EE360: Multiuser Wireless Systems and Networks Lecture 5 Outline

1. EE360: Multiuser Wireless Systems and NetworksLecture 5 Outline • Announcements • Project proposals due 1/27 • Makeup lecture for 2/10 (previous Friday 2/7, time TBD) • Reading, Supplemental reading, and class pace • Small cells, HetNets, and SoN • Shannon Capacity of Cellular Systems • Area Spectral Efficiency • Multiuser Detection in cellular • MIMO in Cellular

2. Review of Last Lecture • Multiuser Detection • Tradeoff in performance versus complexity • Multiuser OFDM Techniques • OFDMA most common • Cellular System Overview • Reuse frequencies to increase spectral efficiency • Sophisticated PHY layer • Centralized control • Standards • 4G is LTE: uses OFDMA and MIMO to achieve • 50-100 Mbps • 10-20 MHz of spectral available: hard to compete with WiFi

3. Rethinking “Cells” in Cellular How should cellular systems be designed? • Traditional cellular design “interference-limited” • MIMO/multiuser detection removes interference • Cooperating BSs form MIMO array: what is a cell? • Distributed antennas move BS towards boundary • Relays change cell shape and boundaries • Mobile relaying, virtual MIMO, analog network coding. • Small cells create a cell within a cell Coop MIMO Small Cell Relay Will gains in practice be big or incremental; in capacity or coverage? DAS Next Lecture

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

5. The Future Cellular Network: Hierarchical Architecture • Today’s architecture • 3M Macrocells serving 5 billion users MACRO: solving initial coverage issue, existing network 10x Lower HW COST PICO:solving street, enterprise & home coverage/capacity issue 10x CAPACITY Improvement Near 100%COVERAGE FEMTO: solving enterprise & home coverage/capacity issue Picocell Femtocell Macrocell Managing interference between cells is hard

6. Deployment Challenges • 5M Pico base stations in 2015 (ABI) • 37.5M Man Days = 103k Man Years • Exorbitant costs • Where to find so many engineers? Small cell deployments require automated self-configuration via software Basic premise of self-organizing networks (SoN)

7. SON for LTE small cells Node Installation SelfHealing Mobile Gateway Or Cloud SoNServer Initial Measurements SON Server Self Configuration Measurement Self Optimization IP Network X2 X2 X2 X2 Small cell BS Macrocell BS

8. Algorithmic Challenge: Complexity Innovation needed to tame the complexity Optimal channel allocation was NP hard in 2nd-generation (voice) IS-54 systems Now we have MIMO, multiple frequency bands, hierarchical networks, … But convex optimization has advanced a lot in the last 20 years Stage 3 Use genetic search to find further improvements by mutating some “genes”

9. Cellular System Capacity • Shannon Capacity • Shannon capacity does no incorporate reuse distance. • Wyner capacity: capacity of a 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. • Area Spectral Efficiency • Capacity per unit area In practice, all techniques have roughly the same capacity for voice, but flexibility of OFDM/MIMO supports more heterogeneous users

10. Defining Cellular Capacity • Shannon-theoretic definition • Multiuser channels typically assume user coordination and joint encoding/decoding strategies • Can an optimal coding strategy be found, or should one be assumed (i.e. TD,FD, or CD)? • What base station(s) should users talk to? • What assumptions should be made about base station coordination? • Should frequency reuse be fixed or optimized? • Is capacity defined by uplink or downlink? • Capacity becomes very dependent on propagation model • Practical capacity definitions (rates or users) • Typically assume a fixed set of system parameters • Assumptions differ for different systems: comparison hard • Does not provide a performance upper bound

11. Approaches to Date • Shannon Capacity • TDMA systems with joint base station processing • Multicell Capacity • Rate region per unit area per cell • Achievable rates determined via Shannon-theoretic analysis or for practical schemes/constraints • Area spectral efficiency is sum of rates per cell • 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.)

12. Wyner Uplink Capacity • Linear or hexagonal cells • Received signal at base station (N total users) • Propagation for out-of-cell interference captured by a • Average power constraint: E • Capacity CN defined as largest achievable rate (N users)

13. Linear Array Theorem: for Optimal scheme uses TDMA within a cell - Users transmit in 1/K timeslots; power KP Treats co-channel signals as interference:

14. Results Alternate TDMA CDMA w/ MMSE

15. Channel Reuse in Cellular Systems • Channel Reuse in Cellular Systems • Motivation: power falloff with transmission distance • Pro: increase system spectral efficiency • Con: co-channel interference (CCI) • “Channel”: time slot, frequency band, (semi)-orthogonal code ... • Cellular Systems with different multiple-access techniques • CDMA (IS-95, CDMA2000): weak CCI, channel reuse in every cell • codes designed with a single and narrow autocorrelation peak • TDMA (GSM), FDMA (AMPS): much stronger CCI • a minimum reuse distance required to support target SINR • Channel reuse: traditionally a fixed system design parameter

