Dynamic clustering for coordinated multipoint transmission with joint prosessing
1University of Oulu, Faculty of Information Technology and Electrical Engineering, Department of Communications Engineering, Communications Engineering
|Online Access:||PDF Full Text (PDF, 1.1 MB)|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-201602111176
|Publish Date:|| 2016-02-15
|Thesis type:||Master's thesis (tech)
Coordinated Multipoint (CoMP) transmission has been identified as a promising concept to handle the substantial interference in the LTE-Advanced systems and it is one of the key technology components in the future 5G networks. CoMP transmission involves two coordination schemes: joint processing (JP) and coordinated beamforming (CB). The scope of this thesis is limited to JP. In the CoMP JP scheme, each user is coherently served by multiple base stations (BSs) and consequently, the user’s signal strength is enhanced and the interference is mitigated. The coherent joint processing requires sharing data and channel state information (CSI) of all the users among all the BSs, which leads to high backhaul capacity requirement and high signaling cost especially in large-scale networks. Grouping the BSs into smaller coordination clusters within which a user is served by only the BSs in the cluster will significantly reduce the signaling cost and the backhaul burden. In this thesis, optimal BS clustering and beamformer design for CoMP JP in the downlink of a multi-cell network is studied. The unique aspect of the study is that the BS clustering and the beamformer design are carried out jointly by iteratively solving a series of convex sub-problems. The BSs are dynamically grouped into small coordination clusters whereby each user is served by a few BSs that are in a coordination cluster. The joint BS clustering and beamformer design is performed to maximize a network utility function in the form of the weighted sum rate maximization (WSRM). The weighted sum rate maximization (WSRM) problem is formulated from the perspective of sparse optimization framework where sparsity is induced by penalizing the objective function with a power penalty. The WSRM problem is known to be non-convex and NP-hard. Therefore, it is difficult to solve directly. Two solutions are studied; in the first approach, the WSRM problem is solved via weighted minimum mean square error (WMMSE) minimization and the second approach involves approximation of the WSRM problem as a successive second order cone program (SSOCP). In both approaches, the objective function is penalized with a power penalty and the clusters can be adjusted by a single parameter in the problem. The performance evaluation of the proposed algorithms is carried out via simulation and it is shown that the serving sets in the network can be controlled according to the available backhaul capacity by properly selecting a single parameter in the problem. Finally, an algorithm for a fixed number of active links is proposed.