Radio resource management in heterogeneous deployments: A system level perspective

Thomas Wirth, Johannes Dommel, Kai Borner, Lars Thiele, Thomas Haustein
2011 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)  
New multimedia services such as HTTP live video streaming [1] demand for higher capacity, especially in mobile networks. In fact, about 50 % of data in mobile networks is currently video traffic, and this number is expected to increase to 70-80 % by 2015 [2] . To enhance the capacity in cellular networks even further, several study items were defined [3] to target peak data rates of up to 1 Gbit/s in the downlink and 500 Mbit/s in the uplink. The different techniques are currently being
more » ... d within the 3GPP for LTEs evolution called LTE-Advanced (LTE-A). A promising approach to enhance the capacity in cellular access networks even further is to create smaller cells [4] . This represents a shift in paradigm from Macro cells to a more heterogeneous ecosystem consisting of a combination of different cell sizes and transmission powers and thus coverage, referred to as Macro-, Pico-, and Femto-cells. The key idea behind heterogeneous networks (HetNet) is that the frequency reuse factor (FRS) is set to 1, which means that all heterogeneous cells operate in the same frequency range, but can be controlled and optimized in frequency domain together. With FRS 1, cell capacity is strongly interference limited and interference management (IM) techniques are required at both base station (eNB) and user equipment (UE) to reach the target performance. Newly defined protocols between HetNet entities allow decentralized, feedback-based radio resource management (RRM) which can help to mitigate interference and enhance HetNet performance on a cellular level. The performance is measured by key performance indicators (KPIs) [5] with performance criteria tailored to HetNet deployments. Aim of this paper is to evaluate RRM techniques which focus on a distributed precoding concept on system-level taking into account realistic antenna models optained by 3D antenna measurements. System Model: We consider a HetNet system with a set of Macro cells and 1 or 2 associated Pico cells per Macro. Considering effect on the wireless channel, e.g. path loss, shadow fading, 3D antenna models, the received downlink signal ym at user m and is given by ym = Hi,mbi,m hm xi,m + j∈M\{m} Hi,mbi,j xi,j ζm +zm (1) The desired data stream xi,m transmitted to the m th user from the i th cell is distorted by the intra-and inter-cell interference plus noise aggregated in ζm and zm, respectively. Hi,m spans the NR × NT channel matrix for user m formed by the i th cell. The NT × NT precoding matrix Bi = [bi,1 · · · bi,M ] includes the power allocation pi and contains the precoders bi,m designed for each user. The proposed precoding matrix treats the Macro and associated Picos as a virtual antenna array and forms a combined virtual precoding matrix. The precoder is defined by the codebooks Bi with Macro power weights γ, and Pico power weights α and β, which fulfill the corresponding per-antenna power constraints of the transmit antennas. The precoding codebooks for a Macro eNB with 2 transmit antenna and 2 Pico eNBs with a single antenna can be written as
doi:10.1109/acssc.2011.6190097 dblp:conf/acssc/WirthDBTH11 fatcat:mtwipbpnnfdwrkkzufuxhc6zfm