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1 Wireless Communications Overview Wireless History 2 Ancient Systems: Smoke Signals, Carrier Pigeons, … Radio invented in the 1880s by Marconi Many sophisticated military radio systems were developed during and after WW2 Cellular has enjoyed exponential growth since 1988, with almost 5 billion users worldwide today Ignited the wireless revolution Voice, data, and multimedia becoming ubiquitous Use in third world countries growing rapidly Wifi also enjoying tremendous success and growth Wide area networks (e.g. Wimax) and short-range systems other than Bluetooth (e.g. UWB) less successful Future Wireless Networks 3 Ubiquitous Communication Among People and Devices Next-generation Cellular Wireless Internet Access Wireless Multimedia Sensor Networks Smart Homes/Spaces Automated Highways In-Body Networks All this and more … 4 Challenges Network/Radio Challenges Gbps data rates with “no” errors Energy efficiency Scarce/bifurcated spectrum Reliability and coverage Heterogeneous networks Seamless internetwork handoff Device/SoC Challenges Performance Complexity Size, Power, Cost High frequencies/mmWave Multiple Antennas Multiradio Integration Coexistance 5 GShort-Range AdHoc BT Cellular Radio GPS Cog Mem WiFi CPU mmW Software-Defined (SD) Radio: Is this the solution to the device challenges? BT Cellular FM/XM A/D GPS DVB-H A/D Apps Processor WLAN A/D Media Processor Wimax A/D DSP Multiband antennas and wideband A/Ds span BW of desired signals DSP programmed to process desired signal based on carrier frequency, signal shape, etc. Avoid specialized HW Today, this is not cost, size, or power efficient Compressed sensing may be a solution for sparse signals Careful what you wish for… 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 6 Scarce Wireless Spectrum $$$ and Expensive 7 On the Horizon: “The Internet of Things” Number of Connected Objects Expected to Reach 50bn by 2020 Are we at the Shannon limit of the Physical Layer? We are at the Shannon Limit “The wireless industry has reached the theoretical limit of how fast networks can go” K. Fitcher, Connected Planet “We’re 99% of the way” to the “barrier known as Shannon’s limit,” D. Warren, GSM Association Sr. Dir. of Tech. Shannon was wrong, there is no limit “There is no theoretical maximum to the amount of data that can be carried by a radio channel” M. Gass, 802.11 Wireless Networks: The Definitive Guide “Effectively unlimited” capacity possible via personal cells (pcells). S. Perlman, Artemis. What would Shannon say? We don’t know the Shannon capacity of most wireless channels Time-varying channels. Channels with interference or relays. Cellular systems Ad-hoc and sensor networks Channels with delay/energy/$$$ constraints. Shannon theory provides design insights and system performance upper bounds Current/Emerging Wireless Systems Current: 4G Cellular Systems (LTE-Advanced) 4G Wireless LANs/WiFi (802.11ac) Satellite Systems Bluetooth Zigbee WiGig Emerging 5G Cellular and WiFi Systems mmWave Systems Ad/hoc and Cognitive Radio Networks Energy-Harvesting Systems Much room For innovation 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 Spectral Reuse 13 Due to its scarcity, spectrum is reused In licensed bands and unlicensed bands BS Cellular, Wimax Wifi, BT, UWB,… Reuse introduces interference Cellular Systems: Reuse channels to maximize capacity Geographic region divided into cells Frequency/timeslots/codes/ reused at spatially-separated locations. Co-channel interference between same color cells. Base stations/MTSOs coordinate handoff and control functions Shrinking cell size increases capacity, as well as networking burden BASE STATION MTSO 14 Cellular Phones Everything Wireless in One Device 15 Cellular Phones Burden for this performance is on the backbone network Everything wireless in