Wireless Communications: Lecture 3 Professor Andrea Goldsmith Short Course: Wireless Communications: Lecture 3 Professor Andrea Goldsmith UCSD March 22-23 La Jolla, CA

Lecture 2 Summary

Capacity of Flat Fading Channels Four cases Nothing known Fading statistics known Fade value known at receiver Fade value known at receiver and transmitter Optimal Adaptation Vary rate and power relative to channel Optimal power adaptation is water-filling Exceeds AWGN channel capacity at low SNRs Suboptimal techniques come close to capacity

Frequency Selective Fading Channels For TI channels, capacity achieved by water-filling in frequency Capacity of time-varying channel unknown Approximate by dividing into subbands Each subband has width Bc (like MCM). Independent fading in each subband Capacity is the sum of subband capacities 1/|H(f)|2 Bc P f

Linear Modulation in Fading BER in AWGN: In fading gs and therefore Ps random Performance metrics: Outage probability: p(Ps>Ptarget)=p(g<gtarget) Average Ps , Ps: Combined outage and average Ps

Variable-Rate Variable-Power MQAM Uncoded Data Bits Delay Point Selector M(g)-QAM Modulator Power: S(g) To Channel g(t) log2 M(g) Bits One of the M(g) Points BSPK 4-QAM 16-QAM Goal: Optimize S(g) and M(g) to maximize EM(g)

Optimal Adaptive Scheme Power Water-Filling Spectral Efficiency Practical Constraints Constellation and power restriction Constellation updates. Estimation error and delay. gk g Equals Shannon capacity with an effective power loss of K. g

Diversity Send bits over independent fading paths Combine paths to mitigate fading effects. Independent fading paths Space, time, frequency, polarization diversity. Combining techniques Selection combining (SC) Equal gain combining (EGC) Maximal ratio combining (MRC) Can almost completely eliminate fading effects

Multiple Input Multiple Output (MIMO)Systems MIMO systems have multiple (r) transmit and receiver antennas With perfect channel estimates at TX and RX, decomposes into r independent channels RH-fold capacity increase over SISO system Demodulation complexity reduction Can also use antennas for diversity (beamforming) Leads to capacity versus diversity tradeoff in MIMO

MCM and OFDM MCM splits channel into flat fading subchannels Fading across subcarriers degrades performance. Compensate through coding or adaptation OFDM efficiently implemented using FFTs OFDM challenges are PAPR, timing and frequency offset, and fading across subcarriers x cos(2pf0t) cos(2pfNt) S R bps R/N bps QAM Modulator Serial To Parallel Converter

Spread Spectrum In DSSS, bit sequence modulated by chip sequence Spreads bandwidth by large factor (K) Despread by multiplying by sc(t) again (sc(t)=1) Mitigates ISI and narrowband interference ISI mitigation a function of code autocorrelation Must synchronize to incoming signal RAKE receiver used to combine multiple paths S(f) s(t) sc(t) Sc(f) S(f)*Sc(f) 1/Tb 1/Tc Tc Tb=KTc 2

Course Outline Overview of Wireless Communications Path Loss, Shadowing, and WB/NB Fading Capacity of Wireless Channels Digital Modulation and its Performance Adaptive Modulation Diversity MIMO Systems Multicarrier Modulation Spread Spectrum Multiuser Communications Wireless Networks Future Wireless Systems Lecture 3

Course Outline Overview of Wireless Communications Path Loss, Shadowing, and WB/NB Fading Capacity of Wireless Channels Digital Modulation and its Performance Adaptive Modulation Diversity MIMO Systems Multicarrier Modulation Spread Spectrum Multiuser Communications Wireless Networks Future Wireless Systems

Multiuser Channels: Uplink and Downlink Downlink (Broadcast Channel or BC): One Transmitter to Many Receivers. Uplink (Multiple Access Channel or MAC): Many Transmitters to One Receiver. x h3(t) R3 x h22(t) x h21(t) x h1(t) R2 R1 Uplink and Downlink typically duplexed in time or frequency

Bandwidth Sharing Frequency Division Time Division Code Division Code Space Time Frequency Frequency Division Time Division Code Division Multiuser Detection Space (MIMO Systems) Hybrid Schemes Code Space Time Frequency Code Space Time Frequency 7C29822.033-Cimini-9/97

