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EE360: Multiuser Wireless Systems and Networks Lecture 1 Outline Course Details Course Syllabus Course Overview Future Wireless Networks Multiuser Channels (Broadcast/MAC Channels) Spectral Reuse and Cellular Systems Ad-Hoc, Sensor, and Cognitive Radio Networks Bandwidth Sharing in Multiuser Channels Overview of Multiuser Channel Capacity Capacity of Broadcast Channels

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Course Information * People Instructor: Andrea Goldsmith, Packard 371, , OHs: MW after class and by appt. TA: Mainak Chowdhury, OHs: around HWs. Possibly around paper discussions organized via Piazza. Class Administrator: Pat Oshiro, Packard 365, *See web or handout for more details

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Course Information Nuts and Bolts Prerequisites: EE359 Course Time and Location: MW 9:30-10:45. Y2E Class Homepage: Contains all required reading, handouts, announcements, HWs, etc. Class Mailing List: ee360win01314-students (automatic for on- campus registered students). Guest list: send TA to sign up Tentative Grading Policy: 10% Class participation 10% Class presentation 15% Homeworks 15% Paper summaries 50% Project (10% prop, 15% progress report, 25% final report+poster)

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Grade Components Class participation Read the required reading before lecture/discuss in class Class presentation Present a paper related to one of the course topics HW 0: Choose 3 possible high-impact papers, each on a different syllabus topic, by Jan. 13. Include a paragraph for each describing main idea(s), why interesting/high impact Presentations begin Jan. 22. HW assignments Two assignments from book or other problems Paper summaries Two 2-4 page summaries of several articles Each should be on a different topic from the syllabus

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Term project on anything related to multiuser wireless Analysis, simulation and/or experiment Must contain some original research 2 can collaborate if project merits collaboration (scope, synergy) Must set up website for project Will post proposal, progress report, and final report to website Project proposal due Jan. 27 at midnight 1-2 page proposal with detailed description of project plan Comments Feb 3, revised project proposal due Feb 10. Progress report: due Feb. 24 at midnight 2-3 page report with introduction of problem being investigated, system description, progress to date, statement of remaining work Poster presentations last week of classes (We 3/13, Th 3/14?) Final report due March 17 at midnight Project See website for details

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Tentative Syllabus Weeks 1-2: Multiuser systems (Chapters 13.4 and 14, additional papers) Weeks 3-4: Cellular systems (Chapter 15, additional papers) Weeks 5-6: Ad hoc wireless networks (Chapter 16, additional papers) Week 7-8: Cognitive radio networks (papers) Week 8-9: Sensor networks (papers) Weeks 10: Additional Topics. Course Summary

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Future Wireless Networks 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 …

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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. R1R1 R2R2 R3R3 x h 1 (t) x h 21 (t) x h 3 (t) x h 22 (t) Challenge: How to share the channel among multiuser users Uplink and Downlink typically duplexed in time or frequency

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

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Interference: Friend or Foe? If treated as noise: Foe If decodable (MUD): Neither friend nor foe If exploited via cooperation and cognition: Friend (especially in a network setting) Increases BER Reduces capacity

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Cellular Systems Reuse channels to maximize capacity 1G: Analog systems, large frequency reuse, large cells, uniform standard 2G: Digital systems, less reuse (1 for CDMA), smaller cells, multiple standards, evolved to support voice and data (IS-54, IS-95, GSM) 3G: Digital systems, WCDMA competing with GSM evolution. 4G: OFDM/MIMO 5G: ??? BASE STATION MTSO

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

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ce Ad-Hoc/Mesh Networks Outdoor Mesh Indoor Mesh

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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 Optimal routing Resource allocation (power, rate, spectrum, etc.)

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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.

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Cognition Radios Cognitive radios can support new wireless users in existing crowded spectrum Without degrading performance of existing users Utilize advanced communication and signal processing techniques Coupled with novel spectrum allocation policies Technology could Revolutionize the way spectrum is allocated worldwide Provide sufficient bandwidth to support higher quality and higher data rate products and services

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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

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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 NCR IPIP CR

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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

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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 RX2 NCR CR

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Wireless Sensor and “Green” Networks Energy (transmit and processing) is 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 Similar ideas can be used to re-architect systems and networks to be green Smart homes/buildings Smart structures Search and rescue Homeland security Event detection Battlefield surveillance

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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.

