The Wireless Data Crunch: Motivating Research in Wireless Communications

Transcription

1 The Wireless Data Crunch: Motivating Research in Wireless Communications Stephen Hanly CSIRO-Macquarie University Chair in Wireless Communications

2 Wireless Growth Rate Cooper s law: Number of conversations (voice or data) over a given area in all of the useful radio spectrum has doubled every two-and-ahalf years for the past 104 years.

3 Wireless Growth Rate 1,000,000 x improvement in 45 years: 25 x due to more spectrum 5 x dividing spectrum into narrower slices (FDM) 5 x advanced modulation and coding techniques 1600 x frequency re-use

4 Wireless Data Crunch In Feb. 2012, CISCO predicts: Mobile data demand will increase by 18 x between By 2016: 2/3 mobile data traffic will be video Mobile connection speeds will need to increase by 9 x 1.4 mobile devices per capita But how can networks adapt to this rate of change?! This is the data crunch that is driving forward research in wireless communications

5 Claude Shannon and Capacity In 1948, Claude Shannon wrote down Newtons laws of the information age in his classic paper: A Mathematical Theory of Communication This paper allows us to compute the maximum possible bit rate, in bits/sec, of any point-to-point communication channel For the Gaussian noise channel: C W log( 1 SNR) bits/sec

6 Claude Shannon and Capacity C W log( 1 SNR) bits/sec Formula above is for the additive white Gaussian noise (AWGN) channel Sometimes you hear people claim to have broken the Shannon limit The reality is they have only broken that particular formula Just means channel model is NOT the AWGN channel Shannon gave the method to get the capacity of any point to point channel

7 Spectral Efficiency Since bandwidths can vary, its more useful to talk of spectral efficiency, in bits/sec/hz Let the received power be P mw and the Gaussian noise power spectral density be s 2 mw/hz; so the Then the spectral efficiency limit is C 1 2 P log 1 2 s SNR P 2 s bits/sec/hz Later in the talk we ll introduce an important extension: area spectral efficiency, in bits/sec/hz/m 2

8 Claude Shannon and Capacity Shannon didn t just tells us a capacity result He also indicated how to get there: eg. use random Gaussian codebooks in the AWGN channel. These ideas pointed the way to Turbo codes and low density parity check codes He also showed how to analyze error probabilities Random Code And design communication systems: eg. the source channel separation theorem tells us to design a layered system

9 x 1 y

10 Beyond Shannon: Multiple Users Shannon focused on the point-to-point channel A cell in a mobile radio network has multiple users The uplink (mobile to base station link) is called a multiple access channel (MAC) Researchers that followed Shannon (Ahlswede and Liao 72) found the capacity region of a MAC The downlink is called a broadcast channel

11 The Multiple Access Channel (MAC) Lets look at the simplest two user MAC: AWGN at the base station There is interference between the two links which is reflected in a tradeoff: If R 1 is the bit rate of user 1 and R 2 is the bit rate of user 2 then there is a tradeoff between these two rates There is a capacity region that describes the set of achievable rates R 2 R 1

12 2-user Rate Region of AWGN MAC The dominant line gives the best pairs of rates the optimal tradeoff FDMA curve touches the dominant line at one point Treating interference as noise is suboptimal Successive decoding is optimal R 2 = FDMA curve x = interference as noise = successive decoding x R 1

13 What About the Real World? In the real world we have cells Mobiles in one cell will interfere at another cell s base station

14 2-user AWGN Interference Channel The rate region for this channel is unknown An open problem for over 40 years! Recent work has characterized the rate region to within 1 bit/sec/hz (Etkin, Tse, and Wang 08). Thus: Rate region is known quite precisely when the noise is low

15 MACs in the Interference Channel Message 1 Tx1 Rx1 Message 1 Message 2 Tx2 Message 2 Rx2 User 1 and user 2 are heard at Rx1: MAC1 User 1 and user 2 are heard at Rx2: MAC2 The intersection of both rate regions is achievable