one device San Francisco BS BS LTE backbone is the Internet Internet Nth-Gen Cellular Phone System Nth-Gen Cellular Paris BS Much better performance and reliability than today - Gbps rates, low latency, 99% coverage indoors and out 4G/LTE Cellular Much higher data rates than 3G (50-100 Mbps) 3G systems has 384 Kbps peak rates Greater spectral efficiency (bits/s/Hz) Through MIMO, adaptive techniques, “ICIC” Flexible use of up to 100 MHz of spectrum 20 MHz spectrum allocation common Low packet latency (<5ms). Reduced cost-per-bit Support for multimedia All IP network Rethinking “Cells” in Cellular Small Cell Coop MIMO Relay DAS How should cellular systems be designed? Will gains in practice be big or incremental; in capacity or coverage? 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 relays, virtual MIMO, network coding. Are small cells the solution to increase cellular system capacity? Yes, with reuse one and adaptive techniques (Alouini/Goldsmith 1999) Area Spectral Efficiency A=.25D2p 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. The Future Cellular Network: Hierarchical Architecture Today’s architecture MACRO: solving • 3M Macrocells serving 5 billion users initial coverage• Anticipated 1M small cells per year issue, existing network 10x Lower COST/Mbps (more with WiFi Offload) PICO: solving street, enterprise & home coverage/capacit y issue 10x CAPACITY Improvement Macrocell Picocell Near 100% COVERAGE Femtocell Future systems require Self-Organization (SON) and WiFi Offload SON Premise and Architecture Mobile Gateway Or Cloud Node Installation Self Healing SoN Server Initial Measurements Self Configurati on Measureme nt SON Server IP Network Self Optimization X2 X2 SON is part of 3GPP/LTE standard Small cell BS Macrocell BS X2 X2 SW Agent Green” Cellular Networks Pico/Femto Coop MIMO Relay DAS How should cellular systems be redesigned for minimum energy? Research indicates that significant savings is possible Minimize energy at both the mobile and base 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 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 Wifi Networks Multimedia Everywhere, Without Wires 24 Wifi is today’s small cell 802.11ac • Streaming video • Gbps data rates • High reliability • Coverage inside and out Wireless HDTV and Gaming Wireless Local Area Networks (WLANs) 01011011 0101 1011 Internet Access Point WLANs connect “local” computers (100m range) Breaks data into packets Channel access is shared (random access + backoff) Backbone Internet provides best-effort service • Poor performance in some apps (e.g. video) 25 26 Evolution of WLAN Single Link from MAC layer to upper layer > 500 Mbps; Multi-user communication >1 Gbps. 802.11ac is based on 802.11n enhanced: OFDM/OFDMA、MIMO、SDMA、FEC、MAC layer optimization、Interference Coordination、 Cognitive Radio and Carrier Aggregation IMT-Advanced 20-40 MHz ==> 1 Gbps 2.4/5GHz, 20 - 40MHz 2.4GHz,20 MHz 2.4/5GHz, 20MHz > 54 Mbps 11/54 Mbps 600 Mbps Wireless LAN Standards 802.11b (Old – 1990s) Standard for 2.4GHz ISM band (80 MHz) Direct sequence spread spectrum (DSSS) Speeds of 11 Mbps, approx. 500 ft range 802.11a/g (Middle Age– mid-late 1990s) Standard for 5GHz band (300 MHz)/also 2.4GHz OFDM in 20 MHz with adaptive rate/codes Speeds of 54 Mbps, approx. 100-200 ft range Many WLAN cards have all 3 (a/b/g/n) 802.11n/ac(current) Standard in 2.4 GHz and 5 GHz band Adaptive OFDM /MIMO in 20/40/80/160 MHz Antennas: 2-4, up to 8 Speeds up to 600Mbps / 10 Gbps), approx. 