Future Directions in Communications Research

1. Future Directions in Communications Research Andrea Goldsmith Stanford University IEEE Communication Theory Workshop Maui, HI May 14, 2012

2. Future Wireless Networks Ubiquitous Communication Among People and Devices Next-generation Cellular Wireless Internet Access Wireless Multimedia Sensor Networks Smart Homes/Spaces Automated Highways Smart Grid Body-Area Networks All this and more …

3. Future Cell Phones BS BS BS Everything wireless in one device Burden for this performance is on the backbone network San Francisco Internet LTE backbone is the Internet Paris Nth-Gen Cellular Nth-Gen Cellular Phone System Much better performance and reliability than today - Gbps rates, low latency, 99% coverage indoors and out

4. Careful what you wish for… Exponential Mobile Data Growth Leading to massive spectrum deficit Source: FCC Source: Unstrung Pyramid Research 2010 Growth in mobile data, massive spectrum deficit and stagnant revenues require technical and political breakthroughs for ongoing success of cellular

5. Can we increase cellular system capacity to compensate for a 300MHz spectrum deficit? Without increasing cost? or power consumption? What would Shannon say?

6. Are we at the Shannon limit of the Physical Layer? • Time-varying channels with memory/feedback. We don’t know the Shannon capacity of most wireless channels • Channels with interference or relays. • Uplink and downlink channels with frequency reuse, i.e. cellular systems. • Channels with delay/energy/$$$ constraints.

7. Rethinking “Cells” in Cellular How should cellular systems be designed? • Traditional cellular design “interference-limited” • MIMO/multiuser detection can remove interference • Cooperating BSs form a MIMO array: what is a cell? • Relays change cell shape and boundaries • Distributed antennas move BS towards cell boundary • Small cells create a cell within a cell • Mobile cooperation via relaying, virtual MIMO, analog network coding. Coop MIMO Small Cell Relay Will gains in practice be big or incremental; in capacity or coverage? DAS

8. Are small cells the solution to increase cellular system capacity? A=.25D2p Yes, with reuse one and adaptive techniques (Alouini/Goldsmith 1999) Area Spectral Efficiency • S/I increases with reuse distance (increases link capacity). • Tradeoff between reuse distance and link spectral efficiency (bps/Hz). • Area Spectral Efficiency: Ae=SRi/(.25D2p) bps/Hz/Km2.

9. The Future Cellular Network: Hierarchical Architecture • Today’s architecture • 3M Macrocells serving 5 billion users • Anticipated MACRO: solving initial coverage issue, existing network 10x Lower COST/Mbps PICO:solving street, enterprise & home coverage/capacity issue (more with WiFi Offload) 10x CAPACITY Improvement Near 100%COVERAGE FEMTO: solving enterprise & home coverage/capacity issue Picocell Macrocell Femtocell Future systems requireSelf-Organization (SON) (and WiFi Offload)

10. SON Premise and Architecture Mobile Gateway Or Cloud Node Installation SelfHealing SoNServer Initial Measurements SON Server Self Configuration Measurement Self Optimization IP Network X2 X2 X2 X2 Small cell BS Macrocell BS

11. Algorithmic Challenge: Complexity • Optimal channel allocation was NP hard in 2nd-generation (voice) IS-54 systems • Now we have MIMO, multiple frequency bands, hierarchical networks, … • But convex optimization has advanced a lot in the last 20 years Innovation needed to tame the complexity

12. Green Cellular Networks How should cellular systems be redesigned for minimum energy? • Minimize energy at both the mobile andbase station via • New Infrastuctures: cell size, BS placement, DAS, Picos, relays • New Protocols: Cell Zooming, Coop MIMO, RRM, Scheduling, Sleeping, Relaying • Low-Power (Green) Radios: Radio Architectures, Modulation, coding, MIMO Pico/Femto Coop MIMO Relay Research indicates that significant savings is possible DAS Gerhard Fettweis’ talk

13. Antenna Placement in DAS • Optimize distributed BS antenna location • Primal/dual optimization framework • Convex; standard solutions apply • For 4+ ports, one moves to the center • Up to 23 dB power gain in downlink • Gain higher when CSIT not available 6 Ports 3 Ports

14. Coding for minimum total power Is Shannon-capacity still a good metric for system design? Computational Nodes On-chip interconnects Extends early work of El Gamal et. al.’84 and Thompson’80

15. Fundamental area-time-performance tradeoffs Area occupied by wires Encoding/decoding clock cycles • For encoding/decoding “good” codes, • Stay away from capacity! • Close to capacity • Large chip-area • More time • More power

