Advanced Topics in Wireless EE 359: Wireless Communications Advanced Topics in Wireless

Topics 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 EE360 EE360 EE360 EE360

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

Software-Defined Radio Cellular Apps Processor BT Media GPS WLAN Wimax DVB-H FM/XM A/D A/D DSP A/D A/D 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 Today, this is not cost, size, or power efficient Compressed sensing may be a solution for sparse signals

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.

Scarce Wireless Spectrum $$$ and Expensive

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

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

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

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

Rethinking “Cells” in Cellular How should cellular systems be designed? Coop MIMO Small Cell Relay Will gains in practice be big or incremental; in capacity or coverage? DAS 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.

Are small cells the solution to increase cellular system capacity? Yes, with reuse one and adaptive techniques (Alouini/Goldsmith 1999) A=.25D2p 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.

10x CAPACITY Improvement The Future Cellular Network: Hierarchical Architecture 10x Lower COST/Mbps 10x CAPACITY Improvement Near 100% COVERAGE (more with WiFi Offload) Today’s architecture 3M Macrocells serving 5 billion users Anticipated 1M small cells per year MACRO: solving initial coverage issue, existing network PICO: solving street, enterprise & home coverage/capacity issue Macrocell Picocell Femtocell Future systems require Self-Organization (SON) and WiFi Offload

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

LTE Small Cell Challenges SoN algorithmic complexity: Optimal channel allocation was NP hard in 2G (IS-54) Now we have MIMO, multiple frequency bands, hierarchical networks, … Innovation needed to tame complexity 1st generation small cells didn’t do a good job in SoN Distributed versus centralized control Tradeoff latency/overhead for better performance Requires a mix of software-defined wireless networks and SoC-based solutions Backhaul Site Acquisition Resistance from macrocell vendors

WiFi is the small cell of today Primary access mode in Residences, Enterprises, and Outdoors The Big Problem with WiFi The WiFi standard lacks good mechanisms to mitigate interference in dense AP deployments 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? Server SoN Server provides radio resource management for embedded or stand-alone APs: Can drastically mitigate WiFi interference 802.11 standards-compliant AP measurements (MCS, RSSI, PER, etc.) AP parameters accessed through a simple interface Cloud-based Server can sit anywhere in Carrier or Enterprise network Provides carrier-grade WiFi in terms of throughput and outage Complements distributed SoC optimization

In fact, why not use SoN for all wireless networks? TV White Space & Cognitive Radio Vehicle networks mmWave networks

Software-Defined Wireless Network Architecture Video Security Vehicular Networks M2M App layer Health Freq. Allocation Power Control Self Healing ICIC QoS Opt. CS Threshold SW layer UNIFIED CONTROL PLANE HW Layer WiFi Cellular mmWave Cognitive Radio

Enablers for small cells More spectrum (esp. at high frequencies) SoN Technology (Massive) MIMO

What is Massive MIMO? A very large antenna array at each base station Dozens of devices Hundreds of BS antennas A very large antenna array at each base station An order of magnitude more antenna elements than in conventional systems A large number of users are served simultaneously An excess of base station (BS) antennas Essentially multiuser MIMO with lots of base station antennas T. L. Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3590–3600, Nov. 2010.

Massive MIMO vs. MIMO MIMO has well-known diversity/capacity gains in ptp channels In multiuser channels, MIMO introduces a spatial dimension that can be used for multiple access or interference reduction Massive MIMO does all this on a bigger scale; holds great promise to increase capacity and reduce energy. Challenges: complexity, cost, size, power consumption, channel estimation Massive MIMO especially well-suited to mmWave systems

Ongoing Massive MIMO Research Uplink design Optimized and robust constellation designs for multiuser uplink systems Noncoherent phase receivers Robust constellation design for phase receivers and comparison with energy receivers Comparison of noncoherent and coherent uplink designs Noncoherent downlink design Exploit diversity to replace beamforming Simple transmitter and receiver architectures Space-time coding and phase receivers Comparison of noncoherent and coherent massive MIMO systems Error exponent characterization for uplink and downlink Identify suitability of each scheme for different scenarios and channel models

Non-coherent optimal in scaling! Constellation design A minimum distance criterion provides a simple design Achieves asymptotically vanishing error probability Scaling Law result As number of receiver antennas increases, scaling law of the achievable rate without CSI is the same as that with perfect CSI: Same scaling applies to multiple single antenna TXs with 1 RX

Constellation Design Optimization in non-asymptotic regime Design Criterion: All points have same right and left error exponents Implies all point have approximately the same SER Robustness Can incorporate uncertainty of statistics into design All points must have minimum value of left and right error exponent for all possible statistics in uncertainty interval Distance between adjacent points increases as power increases

The robust design is not much worse than the “nominal” design Performance The robust design is not much worse than the “nominal” design The robust design is able to sustain a good performance in a mismatched channel Extensions Optimized and robust constellation designs for multiuser uplink systems Noncoherent phase receivers Comparison of noncoherent and coherent uplink designs Noncoherent versus coherent downlink designs Multiuser systems

Spectrum innovations beyond licensed/unlicensed paradigms

Cognitive Radio Paradigms 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 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 IP CR NCR NCR

Underlay MIMO Cognitive Radios (CR) “Intelligent” radio (SU) coexists with licensed user (PU) Uses MIMO technology for interference mitigation data If the SU transmits in 𝑁𝑢𝑙𝑙 𝑯 12 , interference to PU is zero interference How can the SU obtain the null space to the PU?