Multiple Access SS Interference between users mitigated by code cross correlation In downlink, signal and interference have same received power In uplink, “close” users drown out “far” users (near-far problem) a2 a1

Multiuser Detection In all CDMA systems and in TD/FD/CD cellular systems, users interfere with each other. In most of these systems the interference is treated as noise. Systems become interference-limited Often uses complex mechanisms to minimize impact of interference (power control, smart antennas, etc.) Multiuser detection exploits the fact that the structure of the interference is known Interference can be detected and subtracted out Better have a darn good estimate of the interference

Ideal Multiuser Detection Signal 1 - = A/D Signal 1 Demod A/D A/D A/D A/D Iterative Multiuser Detection Signal 2 Signal 2 Demod - = Why Not Ubiquitous Today? Power and A/D Precision

RANDOM ACCESS TECHNIQUES Dedicated channels wasteful for data use statistical multiplexing Techniques Aloha Carrier sensing Collision detection or avoidance Reservation protocols PRMA Retransmissions used for corrupted data Poor throughput and delay characteristics under heavy loading Hybrid methods 7C29822.038-Cimini-9/97

Multiuser Channel Capacity Fundamental Limit on Data Rates Capacity: The set of simultaneously achievable rates {R1,…,Rn} R3 R2 R3 R2 R1 R1 Main drivers of channel capacity Bandwidth and received SINR Channel model (fading, ISI) Channel knowledge and how it is used Number of antennas at TX and RX Duality connects capacity regions of uplink and downlink

Multiuser Fading Channel Capacity Ergodic (Shannon) capacity: maximum long-term rates averaged over the fading process. Shannon capacity applied directly to fading channels. Delay depends on channel variations. Transmission rate varies with channel quality. Zero-outage (delay-limited*) capacity: maximum rate that can be maintained in all fading states. Delay independent of channel variations. Constant transmission rate – much power needed for deep fading. Outage capacity: maximum rate that can be maintained in all nonoutage fading states. Constant transmission rate during nonoutage Outage avoids power penalty in deep fades

Broadcast Channels with ISI w1k H1(w) xk w2k H2(w) ISI introduces memory into the channel The optimal coding strategy decomposes the channel into parallel broadcast channels Superposition coding is applied to each subchannel. Power must be optimized across subchannels and between users in each subchannel.

Broadcast MIMO Channel Non-degraded broadcast channel Channel on the left is the downlink (BC), channel on the right is the uplink (MAC) – notice dual channels have the same channel gains MIMO MAC capacity easy to find MIMO BC channel capacity obtained using dirty paper coding and duality with MIMO MAC

Course Outline Overview of Wireless Communications Path Loss, Shadowing, and WB/NB Fading Capacity of Wireless Channels Digital Modulation and its Performance Adaptive Modulation Diversity MIMO Systems Multicarrier Modulation Spread Spectrum Multiuser Communications Wireless Networks Future Wireless Systems

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

Cellular System Design BASE STATION Frequencies, timeslots, or codes reused at spatially-separate locations Efficient system design is interference-limited Base stations perform centralized control functions Call setup, handoff, routing, adaptive schemes, etc.

Design Issues Reuse distance Cell size Channel assignment strategy Interference management Multiuser detection MIMO Dynamic resource allocation 8C32810.44-Cimini-7/98

Interference: Friend or Foe? If treated as noise: Foe If decodable: Neither friend nor foe Increases BER, reduces capacity Multiuser detection can completely remove interference

MIMO in Cellular How should MIMO be fully exploited? At a base station or Wifi access point MIMO Broadcasting and Multiple Access Network MIMO: Form virtual antenna arrays Downlink is a MIMO BC, uplink is a MIMO MAC Can treat “interference” as a known signal or noise Can cluster cells and cooperate between clusters

MIMO in Cellular: Other Performance Benefits Antenna gain  extended battery life, extended range, and higher throughput Diversity gain  improved reliability, more robust operation of services Multiplexing gain  higher data rates Interference suppression (TXBF)  improved quality, reliability, robustness Reduced interference to other systems

Rethinking “Cells” in Cellular How should cellular systems be designed? Coop MIMO Femto 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 Femtocells create a cell within a cell Mobile cooperation via relays, virtual MIMO, network coding.