16. Reduced-Dimension Communication System Design • Compressed sensing ideas have found widespread application in signal processing and other areas. • Basic premise of CS: exploit sparsity to approximate a high-dimensional system/signal in a few dimensions. • We ask: how can sparsity be exploited to reduce the complexity of communication system design

17. Sparsity: where art thou? To exploit sparsity, we need to find communication systems where it exists • Sparse signals: e.g. white-space detection • Sparse samples: e.g. sub-Nyquist sampling • Sparse users: e.g. reduced-dimension multiuser detection • Sparse state space: e.g reduced-dimension network control

18. Compressed Sensing • Basic premise is that signals with some sparse structure can be sampled below their Nyquist rate • Signal can be perfectly reconstructed from these samples by exploiting signal sparsity • This significantly reduces the burden on the front-end A/D converter, as well as the DSP and storage • Enabler for white space, SD and low-energy radios? • Only for incoming signals “sparse” in time, freq., space, etc. Rob Calderbank’s Talk

19. Software-Defined (SD) Radio: • Wideband antennas and A/Ds span BW of desired signals • DSP programmed to process desired signal: no specialized HW BT FM/XM GPS Cellular A/D A/D A/D A/D DVB-H DSP Apps Processor WLAN Media Processor Wimax Today, this is not cost, size, or power efficient Compressed sensing reduces A/D and DSP burden

20. Sparse Samples New Channel Sampling Mechanism (rate fs) • For a given sampling mechanism (i.e. a “new” channel) • What is the optimal input signal? • What is the tradeoff between capacity and sampling rate? • What known sampling methods lead to highest capacity? • What is the optimal sampling mechanism? • Among all possible (known and unknown) sampling schemes

21. Capacity under Sub-Nyquist Sampling equals zzzzzzzzzz zzzzzzzzzz • Theorem 1: • Theorem 2: • Bank of Modulator+FilterSingle Branch  Filter Bank • Theorem 3: • Optimal among all time-preservingnonuniform sampling techniques of rate fs (ISIT’12; Arxiv)

22. Joint Optimization of Input and Filter Bank low SNR Capacity monotonic in fs highest SNR 2nd highest SNR low SNR How does this translate to practical modulation and coding schemes • Selects the m branches with m highest SNR • Example (Bank of 2 branches)

23. Ideal Multiuser Detection - Signal 1 = Signal 1 Demod Iterative Multiuser Detection A/D A/D A/D A/D Signal 2 Signal 2 Demod MUD proposed for LTE (closes link at -7dB SNR) - = Why Not Ubiquitous Today? Power and A/D Precision

24. Reduced-Dimension MUD • Exploits that number of active users G is random and much smaller than total users (ala compressed sensing) • Using compressed sensing ideas, can correlate with M~log(G) waveforms • Reduced complexity, size, and power consumption Linear Transformation Decision Decision 10% Performance Degradation Decision

25. Reduced-Dimension Network Design Random Network State Reduced-Dimension State-Space Representation Projection Sparse Sampling Approximate Stochastic Control and Optimization Sampling and Learning Utility estimation

26. Feedback in Communications • Memoryless point-to-point channels: • Capacity unchanged with perfect feedback • Feedback drastically increases error exponent (L-fold exponential) • Feedback reduces energy consumption • Capacity of channels with feedback largely unknown • For channels with memory and perfect feedback • Under finite rate and/or noisy feedback • For multiuser channels • For multihop networks • ARQ is ubiquitious in practice: Why? - Output feedback - CSI - Acknowledgements - Something else?

27. Cognitive Radios PRx • Cognitive radios support new wireless users in existing crowded spectrum without degrading licensed users • Utilize advanced communication and DSP techniques • Coupled with novel spectrum allocation policies • Technology could • Revolutionize the way spectrum is allocated worldwide • Provide more bandwidth for new applications/services • Multiple paradigms • Underlay (exploiting unused spatial dimensions) and Overlay (exploiting relaying and interference cancellation) promising PTx MIMO Cognitive Underlay Cognitive Overlay IP CRTx CRRx CRRx CRTx PTx PRx

28. The Smart Grid:Fusion of Sensing, Control, Communications carbonmetrics.eu

29. Wireless and Health, Biomedicine and Neuroscience Body-Area Networks Todd Coleman’s Talk • Doctor-on-a-chip • Cell phone info repository • Monitoring, remote • intervention and services • The brain as a wireless network • EKG signal reception/modeling • Information flow (directed MI) • Signal encoding and decoding • Nerve network (re)configuration Cloud UbliMitra’s talk

30. Summary • Communications research alive and well • Communications technology will enable new applications that will change people’s lives worldwide • Design innovation will be needed to meet the requirements of next-generation wireless networks • A systems view and interdisciplinary design approach holds the key to these innovations