1. Rahul Amin, Dr. Jim Martin Clemson University, Clemson SC Contact: jim.martin@cs.clemson.edujim.martin@cs.clemson.edu http://www.cs.clemson.edu/~jmarty Dr. Ahmed Eltawil, Amr Hussien University of California, Irvine CA This project was sponsored in part by NSF through contract ECCS-0948132

2.  Introduction  Problem Statement  Background  System Description  Simulation Methodology  Results and Discussion  Conclusions

3.  Next Generation Wireless Networks (HetNets)  Made up of several Radio Access Technologies (RATs) such as 2G/3G/4G and Wi-Fi  User devices are reconfigurable (or multi-modal) and support a multitude of RATs  Joint allocation of network-wide resources in this hetnet environment is shown to be more efficient than ‘independent’ resource allocation by each RAT  Frameworks to support network-wide resource allocation process have been defined by 3GPP (CRRM, MRRM, JRRM) and IEEE (802.21, P1900.4) working groups  Our Previous Work  Studied the spectral efficiency vs. energy consumption tradeoffs while using reconfigurable devices in a hetnet system  For a ‘balanced’ network topology, Random Waypoint mobility model, and an FPGA- based reconfigurable device, we showed an increase in spectral efficiency of 75% at the cost of twice (2x) the energy consumption  The network topology, mobility model and hardware assumptions had a significant impact on ‘moderate’ improvements (75%) in spectral efficiency that were shown in our previous work

4.  Analyze spectral efficiency vs. energy consumption tradeoffs resulting from realistic hetnet assumptions that show a ‘significant’ improvement in spectral efficiency  Explore an ‘unbalanced’ network topology where resources of one cellular operator exceed those of another operator  Implement a clustered node movement pattern that changes a highly unfavorable situation to a favorable situation when network-wide resources are jointly allocated  Study the differences in energy consumption for several reconfigurable radio hardware assumptions: ▪ (i) ASIC-based radio ▪ (ii) FPGA-based radio ▪ (iii) Combination of ASIC and FPGA based radio

5.  Towards widespread adoption of HetNet system concept  Need: Due to proliferation of smartphones, user data demand is outpacing operator capacity  Steps taken: (i) ‘Wi-Fi offloading’ problem is being rigorously studied by cellular operators where cellular systems interoperate with 802.11 Wi-Fi networks (ii) ‘Femto-cell’ is utilized in practice today to increase spectral efficiency by supplementing the macro-cell with an overlay of smaller, co-operative networks  Energy-efficient reconfigurable device architectures (MPSoC) are being investigated  Based on various hardware components such as ASICs, FPGAs and DSPs

7. Nodes (user devices) either have (ASIC-based) static radios capable of operating in limited number of connectivity modes or they have (ASIC+FPGA or FPGA-based) reconfigurable radios capable of operating in any connectivity mode Nodes can connect to one or more Autonomous Wireless Systems (AWS) simultaneously Each AWS has a controller that represents all nodes in the AWS and that serves as gateway connecting the AWS with other AWSs or external networks A Global Resource Controller (GRC) implements a centralized scheduler that maps users to access technologies Wireless Virtual Link Layer multiplexes/de-multiplexes data for users associated to multiple AWSs Vertical Handovers are initiated by the GRC  Reconfiguration handoff - Radio reconfigures itself to operate over a different AWS

8.  We formulate use cases that assume presence of 2 major cellular carriers in a given area  Use Case 1  No co-operation between the two carriers  Users use multiple static radios that can connect to its own carrier’s access technologies  Use Case 2  Co-operation exists between the two carriers  Reconfigurable radios are used to support access technologies implemented by the other carrier

9.  Select trade-offs between two conflicting objectives  Maximize overall system throughput (Max sum-rate)  Maximize fairness amongst users (Max-Min Fair)  Comes up with user to access technology mappings every 1 second  Scheduler properties  Wi-Fi resources are evenly distributed  Cellular resources are distributed using a two-step approach: ▪ Allocate minimum required throughput  to each user using its best cellular radios ▪ Allocate unused resources to a window of  users with best connectivity parameters in increments of  ▪ Any user assigned total throughput of 1 Mbps is not assigned any further cellular resources  The parameters ,  and  help tune the fairness and overall system throughput characteristics obtained by the scheduler  For the results presented in this study, we use (α,β,ω) = (100k, 100k, 10)