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Green” Cellular Networks 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 Pico/Femto Relay DAS Coop MIMO How should cellular systems be redesigned for minimum energy? Research indicates that signicant savings is possible

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Crosslayer Design in Wireless Networks Application Network Access Link Hardware Tradeoffs at all layers of the protocol stack are optimized with respect to end-to-end performance This performance is dictated by the application

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Multiuser Channels

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Uplink and Downlink Downlink (Broadcast Channel or BC): One Transmitter to Many Receivers. Uplink (Multiple Access Channel or MAC): Many Transmitters to One Receiver. R1R1 R2R2 R3R3 x h 1 (t) x h 21 (t) x h 3 (t) x h 22 (t) Challenge: How to share the channel among multiuser users Uplink and Downlink typically duplexed in time or frequency

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RANDOM ACCESS TECHNIQUES 7C Cimini-9/97 Random Access Dedicated channels wasteful for data Use statistical multiplexing Techniques ALOHA and slotted ALOHA Carrier sensing l Collision detection or avoidance Reservation protocols (similar to deterministic access) Retransmissions used for corrupted data Poor throughput and delay characteristics under heavy loading

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ALOHA Data is packetized Retransmit When packets collide Pure ALOHA send packet whenever data is available a collision occurs for any partial overlap of packets Slotted ALOHA send packets during predefined timeslots avoids partial overlap of packets Comments Inefficient for heavily loaded systems Capture effect (packets with high SINR decoded) improves efficiency G (Attempts per Packet TIme) Throughput per Packet Time) Pure Aloha Slotted Aloha

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RTS Channel sensed before transmission To determine if it is occupied More efficient than ALOHA fewer retransmissions Carrier sensing is often combined with collision detection in wired networks (e.g. Ethernet) not possible in a radio environment Collision avoidance (busy tone, RTS/CTS) can be used Carrier Sense Techniques Wired Network Busy Tone Wireless Network CTS

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Deterministic Bandwidth Sharing Frequency Division Time Division Code Division Multiuser Detection Space Division (MIMO) Hybrid Schemes What is optimal?Look to Shannon.

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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)

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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

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

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Multiuser Shannon Capacity Fundamental Limit on Data Rates 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 Capacity: The set of simultaneously achievable rates {R 1,…,R n } with arbitrarily small probability of error R1R1 R2R2 R3R3 R1R1 R2R2 R3R3

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Broadcast Channel Capacity Region in AWGN Model One transmitter, two receivers with spectral noise density n 1, n 2 : n 1

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Rate Region: Time Division Time Division (Constant Power) Fraction of time allocated to each user is varied Time Division (Variable Power) Fraction of time and power i allocated to each user is varied

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Rate Region: Frequency Division Frequency Division Bandwidth B i and power S i allocated to each user is varied. Equivalent to TD for B i = i B and P = i i.

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Superposition Coding Best user decodes fine points Worse user decodes coarse points

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Code Division Superposition Coding Coding strategy allows better user to cancel out interference from worse user. DS spread spectrum with spreading gain G and cross correlation 12 = =G: By concavity of the log function, G=1 maximizes the rate region. DS without interference cancellation

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Broadcast and MAC Fading Channels Goal: Maximize the rate region {R 1,…,R n }, subject to some minimum rate constraints, by dynamic allocation of power, rate, and coding/decoding. Wireless Gateway Wired Network Broadcast: One Transmitter to Many Receivers. Multiple Access: Many Transmitters to One Receiver. R1R1 R2R2 R3R3 x g 1 (t) x g 2 (t) x g 3 (t) Assume transmit power constraint and perfect TX and RX CSI