200 ft range Other advances in packetization, antenna use, multiuser MIMO Why does WiFi performance suck? Carrier Sense Multiple Access: if another WiFi signal detected, random backoff Collision Detection: if collision detected, resend The WiFi standard lacks good mechanisms to mitigate interference, especially in dense AP deployments Multiple access protocol (CSMA/CD) from 1970s Static channel assignment, power levels, and carrier sensing thresholds In such deployments WiFi systems exhibit poor spectrum reuse and significant contention among APs and clients Result is low throughput and a poor user experience Why not use SoN for WiFi? SoN Controller - Channel Selection - Power Control - etc. SoN-for-WiFi: dynamic self-organization network software to manage of WiFi APs. Allows for capacity/coverage/interference mitigation tradeoffs. Also provides network analytics and planning. In fact, why not use SoN for all wireless networks? Software-Defined Wireless Networks TV White Space & Cognitive Radio mmWave networks Vehicle networks Software-Defined Network Virtualization Wireless Network Virtualization Layer Network virtualization combines hardware and software network resources and functionality into a single, software-based virtual network Software-Defined Wireless Network (SDWN) Architecture Video Freq. Allocation Vehicular Networks Security Power Control Self Healing ICIC App layer M2M QoS Opt. Health CS Threshold SW layer UNIFIED CONTROL PLANE HW Layer WiFi Cellular mmWave Cognitive Radio WiGig and mmWave WiGig Standard operating in 60 GHz band Data rates of 7-25 Gbps Bandwidth of around 10 GHz (unregulated) Range of around 10m (can be extended) Uses/extends 802.11 MAC Layer Applications include PC peripherals and displays for HDTVs, monitors & projectors mmWave Couples 60GHz with massive MIMO and better MAC Promises long-range communication w/Gbps data rates Hardware, propagation and system design challenges Much research on this topic today Wimax (802.16) Wide area wireless network standard System architecture similar to cellular Hopes to compete with cellular OFDM/MIMO is core link technology Operates in 2.5 and 3.5 MHz bands Different for different countries, 5.8 also used. Bandwidth is 3.5-10 MHz Fixed (802.16d) vs. Mobile (802.16e) Wimax Fixed: 75 Mbps max, up to 50 mile cell radius Mobile: 15 Mbps max, up to 1-2 mile cell radius 34 Satellite Systems Cover very large areas Different orbit heights GEOs (39000 Km) versus LEOs (2000 Km) • http://www.globalcomsatphone.com/globalcom/leo_geo.html Optimized for one-way transmission Radio (XM, Sirius) and movie (SatTV, DVB/S) broadcasts Most two-way systems struggling or bankrupt Global Positioning System (GPS) use growing Satellite signals used to pinpoint location Popular in cell phones, PDAs, and navigation devices 35 Paging Systems Broad coverage for short messaging Message broadcast from all base stations Simple terminals Optimized for 1-way transmission Answer-back hard Overtaken by cellular 36 Bluetooth Cable replacement RF technology (low cost) Short range (10m, extendable to 100m) 2.4 GHz band (crowded) 1 Data (700 Kbps) and 3 voice channels, up to 3 Mbps Widely supported by telecommunications, PC, and consumer electronics companies Few applications beyond cable replacement Bluetooth 4.0, June 30, 2010 Classic Bluetooth Bluetooth high speed (based on WiFi) Bluetooth low energy http://en.wikipedia.org/wiki/Bluetooth 8C32810.61-Cimini-7/98 37 Ultrawideband Radio (UWB) 38 UWB is an impulse radio: sends pulses of tens of picoseconds(10-12) to nanoseconds (10-9) Duty cycle of only a fraction of a percent A carrier is not necessarily needed Uses a lot of bandwidth (GHz) High data rates, up to 500 Mbps 7.5 Ghz of “free spectrum” in the U.S. (underlay) Multipath highly resolvable: good and bad Limited commercial success to date IEEE 802.15.4 / ZigBee Radios 39 Low-Rate WPAN Data rates of 20, 40, 250 Kbps Support for large mesh networking or star clusters Support for low latency devices CSMA-CA channel access Very low power consumption Frequency of operation in ISM bands Focus is primarily on low power sensor networks 40 Tradeoffs 802.11n 3G Rate 802.11g/a Power 802.11b UWB Bluetooth ZigBee Range