16 MACs in the Interference Channel R 2 MAC 2 MAC 1 = treat interference as noise R 1

17 MACs in the Interference Channel R 2 MAC 2 MAC 1 = treat interference as noise R 1

18 Han Kobayashi Scheme Each user splits its data into two parts: private and common The private message is only decoded by the desired receiver (Rx1) The common message is decoded by both and cancelled The private data rate is too high to be decoded at Rx2 and is treated as Gaussian noise Tx1 Private message Common message Rx1 Tx2 Rx2 Treat private message as noise at Rx2

19 Han Kobayashi Scheme Etkin, Tse and Wang 08 show this scheme can be optimized to be within 1 bit/sec/hz of capacity Still uses Gaussian random codes Will not work once we go to three or more users! Beyond Shannon: random codes are no longer any good New research shows we must look for structured codes A new concept called interference alignment

20 Gaussian Han-Kobayashi Not Optimal Lattice codes can achieve constant gap Suppose users 1 and 2 use a random Gaussian codebook: Random Code Sum of Two Random Codebooks Lattice Code for Users 1 and 2 Interference from users 1 and 2 fills the space: no room for user 0. User 0 Code

21 Gaussian Models It remains that the Gaussian model is a robust model It can be shown that the following formula gives achievable rates for interference channels: C 1 2 log 1 signal energy interference energy noise energy bits/sec/hz Even this simple model is highly complex! Energy allocation can be optimized across bandwidth and time These optimization problems have been shown to be mathematically intractable for large numbers of links

22 Symmetric Network Problem N links: All links gains have the same value All cross-link gains have the same (other) value 1 e N=2 e 1

23 Solution to Symmetric Network Problem We might expect optimal solution to be: FDMA between the links when the cross-gain (e) is high Wideband (WB) frequency sharing when e is low FDMA WB W 2 one band for each link W 2 f W 2 One band shared by all links W 2 f

24 Solution to Symmetric Network Problem When the cross-gain (e) is low: FDMA between the links when the SNR is high Wideband (WB) frequency sharing when the SNR is low A mixture of these when the SNR is medium FDMA WB W 2 one band for each link W 2 f W 2 One band shared by all links W 2 f

25 TV White Spaces and Unused Spectrum Is the spectrum really that congested? What about white spaces? eg TV bands that are not being used? Licensed (primary) users may not be active in some areas Room for secondary, unlicensed users? New spectrum opening up, and spectrum auctions

26 Fragmented Spectrum

27 Cognitive Radio Smart, agile radios that can sense and occupy un-utilized spectrum Require flexible hardware, tuneable frequencies Overlay: search out unused bands the FDM approach Underlay: UWB radios taking the WB approach Successive decoding can be used to strip off strong interference Interference alignment strategies can reduce the spectral footprint

28 Multiple Input, Multiple Output (MIMO) Multiple antennas can be used at the base station Multiple users provide multiple antennas Similar in a lot of ways to a point to point MIMO channel Beamforming: N antennas can create up to N noninterfering beams Main challenge is channel measurement

29 Coordinated Beamforming If base stations share channel state information (CSI) over the backhaul network coordinated beamforming CSI Cell 1 Cell 2

30 Distributed Antennas Throw away cells altogether (new architecture!) Backhaul transports both CSI and user data Now a giant broadcast channel with distributed antennas! Base stations must cooperate CSI data

31 Spectral efficiency is really measured in bits/sec/hz/m 2 Small Cells If we can decrease the cell sizes then we increase spectral efficiency by increasing frequency re-use Macrocells, microcells, picocells Tiny picocells can be used in network hotspots

32 Femtocells Femtocells are tiny cells formed with cheap, off-the-shelf base stations Femto base station are like WiFi access point but use cellular frequencies plug and play They offload cellular traffic onto owner s broadband ISP connection Networks are heterogeneous a mix of short and long links, mixture of planned and unplanned layouts

33 Multi-tier Heterogeneous Networks Many research challenges: Closed versus open access Modelling and design (mixture of planned and unplanned) Interference avoidance (long versus short links) Power control Cell association Base station coordination Handovers (cell to cell and tier to tier)

34 Conclusions Exciting time to be in wireless research Great challenge is to drastically increase bits/sec/hz/m 2 to match forecast demand Interference and congestion are the major challenges! Huge gap between theory and practice in MIMO, interference alignment, coordinating base stations New architectures and new algorithms Small cells, heterogeneous networks and cognitive radios offer a prospect to meet the data crunch challenge! Thankyou!