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Fading Capacity Definitions Ergodic (Shannon) capacity: maximum long-term rates averaged over the fading process. l Shannon capacity applied directly to fading channels. l Delay depends on channel variations. l Transmission rate varies with channel quality. Zero-outage (delay-limited * ) capacity: maximum rate that can be maintained in all fading states. l Delay independent of channel variations. l Constant transmission rate – much power needed for deep fading. Outage capacity: maximum rate that can be maintained in all nonoutage fading states. l Constant transmission rate during nonoutage l Outage avoids power penalty in deep fades *Hanly/Tse, IT, 11/98

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Two-User Fading Broadcast Channel + + X[i] 1 [i] 2 [i] Y 1 [i] Y 2 [i] x x h 1 [i] h 2 [i] + + X[i] n [i]= 1 [i]/ h 1 [i] n 2 [i]= 2 [i]/ h 2 [i] Y 1 [i] Y 2 [i] At each time i: n={n 1 [i],n 2 [i]}

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Ergodic Capacity Region * Capacity region:,where The power constraint implies Superposition coding and successive decoding achieve capacity Best user in each state decoded last Power and rate adapted using multiuser water-filling: power allocated based on noise levels and user priorities *Li/Goldsmith, IT, 3/01

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Zero-Outage Capacity Region * The set of rate vectors that can be maintained for all channel states under power constraint P Capacity region defined implicitly relative to power: For a given rate vector R and fading state n we find the minimum power P min (R,n) that supports R. R C zero (P) if E n [P min (R,n)] P *Li and Goldsmith, IT, 3/01

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Outage Capacity Region Two different assumptions about outage: All users turned off simultaneously (common outage Pr) Users turned off independently (outage probability vector Pr) Outage capacity region implicitly defined from the minimum outage probability associated with a given rate Common outage: given (R,n), use threshold policy If P min (R,n)>s * declare an outage, otherwise assign this power to state n. Power constraint dictates s * : Outage probability:

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Independent Outage With independent outage cannot use the threshold approach: Any subset of users can be active in each fading state. Power allocation must determine how much power to allocate to each state and which users are on in that state. Optimal power allocation maximizes the reward for transmitting to a given subset of users for each fading state Reward based on user priorities and outage probabilities. An iterative technique is used to maximize this reward. Solution is a generalized threshold-decision rule.

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Minimum-Rate Capacity Region Combines ergodic and zero-outage capacity: Minimum rate vector maintained in all fading states. Average rate in excess of the minimum is maximized. Delay-constrained data transmitted at the minimum rate at all times. Channel variation exploited by transmitting other data at the maximum excess average rate.

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Minimum Rate Constraints Define minimum rates R * = (R * 1,…,R * M ): These rates must be maintained in all fading states. For a given channel state n: R * must be in zero-outage capacity region Allocate excess power to maximize excess ergodic rate The smaller R *, the bigger the min-rate capacity region

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Comparison of Capacity Regions For R * far from C zero boundary, C min-rate C ergodic For R * close to C zero boundary, C min-rate C zero R *

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Min-Rate Capacity Region: Large Deviation in User Channels Symmetric channel with 40 dB difference in noises in each fading state (user 1 is 40 dB stronger in 1 state, and vice versa). P = 10 mW, B = 100 KHz

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Min-Rate Capacity Region: Smaller Deviation Symmetric channel with 20 dB difference in noises in each fading state (user 1 is 20 dB stronger in 1 state, and vice versa). P = 10 mW, B = 100 KHz

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Broadcast Channels with ISI 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.

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Broadcast Channel Model Both H 1 and H 2 are finite IR filters of length m. The w 1k and w 2k are correlated noise samples. For 1

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Circular Channel Model Define the zero padded filters as: The n-Block Circular Gaussian Broadcast Channel (n-CGBC) is defined based on circular convolution: 0

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Equivalent Channel Model Taking DFTs of both sides yields Dividing by H and using additional properties of the DFT yields 0

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Parallel Channel Model + + X1X1 V 11 V 21 Y 11 Y XnXn V 1n V 2n Y 1n Y 2n N i (f)/H i (f) f

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Channel Decomposition The n-CGBC thus decomposes to a set of n parallel discrete memoryless degraded broadcast channels with AWGN. Can show that as n goes to infinity, the circular and original channel have the same capacity region The capacity region of parallel degraded broadcast channels was obtained by El-Gamal (1980) Optimal power allocation obtained by Hughes-Hartogs(’75). The power constraint on the original channel is converted by Parseval’s theorem to on the equivalent channel.