16. Adaptive Channel Reuse • Tradeoff • Large reuse distance reduces CCI • Small reuse distance increases bandwidth allocation • Related work • [Frodigh 92] Propagation model with path-loss only • channel assignment based on sub-cell compatibility • [Horikawa 05] Adaptive guard interval control • special case of adaptive channel reuse in TDMA systems • Current work • Propagation models incorporating time variation of wireless channels • static (AWGN) channel, fast fading and slow fading • Channel reuse in cooperative cellular systems (network MIMO) • compare with single base station processing

17. System Model • Linear cellular array, one-dimensional, downlink, single cell processing • best models the system along a highway [Wyner 1994] • Full cooperation leads to fundamental performance limit • More practical scheme: adjacent base station cooperation

18. Channel Assignment • Intra-cell FDMA, K users per cell, total bandwidth in the system K·Bm • Bandwidth allocated to each user • maxium bandwidth Bm, corresponding to channel reuse in each cell • may opt for a fraction of bandwidth, based on channel strength • increased reuse distance, reduced CCI & possibly higher rate

19. Single Base Station Transmission: AWGN • Path loss only, receive power • A: path loss at unit distance • γ : path-loss exponent • Receive SINR • L: cell radius. N0: noise power • Optimal reuse factor • Observations • Mobile close to base station -> strong channel, small reuse distance • Reuse factor changes (1 -> ½) at transition distance dT = 0.62 mile

20. Rayleigh Fast Fading Channel • Environment with rich scatters • Applies if channel coherence time shorter than delay constraint • Receive power • g: exponentially distributed r.v. • Optimal reuse factor • Lower bound: random signal • Upper bound: random interference • Observations • AWGN and fast fading yield similar performance • reuse factor changes (1 -> ½)at transition distance dT = 0.65 mile • Both “sandwiched” by same upper/lower bounds (small gap in between)

21. Rayleigh Slow Fading Channel • Stringent delay constraint, entire codeword falls in one fading state • Optimal reuse factor • Compare with AWGN/slow fading: • optimal reuse factor only depends on distance between mobile and base station • Observations • Optimal reuse factor random at each distance, also depends on fading • Larger reuse distance (1/τ > 2) needed when mobiles close to cell edge

22. Base Station Cooperation: AWGN • Adjacent base station cooperation, effectively 2×1 MISO system • Channel gain vectors: signal interference • Transmitter beamforming • optimal for isolated MISO system with per-base power constraint • suboptimal when interference present • an initial choice to gain insight into system design

23. Performance Comparison • no reuse channel in adjacent cell: to avoid base station serving user and interferer at the same time • reuse factor ½ optimal at all d: suppressing CCI without overly shrinking the bandwidth allocation • bandwidth reduction (1-> ½) over-shadows benefit from cooperation Observations • Advantage of cooperation over single cell transmission: only prominent when users share the channel; limited with intra-cell TD/FD [Liang 06] • Remedy: allow more base stations to cooperate • in the extreme case of full cooperation, channel reuse in every cell

24. Area Spectral Efficiency • 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. BASE STATION A=.25D2p =

25. ASE with Adaptive Modulation • Users adapt their rates (and powers) relative to S/I variation. • S/I distribution for each user based on propagation and interference models. • Computed for extreme interference conditions. • Simulated for average interference conditions. • The maximum rate Ri for each user in a cell is computed from its S/I distribution. • For narrowband system use adaptive MQAM analysis

26. Propagation Model • Two-slope path loss model: • Slow fading model: log-normal shadowing • Fast fading model: Nakagami-m • Models Rayleigh and approximates Ricean. • ASE maximized with reuse distance of one! • Adaptive modulation compensate for interference

27. 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]

28. MUD, Smart Antennasand MIMO in Cellular

29. MUD in Cellular In the uplink scenario, the BS RX must decode all K desired users, while suppressing other-cell interference from many independent users. Because it is challenging to dynamically synchronize all K desired users, they generally transmit asynchronously with respect to each other, making orthogonal spreading codes unviable. In the downlink scenario, each RX only needs to decode its own signal, while suppressing other-cell interference from just a few dominant neighboring cells. Because all K users’ signals originate at the base station, the link is synchronous and the K – 1 intracell interferers can be orthogonalized at the base station transmitter. Typically, though, some orthogonality is lost in the channel.