The whole process is only based on energy measurements Our approach The whole process is only based on energy measurements Power Control The SU-Tx intelligently choses the messages x(t) The PU-Rx notifies the PU-Tx to change its TX power (or MCS,…) Variation in the TX power of the PU-Tx is sensed by the SU-Tx The SU-Tx chooses the next message x(t) based only on the increase or decrease of the sensed power from the PU-TX. Tracking algorithm searches “around” outdated null space, can track simultaneously with sending data.

Blind Null Space Learning: Example Tracking meets peak interference constraints Rayleigh fading with maximum Doppler frequency Fd. 𝑃 𝑋 :𝑋-th percentile of decrease in interference 𝑁 𝑡 =2, 𝑁 𝑟 =1

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 RX1 CR RX2 NCR

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

Ad-Hoc Networks Peer-to-peer communications. No backbone infrastructure. Routing can be multihop. Topology is dynamic. Fully connected with different link SINRs -No backbone: nodes must self-configure into a network. -In principle all nodes can communicate with all other nodes, but multihop routing can reduce the interference associated with direct transmission. -Topology dynamic since nodes move around and link characteristics change. -Applications: appliances and entertainment units in the home, community networks that bypass the Internet. Military networks for robust flexible easily-deployed network (every soldier is a node).

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 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.

General Relay Strategies TX1 TX2 relay RX2 RX1 X1 X2 Y3=X1+X2+Z3 Y4=X1+X2+X3+Z4 Y5=X1+X2+X3+Z5 X3= f(Y3) 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 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 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. All the sophisticated high-performance communication techniques developed since WW2 may need to be thrown out the window. By cooperating, nodes can save energy

Channel Coding 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 Shannon Limit Pb Uncoded Coded SNR Coding Gain

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? Computational Nodes 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, Stay away from capacity! Close to capacity we have Large chip-area, more time/power Area occupied by wires Encoding/decoding clock cycles Regular LDPCs closer to bound than capacity-approaching LDPCs! Need novel code designs with short wires, good performance

Green” Cellular Networks Pico/Femto How should cellular systems be redesigned for minimum energy? Coop MIMO Relay DAS 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

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 For a given sampling mechanism (i.e. a “new” channel) (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+FilterSingle Branch  Filter Bank Theorem 3: Optimal among all time-preserving nonuniform sampling techniques of rate fs (ISIT’12; Arxiv) zzzzzzzzzz equals

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

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

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 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 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 Wireless Network 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 Microarrays make use of the DNA characteristic that is can hybridize. Meaning – that A sticks to T and G sticks to C. How does microarrays work: A microarray is a chip with a matrix. In each square in the matrix there are many probes that can stick to a specific messenger RNA (that represents an expressed gene). The matrix in the chip represents all human genes, for example. A tissue (ex. Tumor tissue) is taken, the messenger RNA is extracted from it, labeled and then hybridized onto the microarray chip. If a gene is expressed in the tissue (that is, if there is messenger RNA from that gene), then it sticks to the probes in the square that represent it on the chip, according to the quantity of mRNA that were in the tissue sampled. Then the chip is scanned and sent to image analysis to get the intensity of the binding, and to any additional data analysis. 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 C D E 𝐼 𝑋 𝑛 → 𝑌 𝑛 =𝐻 𝑌 𝑛 − 𝑖=1 𝑛 𝐻 𝑌 𝑖 | 𝑌 𝑖−1 , 𝑋 𝑖 Direct information (DI) inference Neuron layout B A C D E 𝐼 𝑋 𝑛 → 𝑌 𝑛 =𝐻 𝑌 𝑛 − 𝑖=1 𝑛 𝐻 𝑌 𝑖 | 𝑌 𝑖−1 , 𝑋 𝑖−𝐷 DI inference with delay lower bound B A C D E 𝐼 𝑋 𝑛 → 𝑌 𝑛 =𝐻 𝑌 𝑛 − 𝑖=1 𝑛 𝐻 𝑌 𝑖 | 𝑌 𝑖−1 , 𝑋 𝑖−𝐷−𝑁 𝑖−𝐷 Constrained DI inference B A C D E

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

The End Thanks!!! Good luck on the final and final project Have a great winter break Unless you are studying for quals – if so, good luck!