Characterizing Energy Efficiency and Deployment Efficiency Relations for Green Architecture Design By Yan Chen, Shunqing Zhang and Shugong Xu Haluk Celebi

Objective &Results There is a cost for deployment of base stations in terms of capital expenditures (price of BS asset etc..), and operational expenditures (electricity bills, maintenance, etc..). The goal is the paper is to find the tradeoff between energy efficiency and deployment efficiency. Results: There might not be a simple tradeoff between energy efficiency and deployment efficiency depending on the environment (rural, urban, or path loss)

Outline of the paper I)Literature review II) Formal definitions of energy efficiency and deployment efficiency III)Analytical model of energy efficiency and deployment efficiency IV)Numerical examples and discussions

Summary Two metrics Total Network throughput Tnet (unit: bits) Summation of average site throughput Ts within the network area. Defines how many bits on average we have in the given area A. Energy efficiency (EE) (unit: bits/Joule) Total network throughput per unit bandwidth Ratio Tnet/ Enet, where Enet is network energy consumption. Deployment efficiency (DE) (bits/€) total network throughput per unit bandwidth over the network deployment cost We are trying to find the tradeoff between this two.

Summary-2  Modeling energy consumption (sketch) 1. Calculation of I(d) using fluid model 2.Use linear power consumption model 3.Using Shannon`s capacity formula, find required SNR at distance R, or SINR using I(d)[1] as well From the required SINR value, find Ptx of base station at a distance R Using effective working period Thus

Summary-3  Network deployment cost (sketch) Two types of BS, macro BS, and micro BS Macro BS serve for coverage from 500 m to a few kilometers Micro BS serves comparatively smaller area with radius less than 500m The breakdowns of the total CapEX and annual average OpEx per cell (normalized by the equipment cost of a macro BS, denoted by c 0 ) are

Summary-3  Total network throughput Its assumed in the paper that BS has rate adaptation so that the mobile user near Bs enjoys larger rate. Adaptive modulation and coding scheme (AMC) is applied at each BS to model average rate that a mobile gets at a distance R

Numerical Examples and Discussions This sections illustrates how EE, and DE vary with the cell radius R As R increases, BS spends more power so that site annual cost increases When R is small, or path loss component is small (3 or 3.5), non-transmit related power is dominant –BS is idle most of the time When path loss component is large (as in 4.5) in urban area, network annual cost increases because transmit power becomes dominant and curve goes up with R↑

Figure shows how site cost and annual cost vary. Jump at R=500 is due to switching from microCell to macroCell As seen for large α (4.5), network cost increases. Which means that less number of BS in rural environment will decrease the cost, whereas it will increase the cost in urban environment.

This graph contradicts our intuition about how EE varies with R. We assume EE increase monotonically with decreasing cell size, however this is not true when mon-transmit power is included. Monotonicity is no longer preserved. If we know maximum achievable energy efficiency (for given deployment efficiency), we can optimal R from this graph

Left graph shows transmit EE of w.r.t. DE. Right graph shows Total EE w.r.t. D.E Only difference is that left graph doesn`t consider power consumption in idle state. Right graph considers both transmit, and non-transmit power. In graph fits our intuition. Less number of BS(high DE), less EE because of high transmit power In right graph, there is no clear trade-off (for α=4.5)

Conclusions There is not a simple tradeoff between EE, and DE We may gain some insights on the optimal cell size for green cellular architecture. For any target network and given deployment budget, we can first calculate the deployment Efficiency, and from deployment efficiency we can find maximum achievable energy efficiency (in figure 4) From figure 3 (EE vs R), we can get optimal cell radius R