Cellular System Capacity Shannon Capacity Shannon capacity does no incorporate reuse distance. Some results for TDMA systems with joint base station processing User Capacity Calculates how many users can be supported for a given performance specification. Results highly dependent on traffic, voice activity, and propagation models. Can be improved through interference reduction techniques. (Gilhousen et. al.) Area Spectral Efficiency Capacity per unit area In practice, all techniques have roughly the same capacity

Area Spectral Efficiency BASE STATION A=.25D2p = S/I increases with reuse distance. For BER fixed, tradeoff between reuse distance and link spectral efficiency (bps/Hz). Area Spectral Efficiency: Ae=SRi/(.25D2p) bps/Hz/Km2.

Average Area Spectral Efficiency ASE vs. Cell Radius fc=2 GHz 101 100 D=4R Average Area Spectral Efficiency [Bps/Hz/Km2] D=6R D=8R 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Cell Radius R [Km]

Improving Capacity Interference averaging Interference cancellation WCDMA Interference cancellation Multiuser detection Interference reduction Sectorization and smart antennas Dynamic resource allocation Power control MIMO techniques Space-time processing

Dynamic Resource Allocation Allocate resources as user and network conditions change BASE STATION Resources: Channels Bandwidth Power Rate Base stations Access Optimization criteria Minimize blocking (voice only systems) Maximize number of users (multiple classes) Maximize “revenue” Subject to some minimum performance for each user

Interference Alignment Addresses the number of interference-free signaling dimensions in an interference channel Based on our orthogonal analysis earlier, it would appear that resources need to be divided evenly, so only 2BT/N dimensions available Jafar and Cadambe showed that by aligning interference, 2BT/2 dimensions are available Everyone gets half the cake!

Ad-Hoc Networks Peer-to-peer communications Routing can be multihop. No backbone infrastructure or centralized control Routing can be multihop. Topology is dynamic. Fully connected with different link SINRs Open questions Fundamental capacity Optimal routing Resource allocation (power, rate, spectrum, etc.) to meet QoS -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).

Capacity Much progress in finding the Shannon capacity limits of wireless single and multiuser channels Little known about these limits for mobile wireless networks, even with simple models Recent results on scaling laws for networks No separation theorems have emerged Robustness, security, delay, and outage are not typically incorporated into capacity definitions

Network Capacity Results Multiple access channel (MAC) Broadcast channel Relay channel upper/lower bounds Interference channel Scaling laws Achievable rates for small networks

Capacity for Large Networks (Gupta/Kumar’00) Make some simplifications and ask for less Each node has only a single destination All nodes create traffic for their desired destination at a uniform rate l Capacity (throughput) is maximum l that can be supported by the network (1 dimensional) Throughput of random networks Network topology/packet destinations random. Throughput l is random: characterized by its distribution as a function of network size n. Find scaling laws for C(n)=l as n .

Extensions Fixed network topologies (Gupta/Kumar’01) Similar throughput bounds as random networks Mobility in the network (Grossglauser/Tse’01) Mobiles pass message to neighboring nodes, eventually neighbor gets close to destination and forwards message Per-node throughput constant, aggregate throughput of order n, delay of order n. Throughput/delay tradeoffs Piecewise linear model for throughput-delay tradeoff (ElGamal et. al’04, Toumpis/Goldsmith’04) Finite delay requires throughput penalty. Achievable rates with multiuser coding/decoding (GK’03) Per-node throughput (bit-meters/sec) constant, aggregate infinite. Rajiv will provide more details S D

Is a capacity region all we need to design networks? Yes, if the application and network design can be decoupled Application metric: f(C,D,E): (C*,D*,E*)=arg max f(C,D,E) (C*,D*,E*) Capacity Delay Energy

Ad Hoc Network Achievable Rate Regions All achievable rate vectors between nodes Lower bounds Shannon capacity An n(n-1) dimensional convex polyhedron Each dimension defines (net) rate from one node to each of the others Time-division strategy Link rates adapt to link SINR Optimal MAC via centralized scheduling Optimal routing Yields performance bounds Evaluate existing protocols Develop new protocols 3 1 2 4 5