10. 2 * 2 km 2 grid Carrier 1 – bad coverage area – 2 EVDO (3G) base-stations at the edge of the grid Carrier 2 – good coverage area – 1 HSPA (3G) and 1 LTE (4G) base-station at the center of the grid and 6 IEEE 802.11g (Wi-Fi) APs spread throughput the topology Each technology supports adaptive Modulation and Coding Scheme (MCS) MCS mapping for each user is determined based on distance of the user from the Base-Station 100 nomadic users (50 subscribed to Carrier 1 and 50 to Carrier 2) – Carrier 1 users grouped in 2 clusters; one cluster located at left edge of grid and the other at the right edge of the grid – Carrier 2 users are grouped in 1 cluster located at the center of the grid – Movement of each user follows Random Waypoint Model and is restricted to an area of 500 * 500 m 2 from its initial starting position Experimental Parameters – Network Outage: Varied between 0-25% in increments of 5% – Impact of Reconfiguration: Increased Energy Consumption and Communications Downtime multiplied by a scalar ∈ [0,1] Simulation run for 10,000 seconds

12. Carrier 2: WiFi Carrier 1: WiFi Carrier 2: WiFi Carrier 1: WiMAX Carrier 2: LTE Carrier 2: HSPA Carrier 1: EVDO

13. Worst Case: For no network outage and impact of reconfiguration equal to 1, the spectral efficiency gain for use case 2 (1.43 bits/sec/Hz) when compared to use case 1 (0.34 bits/sec/Hz) is 314.30%. Best Case: For 25% network outage and no impact of reconfiguration, the spectral efficiency gain for use case 2 (2.73 bits/sec/Hz) when compared to use case 1 (1.79 bits/sec/Hz) is 553.70%. Due to an unbalanced network topology and clustered movement pattern, the spectral efficiency increase which is in the range [314.3%, 553.7] is ‘significantly’ higher compared to [14.3%, 75%] obtained for our previous study 314.3% 553.7%

14. Energy Consumption Model: where: P dyn,FPGA represents run-time energy consumption of FPGA-based hardware P dyn,ASIC represents run-time energy consumption of ASIC-based hardware P rec,FPGA represents energy consumption of FPGA-based hardware during reconfiguration P rec,ASIC represents energy consumption of ASIC-based hardware when switching from ‘off’ to an ‘on’ mode ∝ run represents percentage of time system operates in regular mode ∝ rec represents percentage of time system operates in reconfiguration mode β percentage of hardware built using FGPA components 1 – β percentage of hardware built using ASIC components λ impact of reconfiguration The ratio of P dyn,FPGA :P dyn,ASIC is 12:1 P rec,FPGA and P rec,ASIC values are the same

15. Hardware Setting 1: Use Case 1 (Made up of completely ASIC components, i.e. β = 0) vs. Use Case 2 (Made up of completely FPGA components, i.e. β = 1 ) For increase in spectral efficiency of 314.30% shown earlier, for these hardware assumptions, the increase in energy consumption is 104.90% For increase in spectral efficiency of 553.70% also shown earlier, for these hardware assumptions, the increase in energy consumption is 614.9% Most pessimistic hardware implementation for a reconfigurable radio in practice. Almost linear tradeoff for worst case increase in energy consumption 104.9% 614.9%

16. Hardware Setting 2: Use Case 1 (Made up of completely ASIC components, i.e. β = 0) vs. Use Case 2 (Made up of 50% ASIC, 50% FPGA components, i.e. β = 0.5 ) For increase in spectral efficiency of 314.30% shown earlier, for these hardware assumptions, the increase in energy consumption is 70.0% For increase in spectral efficiency of 553.70% also shown earlier, for these hardware assumptions, the increase in energy consumption is 355.40% Most likely hardware implementation for a reconfigurable radio in practice. Increase in spectral efficiency is greater than increase in energy consumption 70.0% 355.4%

17. Hardware Setting 3: Use Case 1 (Made up of completely ASIC components, i.e. β = 0) vs. Use Case 2 (Made up of completely ASIC components, i.e. β = 0 ) For increase in spectral efficiency of 314.30% shown earlier, for these hardware assumptions, the increase in energy consumption is 35.10% For increase in spectral efficiency of 553.70% also shown earlier, for these hardware assumptions, the increase in energy consumption is 98.80% Hardware setting 3 is not really possible in practice. But gives an estimate of increase in energy consumption if the only difference between two use cases is the number of ‘reconfiguration handoffs’ experienced by devices 35.1% 98.8%

18.  The gains in spectral efficiency are much greater (increase from 75.5% to 553.7%) for an unbalanced network topology and clustered node movement pattern when compared to a balanced network topology and random waypoint movement pattern studied in our previous work  Based on the hardware choices, the increase in energy consumption can range from 98.80% to 614.90% for the corresponding increase in spectral efficiency of 553.7%  Depending on the number of possible modalities supported by user devices, it might be possible to attain a tradeoff in terms of lower energy consuming ASIC radios at the cost of decreased reconfigurable options  In the worst case, our results show a more or less linear trade-off between spectral efficiency and power consumption  This result is an artifact of our workload assumptions that assume traffic flows are always backlogged. In future work, we will explore more realistic scenarios that involve on/off traffic flows