1. Advanced interference coordination techniques in heterogeneous cellular networks Collaborator: Naga Bhushan, Mohammad Jaber Borran, Aamod Khandekar, Ritesh Madan, and Ashwin Sampath Speaker: Tingfang Ji ITA Workshop 2010

2. 2 Introduction Definition: heterogeneous network –Extension of cellular networks –Base stations are not homogeneous Different transmit power Different topology (above rooftop, below rooftop, indoor) Different access policy (open access, closed access) Why are heterogeneous networks interesting? –Heterogeneous networks provide flexible coverage enhancement Pico/femto/relay cells provide coverage in areas with insufficient macro coverage –Traffic growth is outpacing macro network growth Heterogeneous networks offload traffic from macro networks Lower $/bit cost

3. 3 ITA Workshop 2010 Challenges and Solutions Severe interference issue –Example: Closed femto cells only serves subscribed users A macro cell mobile could get very close to a femto cell but not receiving service form the femto cell Interference from the femto could be much higher than the macro signal Interference from the non-member mobile could be much higher than a member mobile signal. –Similar issues exist for pico cells and relays with aggressive load balancing Solutions –Interference cancellation –Power control –TDM/FDM partitioning –Cooperative beamforming –Coherent joint transmission Femtos Macro User Macro Pico

4. 4 ITA Workshop 2010 Receiver Techniques Interference cancellation –In theory, interference cancellation could be used to peel off dominant interferers. –In practice, unicast data from interfering cell is hard to decode due to scrambling, adaptive modulation and coding, HARQ retransmissions. –In practice, interference cancellation is very effective for broadcast information Acquisition –Synchronization and broadcast signals from dominant interferer could be decoded and cancelled –3GPP 4G (OFDM) system have synchronization signals interfering with each other. Hence cancellation of interfering synchronization signals would allow the acquisition of the desired cell. Pilot –Common pilot are used for channel estimation –Pilots from interfering cell could also be decoded and cancelled Data –After all the overhead channels have been cancelled, only unicast data is left unprotected.

5. 5 ITA Workshop 2010 Sensitivity to Topology and Fairness Tradeoff between different schemes changes drastically with topology Fairness requirements also changes the tradeoff Rate region for two interfering links for medium interference case. Rate region for two interfering links for high interference case

6. 6 ITA Workshop 2010 Maximizing Sum Rate is Not the Ultimate Goal Utility function models perceived value of allocated rate to a user Utility = log(R 1 ) + 5log(R 2 ) Optimal rate pair via power control Utility = 2log(R 1 ) + log(R 2 ) Optimal rate pair achieved by time-sharing user 1 with power controlled point of user1, user 2

7. 7 ITA Workshop 2010 Semi-static Resource Partitioning Optimization problem:  j,r the fraction of resource r that is assigned to user j (by the scheduler at the serving node S(j)), r = 1, …, N r denotes the resource, ρ the spectral efficiency of a link π = (π i,r ) where π i,r  P i, i = 1, …, N denotes the transmitting node, P is the transmitting power Joint optimization of the resource partitioning and load balancing Allow mobile to connect to a very weak cell that’s lightly loaded Used in conjunction of interference mitigation

8. 8 ITA Workshop 2010 Capacity and Fairness Improvements with Interference Mitigation Iterative algorithm was defined to solve the optimization problem Assumptions: 3GPP LTE (OFDM) system: 57 macro cells (40 Watts), embedded with 228 pico cells (1 Watt), 25 mobiles/cell

9. 9 ITA Workshop 2010 Dynamic Interference Avoidance Short term interference avoidance (e.g., on a packet-by-packet basis) –Enables fast coordination for bursty and latency-sensitive traffic. –Optimizes capacity and user experience. –Uses simple over-the-air or over-the-backhaul message. Define Priority Metrics –QoS, HTTP: pkt_delay*rate (i.e., assume same delay target across cells) –Best effort: rate/avg. rate Simulations –Apartment building with 25 units, on average 5 units have femto cells, one mobile in each femto cell. –Mixed background downloading and HTTP traffic, 75% HTTP, 25% background download. –HTTP pkt inter-arrival times geometric with mean 1 ms pkt Size = 6KB call size is Pareto distributed –min 30 KB, max 10 MB, mean 200 KB reading time geometric with mean 4 seconds

10. 10 ITA Workshop 2010 Cooperative Beamforming The baseline: –Every active femto schedules its mobile at every scheduling instance –Eigen-beamforming with equal power distribution across layers –Dynamic rank selection based on the maximum spectral efficiency. CB scheme: –Spatial coordination information is sent from mobile to the neighboring cell Compressed CSI and utility –Neighboring femto choose from two options Coordinated silencing Signal-to-leakage ratio (SLR) beamforming –Optimize local utility Significant gain at tail with slight loss at mean

11. 11 ITA Workshop 2010 Other Practical Constraints Coherent processing (joint transmission) is optimal at a high cost –Backhaul load could be orders of magnitude higher –Backhaul latency requirement is high Sum power optimization is useful, but peak power constraint has to be enforced at each node –Can not move power between different nodes, especially considering heterogeneous networks with vastly different power level Orthogonalization in frequency provides limited protection –Adjacent channel interference (Tx leakage and Rx sensitivity) is regulated by FCC and standards bodies –It is often found to be insufficient in dominant interference scenarios Spectrum availability –TDD spectrum requires tight coordination between adjacent carriers –FDD full duplex devices requires large frequency separate due to self-dense (Tx, RX desense)

12. 12 ITA Workshop 2010 Conclusions Heterogeneous networks provides capacity/coverage gain compared to conventional macro networks, while introducing severe interference due to the low power nodes and restricted association Investigated an array of techniques: –Interference cancellation –Resource partitioning and adaptive association, –Dynamic packet-by-packet negotiation for QoS and capacity optimization –Coordinated beamforming, Lesson learnt: –Optimality depends heavily on topology, fairness and QoS –Joint optimization of resource allocation and association provides significant gain –RF planning is difficult for unplanned networks, distributed iterative solutions are more robust and efficient