Achievable Rates Achievable rate Capacity region vectors achieved by time division Capacity region is convex hull of all rate matrices A matrix R belongs to the capacity region if there are rate matrices R1, R2, R3 ,…, Rn such that Linear programming problem: Need clever techniques to reduce complexity Power control, fading, etc., easily incorporated Region boundary achieved with optimal routing

Example: Six Node Network Capacity region is 30-dimensional

Capacity Region Slice (6 Node Network) (a): Single hop, no simultaneous transmissions. (b): Multihop, no simultaneous (c): Multihop, simultaneous (d): Adding power control (e): Successive interference cancellation, no power control. Multiple hops Spatial reuse SIC Extensions: - Capacity vs. network size - Capacity vs. topology - Fading and mobility - Multihop cellular

Ad-Hoc Network 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.

Medium Access Control Nodes need a decentralized channel access method Minimize packet collisions and insure channel not wasted Collisions entail significant delay Aloha w/ CSMA/CD have hidden/exposed terminals 802.11 uses four-way handshake Creates inefficiencies, especially in multihop setting Hidden Terminal Exposed 1 2 3 4 5

Frequency Reuse More bandwidth-efficient Distributed methods needed. Dynamic channel allocation hard for packet data. Mostly an unsolved problem CDMA or hand-tuning of access points.

DS Spread Spectrum: Code Assignment Common spreading code for all nodes Collisions occur whenever receiver can “hear” two or more transmissions. Near-far effect improves capture. Broadcasting easy Receiver-oriented Each receiver assigned a spreading sequence. All transmissions to that receiver use the sequence. Collisions occur if 2 signals destined for same receiver arrive at same time (can randomize transmission time.) Little time needed to synchronize. Transmitters must know code of destination receiver Complicates route discovery. Multiple transmissions for broadcasting.

Transmitter-oriented Each transmitter uses a unique spreading sequence No collisions Receiver must determine sequence of incoming packet Complicates route discovery. Good broadcasting properties Poor acquisition performance Preamble vs. Data assignment Preamble may use common code that contains information about data code Data may use specific code Advantages of common and specific codes: Easy acquisition of preamble Few collisions on short preamble New transmissions don’t interfere with the data block

Introduction to Routing Destination Source Routing establishes the mechanism by which a packet traverses the network A “route” is the sequence of relays through which a packet travels from its source to its destination Many factors dictate the “best” route Typically uses “store-and-forward” relaying Network coding breaks this paradigm

Routing Techniques Table-driven Flooding Point-to-point routing Broadcast packet to all neighbors Point-to-point routing Routes follow a sequence of links Connection-oriented or connectionless Table-driven Nodes exchange information to develop routing tables On-Demand Routing Routes formed “on-demand” “A Performance Comparison of Multi-Hop Wireless Ad Hoc Network Routing Protocols”: Broch, Maltz, Johnson, Hu, Jetcheva, 1998.  

If exploited via cooperation and cognition Interference: Friend or Foe? If exploited via cooperation and cognition Friend Especially in a network setting

Cooperation in Wireless Networks Many possible cooperation strategies: Virtual MIMO , generalized relaying, interference forwarding, and one-shot/iterative conferencing Many theoretical and practice issues: Overhead, forming groups, dynamics, synch, …

Generalized Relaying Relay can forward all or part of the messages TX1 TX2 relay RX2 RX1 X1 X2 Y3=X1+X2+Z3 Y4=X1+X2+X3+Z4 Y5=X1+X2+X3+Z5 X3= f(Y3) Analog network coding 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

In fact, it can achieve capacity Ps S D P2 P4 For large powers Ps, P1, P2, analog network coding approaches capacity

How to use Feedback in Wireless Networks Output feedback CSI Acknowledgements Network/traffic information Something else Noisy/Compressed

MIMO in Ad-Hoc Networks Antennas can be used for multiplexing, diversity, or interference cancellation Cancel M-1 interferers with M antennas What metric should be optimized? Cross-Layer Design

Diversity-Multiplexing-Delay Tradeoffs for MIMO Multihop Networks with ARQ Error Prone Multiplexing Low Pe Beamforming ARQ ARQ H2 H1 MIMO used to increase data rate or robustness Multihop relays used for coverage extension ARQ protocol: Can be viewed as 1 bit feedback, or time diversity, Retransmission causes delay (can design ARQ to control delay) Diversity multiplexing (delay) tradeoff - DMT/DMDT Tradeoff between robustness, throughput, and delay

