EE 359: Wireless Communications

1. EE 359: Wireless Communications Advanced Topics in Wireless

2. Topics EE360 EE360 EE360 EE360 Future wireless networks Software-defined and low-complexity radios Advanced design of cellular systems Wireless network convergence and SDN Ad-hoc and sensor networks Cognitive and software-defined radios Energy Constraints in Wireless Systems Control over Wireless Neuroscience applications of wireless

3. Challenges BT • Network Challenges • Scarce spectrum • Demanding applications • Reliability • Ubiquitous coverage • Seamless indoor/outdoor operation • Device Challenges • Size, Power, Cost • MIMO in Silicon • Multiradio Integration • Coexistance FM/XM GPS Cellular DVB-H Apps Processor WLAN Media Processor Wimax

4. Software-Defined Radio 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 Multiband antennas and wideband A/Ds span BW of desired signals The DSP is programmed to process the desired signal based on carrier frequency, signal shape, etc. Avoids specialized hardware Compressed sensing may be a solution for sparse signals

5. 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 • Might be key enabler for SD and low-energy radios • Only for incoming signals “sparse” in time, freq., space, etc.

6. 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. • Can sparsity be exploited to reduce the complexity of communication system design?

7. 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

8. 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

9. 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)

10. 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)

11. Reduced-Dimension Network Design Reduced-Dimension Representation Random State Evolution Resource Management Stochastic Control Sampling and Inference

12. Scarce Wireless Spectrum $$$ and Expensive

13. BS Spectral Reuse In licensed bands and unlicensed bands Wifi, BT, UWB,… Cellular, Wimax Reuse introduces interference Due to its scarcity, spectrum is reused

14. Careful what you wish for… Growth in mobile data, massive spectrum deficit and stagnant revenues require technical and political breakthroughs for ongoing success of cellular

15. “Sorry, America: Your wireless airwaves are full” CNNMoneyTech – Feb. 2012 The “Spectrum Crunch”

16. 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.

17. 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

18. 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.

19. The Future Cellular Network: Hierarchical Architecture Today’s architecture • 3M Macrocells serving 5 billion users • Anticipated 1M small cells per year 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 Picocell Macrocell Femtocell Future systems requireSelf-Organization (SON) and WiFi Offload

20. SON Premise and Architecture Node Installation SelfHealing Mobile Gateway Or Cloud SoNServer Initial Measurements SON Server Self Configuration Measurement Self Optimization IP Network X2 X2 SW Agent X2 X2 • SON is part of 3GPP/LTE standard Small cell BS Macrocell BS

21. Convergence of Cellular and WiFi LTE.11 • Seamless handoff between networks • Load-balancing of air interface and backbone • Carrier-grade performance on both networks

22. Wireless networks are everywhere, yet… TV White Space & Cognitive Radio - Connectivity is fragmented - Capacity is limited (spectrum crunch and interference) - Roaming between networks is ad hoc

23. SDWN Architecture App layer Video Security Vehicular Networks M2M Health Freq. Allocation Power Control Self Healing ICIC QoS Opt. CS Threshold SW layer UNIFIED CONTROL PLANE Commodity HW Pico Cell WiFi AP Femto Cell Cognitive Radio

24. Cognitive Radio Paradigms Knowledge and Complexity • Underlay • Cognitive radios constrained to cause minimal interference to noncognitive radios • Interweave • Cognitive radios find and exploit spectral holes to avoid interfering with noncognitive radios • Overlay • Cognitive radios overhear and enhance noncognitive radio transmissions

25. Underlay Systems IP NCR CR CR NCR • Cognitive radios determine the interference their transmission causes to noncognitive nodes • Transmit if interference below a given threshold • The interference constraint may be met • Via wideband signalling to maintain interference below the noise floor (spread spectrum or UWB) • Via multiple antennas and beamforming

26. Interweave Systems • Measurements indicate that even crowded spectrum is not used across all time, space, and frequencies • Original motivation for “cognitive” radios (Mitola’00) • These holes can be used for communication • Interweave CRs periodically monitor spectrum for holes • Hole location must be agreed upon between TX and RX • Hole is then used for opportunistic communication with minimal interference to noncognitive users

27. Overlay Systems RX1 CR RX2 NCR • Cognitive user has knowledge of other user’s message and/or encoding strategy • Used to help noncognitive transmission • Used to presubtract noncognitive interference

28. outer bound • our scheme • prior schemes Performance Gains from Cognitive Encoding • Only the CR • transmits

29. Ad-Hoc Networks • Peer-to-peer communications. • No backbone infrastructure. • Routing can be multihop. • Topology is dynamic. • Fully connected with different link SINRs

30. Cooperation in Wireless Networks • Many possible cooperation strategies: • Virtual MIMO, relaying (DF, CF, AF), one-shot/iterative conferencing, and network coding • Nodes can use orthogonal or non-orthogonal channels. • Many practice and theoretical challenges • New full duplex relays can be exploited

31. Design Issues • Ad-hoc networks provide a flexible network infrastructure for many emerging applications. • The capacity of such networks is generally unknown. • Transmission, access, and routing strategies for ad-hoc networks are generally ad-hoc. • Crosslayer design critical and very challenging. • Energy constraints impose interesting design tradeoffs for communication and networking.

