH unit to detect pilot collisions, i.e., when more than a single user in its proximity uses the same pilot dimensions. Thanks to the proposed coded pilot approach, pilot contamination can be substantially avoided. As shown, such a strategy can yield densification benefits in the form of increased multiplexing gains per UL pilot dimension with respect to conventional reuse schemes and some recent approaches assigning pseudorandom pilot vectors to the active users.