Spectrum Regulation 41 Spectrum a scarce public resource, hence allocated Spectral Allocation in US controlled by FCC (commercial) or OSM (defense) FCC auctions spectral blocks for set applications. Some spectrum set aside for universal use Worldwide spectrum controlled by ITU-R Regulation is a necessary evil. in multiple cognitive radio Innovations in regulation being considered worldwide paradigms Standards Interacting systems require standardization Companies want their systems adopted as standard Alternatively try for de-facto standards Standards determined by TIA/CTIA in US IEEE standards often adopted Process fraught with inefficiencies and conflicts Worldwide standards determined by ITU-T In Europe, ETSI is equivalent of IEEE Emerging Systems* • Software-defined and low-complexity radios • Cognitive radio networks • Ad hoc/mesh wireless networks • Sensor networks • Distributed control networks • The smart grid • Biomedical networks 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. 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? 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 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 Capacity under Sub-Nyquist Sampling Theorem 1: Theorem 2: Bank of Modulator+FilterSingle Branch Filter Bank t n(mTs ) q(t) zzzz p(t) zzzz zz y1[n] s1 (t ) zzzzzz s (t ) zzzz y[n] t n(mTs ) equals yi [n] si (t ) t n(mTs ) Theorem 3: sm (t ) Optimal among all time-preserving nonuniform sampling techniques of rate fs (ISIT’12; Arxiv) ym [n] Joint Optimization of Input and Filter Bank Selects the m branches with m highest SNR Example (Bank of 2 branches) low SNR H ( f 2kfs ) X f 2kfs H ( f kfs ) highest SNR X f kfs highest SNR Xf H ( f kfs ) low SNR X f kfs N ( f kfs ) S ( f kfs ) H( f ) 2nd N ( f 2kfs ) S ( f 2kfs ) N( f ) S( f ) Y1 f Capacity monotonic in fs Y2 f N ( f kfs ) S ( f kfs ) How does this translate to practical modulation and coding schemes Reduced-Dimension Network Design Random State Evolution Stochastic Control Resource Management Reduced-Dimension Representation Sampling and Inference Cognitive Radios CRTx IP NCR NCR CR MIMO Cognitive Underlay CR CRRx NCRTx NCRRx Cognitive Overlay Cognitive radios support new users in existing crowded spectrum without degrading licensed users Utilize advanced communication and DSP techniques Coupled with novel spectrum allocation policies Multiple paradigms (MIMO) Underlay (interference below a threshold) Interweave finds/uses unused time/freq/space slots Overlay (overhears/relays primary message while cancelling interference it causes to cognitive receiver) Cognitive Radio Multiple Paradigms 52 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 Knowledge and Complexity Underlay Systems Cognitive radios determine the interference their transmission causes to noncognitive nodes Transmit if interference below a given threshold IP NCR NCR CR CR 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 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 Overlay Systems Cognitive user has knowledge of other user’s message and/or encoding strategy Used to help noncognitive transmission Used to presubtract noncognitive interference CR NCR RX1 RX2 Performance Gains from Cognitive Encoding outer bound our scheme prior schemes Only the CR transmits Ad-Hoc/Mesh Networks Outdoor Mesh Indoor Mesh ce 57 Ad-Hoc Networks Peer-to-peer communications No backbone infrastructure or centralized control Routing can be multihop. Topology is dynamic. Fully connected with different link SINRs Open questions Fundamental capacity region Resource allocation (power, rate, spectrum, etc.) Routing 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 Design Issues 60 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 adhoc networks are generally ad-hoc. Crosslayer design critical and very challenging. Energy constraints impose