Multihop ARQ Protocols Fixed ARQ: fixed window size Maximum allowed ARQ round for ith hop satisfies Adaptive ARQ: adaptive window size Fixed Block Length (FBL) (block-based feedback, easy synchronization) Variable Block Length (VBL) (real time feedback) Block 1 ARQ round 1 ARQ round 2 ARQ round 3 Block 2 Receiver has enough Information to decode Block 1 ARQ round 1 ARQ round 2 round 3 Block 2 Receiver has enough Information to decode

Asymptotic DMDT Optimality Theorem: VBL ARQ achieves optimal DMDT in MIMO multihop relay networks in long-term and short-term static channels. Proved by cut-set bound An intuitive explanation by stopping times: VBL ARQ has the smaller outage regions among multihop ARQ protocols

Crosslayer Design in Ad-Hoc Wireless Networks Application Network Access Link Hardware Interdisciplinary research, design, and development very challenging, but necessary to meet the requirements of future wireless applications Substantial gains in throughput, efficiency, and end-to-end performance from cross-layer design

Delay/Throughput/Robustness across Multiple Layers Multiple routes through the network can be used for multiplexing or reduced delay/loss Application can use single-description or multiple description codes Can optimize optimal operating point for these tradeoffs to minimize distortion

Cross-layer protocol design for real-time media Loss-resilient source coding and packetization Application layer Rate-distortion preamble Congestion-distortion optimized scheduling Transport layer Congestion-distortion optimized routing Traffic flows Network layer Capacity assignment for multiple service classes Link capacities MAC layer Link state information Adaptive link layer techniques Joint with T. Yoo, E. Setton, X. Zhu, and B. Girod Link layer

Video streaming performance 5 dB 3-fold increase 100 1000 (logarithmic scale)

FLoWS C B A D Research Areas Capacity Delay Outage Robustness Network Fundamental Limits Cross-layer Design and End-to-end Performance Network Metrics Application Metrics (C*,D*,R*) Fundamental Limits of Wireless Systems (DARPA Challenge Program) FLoWS A B C D Research Areas Fundamental performance limits and tradeoffs Node cooperation and cognition Adaptive techniques Layering and Cross-layer design Network/application interface End-to-end performance optimization and guarantees

Approaches to Network Optimization* Dynamic Programming Network Utility Maximization Distributed Optimization Game Theory State Space Reduction Wireless NUM Multiperiod NUM Distributed Algorithms Mechanism Design Stackelberg Games Nash Equilibrium *Much prior work is for wired/static networks

Dynamic Programming (DP) Simplifies a complex problem by breaking it into simpler subproblems in recursive manner. Not applicable to all complex problems Decisions spanning several points in time often break apart recursively. Viterbi decoding and ML equalization can use DP State-space explosion DP must consider all possible states in its solution Leads to state-space explosion Many techniques to approximate the state-space or DP itself to avoid this

Network Utility Maximization Maximizes a network utility function Assumes Steady state Reliable links Fixed link capacities Dynamics are only in the queues U1(r1) U2(r2) Un(rn) Ri Rj 1 NUM views network protocols as optimization algorithms 2 Underlying problem is… 2.1 max the sum of the utilities 2.2 sum of the flows across a link cannot exceed the capacity of the link flow k routing Fixed link capacity Optimization is Centralized

Course Outline Overview of Wireless Communications Path Loss, Shadowing, and WB/NB Fading Capacity of Wireless Channels Digital Modulation and its Performance Adaptive Modulation Diversity MIMO Systems Multicarrier Modulation Spread Spectrum Multiuser Communications & Wireless Networks Future Wireless Systems

Scarce Wireless Spectrum $$$ and Expensive

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

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

Broadcast Channel with Cognitive Relays (BCCR) Enhance capacity via cognitive relays Cognitive relays overhear the source messages Cognitive relays then cooperate with the transmitter in the transmission of the source messages data Source Cognitive Relay 2

Wireless Sensor Networks Smart homes/buildings Smart structures Search and rescue Homeland security Event detection Battlefield surveillance Energy is the driving constraint Data flows to centralized location 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

