Traditionally cellular networks have employed a fixed structure of infrastructure antennas to support user data requests. The most prominent element in this structure has been the base station, which utilizes a set of radio resources in a certain spatial area, leading to the deployment of many base stations over a bigger area to achieve coverage, e.g. in a city. In this given structure, user terminals are typically associated with one base station, which serves the user as long as the user is in its coverage range. If the user moves out of the coverage range, then typically a handover is performed such that the user experience can be preserved by the network. A handover typically involves the association of the user with a new base station leading to a redirection of backlogged and newly arriving data in the backbone to the new base station.
In these state-of-the-art systems, in addition to handover phenomena described above, two additional scenarios are known that alter the user-base station association. Load imbalance: If significantly more users are associated locally to one base station in comparison to the neighbouring ones, then load balancing might be invoked which consists of handing over selected user terminals from the overloaded cell. Several reasons can lead to such load balancing action, such as overload of the backhaul, clogging of the random access channel, or insufficient radio resources on the air interface to serve all users. Cell-edge users: When a user terminal is at the border of one cell, the user terminal is likely to experience a very low Signal to Interference and Noise Ratio (SINR). Then, one solution proposed in the literature and implemented in Long Term Evolution (LTE) advanced is to have the user terminal associate to two base stations simultaneously. This is referred to as coordinated multi-point transmission, which requires tight synchronization of the involved base stations, as they transmit data symbols on the same radio resources at the same time points to the receiving user terminal. In this case, the user data is forwarded to two base stations simultaneously. Coordinated multi-point furthermore has the advantage to potentially reduce interference between neighbouring base stations.
More recently, with the beginning of discussions on 5G specifications and their technological enablers, novel cellular architectures are emerging. Among those architectures is the Cloud Radio Access Network (CRAN), a centralized cloud-computing solution proposed for future 5G cellular networks. By employing a large number of transmit antennas in a dense setting, CRAN is able to achieve extremely high data rates/area spectral efficiency requirements specified by the 5G. More specifically, there exists the idea to deploy large sets of so called Remote Radio Heads (RRHs), essentially antenna arrays deployed locally with low processing capabilities, which are controlled in terms of their transmission characteristics by a more centralized entity referred to as Aggregation Node (AN). The AN coordinates the transmission of multiple RRHs and therefore requires the RRHs to be assigned to the AN, building a so called Antenna Domain (AD). In this architecture, user terminals are associated to some RRH, and through the RRH to some AN. This novel architecture has significant advantages when it comes to serving the associated terminals as the transmission of the multiple RRHs can be coordinated leading (among other issues) to a fine-grained control of the interference.
In a conventional solution one single antenna domain is considered and assumes that grand coalition (i.e., cooperative transmission by all the RRHs in the considered AD) is not possible due to limited backhaul and lack of global Channel State Information at the Transmitter (CSIT) at the AN. This conventional solution then investigates the gains in sum rate and energy efficiency due to RRH clustering (when instead of a grand coalition, local coalitions are formed by the RRHs). Authors assume that transmission strategy within each RRH cluster is fixed to Block Diagonal/Zero-Forcing Beamforming (BD/ZFBF). Then, for each RRH cluster, authors compute successful access (i.e., coverage) probability for each user (given some pre-specified threshold SINR) using stochastic geometry concepts; this coverage probability is then multiplied with the deterministic/pre-specified rate to compute the sum rate per cluster. This sum rate is then used to construct a utility function to set up a coalitional formation game to solve RRH clustering problem; the coalitional formation game is then solved using merge and split method. The proposed scheme performs in between grand-coalition and no-coalition schemes (the two extreme cases), as expected.
In another conventional solution a single antenna domain is considered where each RRH is connected to the AN via a constrained backhaul link. Moreover, the backhaul links of different RRHs are assumed to have different capacities. The per-RRH backhaul constraint into an equivalent per-RRH transmit power constraint is then re-formulated. They then solve the problem of weighted sum rate maximization by doing static/dynamic user-centric clustering of RRHs (i.e., multiple RRHs jointly serving a user, similar to CoMP) while RRHs within one cluster employ Weighted Minimum Mean Square Error (WMMSE) precoding scheme. The authors also propose some heuristic, sub-optimal methods for RRH clustering. The simulation results show a performance gain of proposed methods compared to naive RRH clustering methods available in the literature.
Several major shortcomings arise from the conventional solutions in the context of the CRAN architecture.
Interference between ADs: While conventional solutions has been investigating techniques to balance interference between several clusters within an AD, it is open how to deal with interference between neighbouring ADs, except for resorting to traditional RRM based interference mitigation techniques.
Backhaul limitations of the ANs: It is furthermore completely open how to deal with capacity constraints on the backhaul of each AD, or also with capacity constraints regarding the backhaul between RRHs and an AN.
Radio resource limitations on the air interface between RRHs and user terminals/limitations on the control channel/limitations on the random access channel: It is open in CRAN how to deal with strongly varying user distribution over a certain area of interest with multiple ANs and many RRHs, i.e. how to perform load balancing in CRAN, except for resorting to traditional load balancing techniques.
Processing limitations of the RRHs or the AN: It is open how to deal with limitations with respect to the processing capabilities of either RRHs (if they are equipped with such resources), and/or how to deal with processing limitations at the ANs.