30. MIMO in Cellular:Performance Benefits • Antenna gain  extended battery life, extended range, and higher throughput • Diversity gain  improved reliability, more robust operation of services • Interference suppression (TXBF)  improved quality, reliability, and robustness • Multiplexing gain  higher data rates • Reduced interference to other systems Optimal use of MIMO in cellular systems, especially given practical constraints, remains an open problem

31. 5 7 2 5 6 5 1 5 3 4 5 5 Sectorization and Smart Antennas • 1200 sectoring reduces interference by one third • Requires base station handoff between sectors • Capacity increase commensurate with shrinking cell size • Smart antennas typically combine sectorization with an intelligent choice of sectors 8C32810.46-Cimini-7/98

32. Beam Steering SIGNAL • Beamforming weights used to place nulls in up to NR directions • Can also enhance gain in direction of desired signal • Requires AOA information for signal and interferers INTERFERENCE SIGNAL OUTPUT INTERFERENCE BEAMFORMING WEIGHTS

33. Multiplexing/diversity/interference cancellation tradeoffs Interference • Spatial multiplexing provides for multiple data streams • TX beamforming and RX diversity provide robustness to fading • TX beamforming and RX nulling cancel interference • Can also use DSP techniques to remove interference post-detection Stream 2 Stream 1 Optimal use of antennas in wireless networks unknown

34. Diversity vs. Interference Cancellation wr1(t) wt1(t) r1(t) x1(t) wr2(t) wt2(t) r2(t) x2(t) sD(t) + y(t) wrR(t) wtT(t) xM(t) rR(t) Nt transmit antennas NR receive antennas Romero and Goldsmith: Performance comparison of MRC and IC Under transmit diversity, IEEE Trans. Wireless Comm., May 2009

35. Diversity/IC Tradeoffs • NR antennas at the RX provide NR-fold diversity gain in fading • Get NTNR diversity gain in MIMO system • Can also be used to null out NR interferers via beam-steering • Beam steering at TX reduces interference at RX • Antennas can be divided between diversity combining and interference cancellation • Can determine optimal antenna array processing to minimize outage probability

36. Diversity Combining Techniques • MRC diversity achieves maximum SNR in fading channels. • MRC is suboptimal for maximizing SINR in channels with fading and interference • Optimal Combining (OC) maximizes SINR in both fading and interference • Requires knowledge of all desired and interferer channel gains at each antenna

37. SIR Distribution and Pout • Distribution of g obtained using similar analysis as MRC based on MGF techniques. • Leads to closed-form expression for Pout. • Similar in form to that for MRC • Fo L>N, OC with equal average interference powers achieves the same performance as MRC with N −1 fewer interferers.

38. Performance Analysis for IC • Assume that N antennas perfectly cancel N-1 strongest interferers • General fading assumed for desired signal • Rayleigh fading assumed for interferers • Performance impacted by remaining interferers and noise • Distribution of the residual interference dictated by order statistics

39. SINR and Outage Probability • The MGF for the interference can be computed in closed form • pdf is obtained from MGF by differentiation • Can express outage probability in terms of desired signal and interference as • Unconditional Pout obtained as Obtain closed-form expressions for most fading distributions

40. OC vs. MRC for Rician fading

41. IC vs MRC as function of No. Ints

42. Diversity/IC Tradeoffs

43. Distributed Antennasin Cellular

44. Distributed Antennas (DAS) in Cellular DAS • Basic Premise: • Distribute BS antennas throughout cell • Rather than just at the center • Antennas connect to BS through wireless/wireline links • Performance benefits • Capacity • Coverage • Power consumption

45. Average Ergodic Rate • Assume full CSIT at BS of gains for all antenna ports • Downlink is a MIMO broadcast channel with full CSIR • Expected rate is • Average over user location and shadowing • DAS optimization • Where to place antennas • Goal: maximize ergodic rate

46. Solve via Stochastic Gradients • Stochastic gradient method to find optimal placement • Initialize the location of the ports randomly inside the coverage region and set t=0. • Generate one realization of the shadowing vector f(t) based on the probabilistic model that we have for shadowing • Generate a random location u(t), based on the geographical distribution of the users inside the cell • Update the location vector as • Let t = t +1 and repeat from step 2 until convergence.

47. Gradient Trajectory N = 3 (three nodes) Circular cell size of radius R = 1000m Independent log-Normal shadow fading Path-loss exponent: a=4 Objective to maximize : average ergodic rate with CSIT

48. Power efficiency gains • Power gain for optimal placement versus central placement • Three antennas

49. Non-circular layout N = 6, α = 5 N = 12, α = 5 For typical path-loss exponents 2<α<6, and for N>5, optimal antenna deployment layout is not circular

50. Interference Effect • Impact of intercell interference • is the interference coefficient from cell j • Autocorrelation of neighboring cell codes for CDMA systems • Set to 1 for LTE(OFDM) systems with frequency reuse of one.