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Capacity Region of Parallel Set Achievable Rates (no common information) Capacity Region For 0< find { j }, {P j } to maximize R 1 + R 2 + P j. Let (R 1 *,R 2 * ) n, denote the corresponding rate pair. C n ={(R 1 *,R 2 * ) n, : 0< }, C =liminf n C n. R1R1 R2R2

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Optimal Power Allocation: Two Level Water Filling

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Capacity vs. Frequency

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Capacity Region

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Multiple Access Channel Multiple transmitters Transmitter i sends signal X i with power P i Common receiver with AWGN of power N 0 B Received signal: X1X1 X2X2 X3X3

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MAC Capacity Region Closed convex hull of all (R 1,…,R M ) s.t. For all subsets of users, rate sum equals that of 1 superuser with sum of powers from all users Power Allocation and Decoding Order Each user has its own power (no power alloc.) Decoding order depends on desired rate point

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Two-User Region Superposition coding w/ interference canc. SC w/ IC and time sharing or rate splitting Frequency division Time division C1C1 C2C2 Ĉ1Ĉ1 Ĉ2Ĉ2 SC w/out IC

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Fading and ISI MAC capacity under fading and ISI determined using similar techniques as for the BC In fading, can define ergodic, outage, and minimum rate capacity similar as in BC case Ergodic capacity obtained based on AWGN MAC given fixed fading, averaged over fading statistics Outage can be declared as common, or per user MAC capacity with ISI obtained by converting to equivalent parallel MAC channels over frequency

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Differences: Shared vs. individual power constraints Near-far effect in MAC Similarities: Optimal BC “superposition” coding is also optimal for MAC (sum of Gaussian codewords) Both decoders exploit successive decoding and interference cancellation Comparison of MAC and BC P P1P1 P2P2

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MAC-BC Capacity Regions MAC capacity region known for many cases Convex optimization problem BC capacity region typically only known for (parallel) degraded channels Formulas often not convex Can we find a connection between the BC and MAC capacity regions? Duality

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Dual Broadcast and MAC Channels x x + x x + + Gaussian BC and MAC with same channel gains and same noise power at each receiver Broadcast Channel (BC) Multiple-Access Channel (MAC)

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The BC from the MAC Blue = BC Red = MAC P 1 =1, P 2 =1 P 1 =1.5, P 2 =0.5 P 1 =0.5, P 2 =1.5 MAC with sum-power constraint

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Sum-Power MAC MAC with sum power constraint Power pooled between MAC transmitters No transmitter coordination MAC BC Same capacity region!

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BC to MAC: Channel Scaling Scale channel gain by , power by 1/ MAC capacity region unaffected by scaling Scaled MAC capacity region is a subset of the scaled BC capacity region for any MAC region inside scaled BC region for any scaling MAC BC

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The BC from the MAC Blue = Scaled BC Red = MAC

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BC in terms of MAC MAC in terms of BC Duality: Constant AWGN Channels What is the relationship between the optimal transmission strategies?

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Equate rates, solve for powers Opposite decoding order Stronger user (User 1) decoded last in BC Weaker user (User 2) decoded last in MAC Transmission Strategy Transformations

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Duality Applies to Different Fading Channel Capacities Ergodic (Shannon) capacity: maximum rate averaged over all fading states. Zero-outage capacity: maximum rate that can be maintained in all fading states. Outage capacity: maximum rate that can be maintained in all nonoutage fading states. Minimum rate capacity: Minimum rate maintained in all states, maximize average rate in excess of minimum Explicit transformations between transmission strategies

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Duality: Minimum Rate Capacity BC region known MAC region can only be obtained by duality Blue = Scaled BC Red = MAC MAC in terms of BC What other capacity regions can be obtained by duality? Broadcast MIMO Channels

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