interesting design tradeoffs for communication and networking. General Relay Strategies TX1 RX1 Y4=X1+X2+X3+Z4 X1 relay Y3=X1+X2+Z3 TX2 X3= f(Y3) X2 Y5=X1+X2+X3+Z5 RX2 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 Beneficial to forward both interference and message • For large powers, this strategy approaches capacity Wireless Sensor Networks 63 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 Energy-Constrained Nodes 64 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. Channel Coding Channel coding used to reduce error probability Metric is typically the coding gain Also measured against Shannon limit Shannon Limit Pb Uncoded 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 Coded Coding Gain SNR Green Codes (for short distances) Is Shannon-capacity still a good metric for system design? Coding for minimum total power Is Shannon-capacity still a good metric for system design? X5 B1 B2 B3 Computational Nodes B4 X6 X7 X8 On-chip interconnects Extends early work of El Gamal et. al.’84 and Thompson’80 Fundamental area-time-performance tradeoffs For encoding/decoding “good” codes, X5 B1 B2 B3 B4 Area occupied by wires Encoding/decoding clock cycles Stay away from capacity! Close to capacity we have • Large chip-area, more time/power Regular LDPCs closer to bound than capacity-approaching LDPCs! Need novel code designs with short wires, good performance X6 X7 X8 69 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 The Smart Grid: Fusion of Sensing, Control, Communications Open problems: - Optimize sensor placement in the grid - New designs for energy routing and distribution - Develop control strategies to robustify the grid - Look at electric cars for storage and path planning carbonmetrics.eu Much progress in some areas Smart metering Green buildings and structures Optimization Demand response Modeling and simulation Incentives and economics But many of the hard research questions have not yet been asked Current work: sensor placement, fault Detection and control with sparse sensors Applications in Health, Biomedicine and Neuroscience Body-Area Networks Doctor-on-a-chip Wireless Network Neuro/Bioscience - EKG signal reception/modeling - Brain information theory - Nerve network (re)configuration - Implants to monitor/generate signals -In-brain sensor networks - SP/Comm applied to bioscience Recovery from Nerve Damage Gene Expression Profiling 70 genes RNA extraction labeling hybridization scan tumor tissue Gene expression profiling predicts clinical outcome of breast cancer (Van’t Veer et al., Nature 2002.) Immune cell infiltration into tumors good prognosis. Gene expression measurements: a mix of many cell types Gene Signatures Cell-type Proportion Cell Types 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” Vennet et 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 techniques from hyperspectroscopy (Piper et al, AMOS 2004) assuming “C”, “G” and “k” unknown Beat existing techniques, even nonblind ones Pathways through the brain Direct information (DI) inference Neuron layout B A C B A E C D E D 𝑛 𝐼 𝑋𝑛 → 𝑌𝑛 = 𝐻 𝑌𝑛 − 𝐻 𝑌𝑖 |𝑌 𝑖−1 , 𝑋 𝑖 𝑖=1 DI inference with delay lower bound B A C E 𝑛 𝐼 𝑋𝑛 → 𝑌𝑛 = 𝐻 𝑌𝑛 − D 𝐻 𝑌𝑖 |𝑌 𝑖−1 , 𝑋 𝑖−𝐷 𝑖=1 Constrained DI inference B A C E 𝑛 D 𝑖−𝐷 𝐻 𝑌𝑖 |𝑌 𝑖−1 , 𝑋𝑖−𝐷−𝑁 𝐼 𝑋𝑛 → 𝑌𝑛 = 𝐻 𝑌𝑛 − 𝑖=1 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 Main Points 77 The wireless vision encompasses many exciting applications Technical challenges transcend all system design layers 5G networks must support higher performance for some users and extreme energy efficiency for others Cloud-based software to dynamically control and optimize wireless networks needed (SDWN) Innovative wireless design needed for 5G cellular/WiFi, mmWave systems, massive MIMO, and IoT connectivity Standards and spectral allocation heavily impact the evolution of wireless technology