Cross-Layer Tradeoffs under Energy Constraints Hardware All nodes have transmit, sleep, and transient modes Each node can only send a finite number of bits Link High-level modulation costs transmit energy but saves circuit energy (shorter transmission time) Coding costs circuit energy but saves transmit energy Access Power control impacts connectivity and interference Adaptive modulation adds another degree of freedom Routing: Circuit energy costs can preclude multihop routing

Modulation Optimization Tx Rx

Key Assumptions Narrow band, i.e. B<<fc Peak power constraint Power consumption of synthesizer and mixer independent of bandwidth B. Peak power constraint L bits to transmit with deadline T and bit error probability Pb. Square-law path loss for AWGN channel

Multi-Mode Operation Transmit, Sleep, and Transient Deadline T: Total Energy: Transmit Circuit Transient Energy where a is the amplifier efficiency and

Energy Consumption: Uncoded Two Components Transmission Energy: Decreases with Ton & B. Circuit Energy: Increases with Ton Minimizing Energy Consumption Finding the optimal pair ( ) For MQAM, find optimal constellation size (b=log2M)

Total Energy (MQAM)

Energy Consumption: Coded Coding reduces required Eb/N0 Reduced data rate increases Ton for block/convolutional codes Coding requires additional processing Is coding energy-efficient If so, how much total energy is saved.

MQAM Optimization Find BER expression for coded MQAM Assume trellis coding with 4.7 dB coding gain Yields required Eb/N0 Depends on constellation size (bk) Find transmit energy for sending L bits in Ton sec. Find circuit energy consumption based on uncoded system and codec model Optimize Ton and bk to minimize energy

Coded MQAM 90% savings at 1 meter. Reference system has bk=3 (coded) or 2 (uncoded) 90% savings at 1 meter.

Minimum Energy Routing 0.1 Red: hub node Green: relay/source 0.085 4 0.185 3 2 0.115 1 (0,0) (5,0) (10,0) (15,0) 0.515 • Optimal routing uses single and multiple hops • Link adaptation yields additional 70% energy savings

Cooperative Compression Source data correlated in space and time Nodes should cooperate in compression as well as communication and routing Joint source/channel/network coding What is optimal: virtual MIMO vs. relaying

“Green” Cellular Networks How should cellular systems be designed to conserve energy at both the mobile and base station The infrastructure and protocols should be redesigned based on miminum energy consumption, including Base station placement, cell size, distributed antennas Cooperation and cognition MIMO and virtual MIMO techniques Modulation, coding, relaying, routing, and multicast

Wireless Applications and QoS Wireless Internet access Nth generation Cellular Wireless Ad Hoc Networks Sensor Networks Wireless Entertainment Smart Homes/Spaces Automated Highways All this and more… Applications have hard delay constraints, rate requirements, and energy constraints that must be met These requirements are collectively called QoS

Challenges to meeting QoS Wireless channels are a difficult and capacity-limited broadcast communications medium Traffic patterns, user locations, and network conditions are constantly changing No single layer in the protocol stack can guarantee QoS: cross-layer design needed It is impossible to guarantee that hard constraints are always met, and average constraints aren’t necessarily good metrics.

Distributed Control over Wireless Links Automated Vehicles - Cars - UAVs - Insect flyers - Different design principles Control requires fast, accurate, and reliable feedback. Networks introduce delay and loss for a given rate. - Controllers must be robust and adaptive to random delay/loss. - Networks must be designed with control as the design objective.

Course Summary Overview of Wireless Communications Path Loss, Shadowing, and WB/NB Fading Capacity of Wireless Channels Digital Modulation and its Performance Adaptive Modulation Diversity MIMO Systems ISI Countermeasures Multicarrier Modulation Spread Spectrum Multiuser Communications & Wireless Networks Future Wireless Systems

Short Course Megathemes The wireless vision poses great technical challenges The wireless channel greatly impedes performance Channel varies randomly randomly Flat-fading and ISI must be compensated for. Hard to provide performance guarantees (needed for multimedia). We can compensate for flat fading using diversity or adapting. MIMO channels promise a great capacity increase. OFDM is the predominant mechanism for ISI compensation Channel sharing mechanisms can be centralized or not Biggest challenge in cellular is interference mitigation Wireless network design still largely ad-hoc Many interesting applications: require cross-layer design