32. RX1 TX1 X1 Y4=X1+X2+X3+Z4 relay Y3=X1+X2+Z3 X3= f(Y3) Y5=X1+X2+X3+Z5 X2 TX2 RX2 General Relay Strategies • Can forward message and/or interference • Relay can forward all or part of the messages • Much room for innovation • Relay can forward interference • To help subtract it out

33. Beneficial to forward bothinterference and message • For large powers, this strategy approaches capacity

34. Wireless Sensor Networks Data Collection and Distributed Control • Smart homes/buildings • Smart structures • Search and rescue • Homeland security • Event detection • Battlefield surveillance • Energy (transmit and processing) is the driving constraint • Data flows to centralized location (joint compression) • Low per-node rates but tens to thousands of nodes • Intelligence is in the network rather than in the devices

35. Energy-Constrained Nodes • Each node can only send a finite number of bits. • Transmit energy minimized by maximizing bit time • Circuit energy consumption increases with bit time • Introduces a delay versus energy tradeoff for each bit • Short-range networks must consider transmit, circuit, and processing energy. • Sophisticated techniques not necessarily energy-efficient. • Sleep modes save energy but complicate networking. • Changes everything about the network design: • Bit allocation must be optimized across all protocols. • Delay vs. throughput vs. node/network lifetime tradeoffs. • Optimization of node cooperation.

36. Channel Coding Shannon Limit Pb Uncoded Coded SNR Coding Gain • Channel coding used to reduce error probability • Metric is typically the coding gain • Also measured against Shannon limit • Block and Convolutional Codes • Induce bandwidth expansion, reduce data rate • Easy to implement; well understood (decades of research) • Coded Modulation (Trellis/Lattice Codes; BICM) • Joint design of code and modulation mapper • Provides coding gain without bandwidth expansion • Can be directly applied to adaptive modulation • Turbo Codes (within a fraction of a dB of Shannon limit) • Clever encoding produces pseudo “random” codes easily • Decoder fairly complex • Low-density parity check (LDPC) Codes • State-of-the-art codes (802.11n, LTE) • Invented by Gallager in 1950s, reinvented in 1990s • Low density parity check matrix • Encoder complex; decoder uses belief propagation techniques

37. Green Codes (for short distances) Is Shannon-capacity still a good metric for system design?

38. 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

39. Fundamental area-time-performance tradeoffs Area occupied by wires Encoding/decoding clock cycles Regular LDPCs closer to bound than capacity-approaching LDPCs! • For encoding/decoding “good” codes, • Stay away from capacity! • Close to capacity we have • Large chip-area, more time/power Need novel code designs with short wires, good performance

40. 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

41. Why Green, why now? • The energy consumption of cellular networks is growing rapidly with increasing data rates and numbers of users • Operators are experiencing increasing and volatile costs of energy to run their networks • There is a push for “green” innovation in most sectors of information and communication technology (ICT) • There is a wave of consortia and government programs focused on green wireless

42. Distributed Control over Wireless Automated Vehicles - Cars - Airplanes/UAVs - Insect flyers Interdisciplinary design approach • Control requires fast, accurate, and reliable feedback. • Wireless networks introduce delay and loss • Need reliable networks and robust controllers • Mostly open problems : Many design challenges

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

44. Much progress in some areas But many of the hard research questions have not yet been asked Current work: sensor placement, fault Detection and control with sparse sensors Smart metering Green buildings and structures Optimization Demand response Modeling and simulation Incentives and economics

45. Applications in Health, Biomedicine and Neuroscience • Neuro/Bioscience • EKG signal reception/modeling • Brain information theory • Nerve network (re)configuration • Implants to monitor/generate signals • In-brain sensor networks Body-Area Networks Doctor-on-a-chip Recovery from Nerve Damage Wireless Network

46. Gene Expression Profiling 70 genes RNA extraction hybridization labeling scan tumor tissue Gene Signatures Cell-type Proportion • Gene expression profiling predicts clinical outcome of breast cancer (Van’tVeer et al., Nature 2002.) • Immune cell infiltration into tumors  good prognosis. • Gene expression measurements: a mix of many cell types Cell Types

47. Looks like CDMA “despreading” • Many gene expression deconvolution algorithms exist Shen-Orr et al., Nature methods 2010  known “C” and “k” Lu et al. , PNAS 2003  known “G” and “k” Vennetet al., Bioinformatics 2001  known “k” • Large databases exist where these parameters are unknown • Can we apply signal processing methods to blindly separate gene expression? • We adapt techniquesfrom hyperspectroscopy (Piper et al, AMOS 2004) assuming “C”, “G” and “k” unknown Beat existing techniques, even nonblind ones

48. Pathways through the brain Neuron layout Direct information (DI) inference B B B B A A A A E E E E DI inference with delay lower bound Constrained DI inference C C C C D D D D

49. Other Applications of Communications and Signal Processing to Brain Science • Epilepsy • Epileptic fits caused by an oscillating signal moving from one region to another. • When enough regions oscillate, a fit occurs • Working with epilepsy expert (MD) to understand how signal travels between regions. • Has sensors directly implanted in brain: can read signals and inject them. • Can we use drugs to block propagation or signal injection to cancel signals • Parkinsons • Creates 20 MHz noise in brain region • 120 MHz square wave injection mitigates the symptoms

50. The End • Thanks!!! • Have a great winter break Unless you are studying for quals – if so, good luck!