Fundamentals of Wireless Communications and Its Recent

Developments鍾偉和助研究員

中央研究院 資訊科技創新研究中心http://www.citi.sinica.edu.tw/pages/whc/vita_zh.html

whc@citi.sinica.edu.tw

Research Center for Information Technology Innovation, Academia Sinica-Overview

• Formally Founded in 2007, Started Operation in Sept. 2008

• The 31st youngest research unit in Academia Sinica• Faculty Member and Research Staff

– Tenure-track appointments(Research fellow): 14– Research assistants (postdocs): 220+– Jointly appointed faculty:50+

• Joint advisory with universities• Collaborations with industries

2

Outline

I. Introduction to communication system

II. MIMO System

III. Wireless Local Area Network(WLAN)

IV. WiMax

3

Wireless Networks are Overloaded• AT&T reported that mobile data traffic increases

5,000% in the past three years

Demands keep increasing

congested

congested

I. Introduction to communication system– Mathematical models for channels– Bandpass signals– Random process– Sampling theorem– Digital modulation– Types of Codes in communication system

5

Functional diagram of a communication system

6

Communication channels and their characteristics

7

• Physical channel media– magnetic-electrical signaled wire channel– modulated light beam optical (fiber) channel– antenna radiated wireless channel

• Noise characteristic– thermal noise (additive noise)– signal attenuation– amplitude and phase distortion– multi-path distortion

• Limitation of channel usage– transmitter power– receiver sensitivity– channel capacity (such as bandwidth)

8

Mathematical models forcommunication channels

1. Additive noise channel:

where α is the attenuation factor, s(t) is the transmitted signal, and n(t) is the additive

random noise process. (Note: Additive Gaussian noise channel: If n(t) is a Gaussian noise process.)

9

2. The linear filter channel with additive noise:

10

3. The linear time-variant filter channel with additive noise:

11

c(τ ;t) : τ is the argument for filtering; t is the argument for time-dependence.(The time-invariant filter can be viewed as a specialcase of the time-variant filter. Cf. the next slide.)

12

Example:

• The linear time-variant filter channel with additive noise:

c(τ ;t) usually has the form

where {ak(t)} represents the possibly time-variant attenuation factor for the L multipath propagation paths, and {τk} are the corresponding time delays. Hence,

13

Example : The linear time-variant filter channel with additive noise:

14

• Representation of band-pass:– Carrier modulation : carrier =

• : Amplitude modulation• : Frequency modulation• : Phase modulation

where m(t ) is the baseband signal.

15

Band-pass signals and systems

– The transmitted signal (after carrier modulation)

• is usually a real-valued bandpass signal.• Mathematical model of a real-valued narrowband

bandpass signal:

16

• For analytical convenience, the real-valued transmitted bandpass signal is usually analyzed in terms of its complex-valued equivalent lowpass signal.

• We need to Develop a mathematical representation (in time domain) of S+(f) and S−(f).

17

18

.

19

20

Previously, we discussed the representations of deterministic signals.

We now turn to discuss stochastically modeledsignals, i.e., stochastic processes.

Random process

• Engineers :

A random process is a collection of random

variables that arise in the same probability experiment.• Mathematicians :

A random process is a collection of random variables that are defined on a common probability space.

It is usually denoted by

21

Complex-valued random processes

• Auto-correlation function

22

• Cross-correlation function for two complex-valued random processes:

23

Sampling theorem

• Band-limited– A deterministic signal (or waveform) s(t) is said to be

(absolutely) band-limited if

• Sampling Theorem– A band-limited signal can be reconstructed by its samples

if the sampling rate is greater than 2W (Nyquist rate). The reconstruction formula is

24

Deterministic signal

The sinc function sinc(t) is evluated by sin(t)/t.

• Band-limited random process– Definition : A (WSS) random process Xt is said to be band-

limited if

– Hence,

• Sampling representation of a random process– For a band-limited stationary stochastic process Xt

25

Random process

Digital modulation

26

27

Example: M = 8

Memoryless modulation methods

• Digital pulse amplitude modulated (PAM) signals (Amplitude-Shift Keying or ASK)

• Digital phase-modulated (PM) signals (Phase Shift Keying or PSK)

• Quadrature amplitude modulated (QAM) signals• Multidimensional modulated signals

– General– Orthogonal

• Mutidimensional

• Biorthogonal

• Simplex signals

28

• (M-level) pulse amplitude modulated (M-PAM) signals– Channel symbol :

– Example: M = 4

• The distance between two adjacent signal amplitude = 2d.

• Bit interval = Tb = 1/R, symbol interval = T and k =log2M. Then symbol rate = symbol / sec = 1 / T = R / k (Note T = k Tb = k / R).

29

30

Vectorization of M-PAM signals (Gram Schmidt)

• Transmitted energy of M-PAM signals

• Error consideration– The most possible error is the erroneous selection of an

adjacent amplitude to the transmitted signal amplitude.– Therefore, the mapping (from bit pattern to channel symbol) is

assigned to result in that the adjacent signal amplitudes differ by exactly one bit. (Gray encoding)

– In such a way, the most possible bit error pattern caused by the noise is a single bit error.

– Gray code (Signal space diagram : one dimension)

31

• Euclidean distance

32

For Channel symbol, m = 1~M:

Euclidean distance:

• Single Side Band (SSB) PAM– g(t) is real => G(f) is symmetric.– Consequently, the previous PAM is based on DSB transmission

which requires twice the bandwidth.– Recall where is the Hilbert

transform of g(t).

33

• Transmitted energy of SSB M-PAM signals

• Recall : transmitted energy of DSB M-PAM signals

34

Under the condition that DSB M-PAM and SSB M-PAM signals require the same transmitted energy, the latter consumes only half of the bandwidth of the former by the cost of an additional Hilbert transformer.

35

• Applications of PAM

36

• Phase-modulated (PM) signals– Channel symbol

– Example: M = 4

37

38

• Transmission energy of PM signals

– Advantages of PM signals : equal energy for every channel symbol.

• Error consideration– The most possible error is the erroneous selection of an

adjacent phase of the transmitted signal.– Therefore, we assign the mapping from bit pattern to channel

symbol as the adjacent signal phase differs by one bit. (Gray encoding).

– In such a way, the most possible bit error pattern caused by the noise is a single-bit error.

39

• (Two dimensional) signal space diagram for Gray code

• Euclidean distance

40

• SSB for PM ?– Note that the baseband signal is not a real

number. (Hence, non - symmetric in spectrum.) So, there is no SSB version for PM.

– This can be considered as a tradeoff, when being compared to PAM.

41

Types of Codes

• Channel codes– data transmission codes (error-correcting codes)– data translation codes (to meet channel constraints)

• Source codes – lossless data compression codes – lossy data compression codes

• Secrecy codes

42

Channel Codes

Concept:

The purpose of channel encoder-decoder pair is to correct

the error introduced by the channel (noise, fading,

interference). The approach is to add redundancy (that is

algebraic related to the information to be transmitted) in the

channel encoder and to use this redundancy (and algebraic

relation) at the decoder to reconstruct the channel

input sequence as accurately as possible.

43

• Shannon’s noisy channel coding theorem– “With sufficient but finite redundancy properly introduced at the channel

encoder, it is possible for the channel decoder to reconstruct the input sequence to any degree of accuracy desired, provided that the input rate to the channel encoder is less than a given value called channel capacity.”

• General Design Criteria– For (digital) communication system users, BER is usually the

most important performance measure.– Spectral efficiency (in bits/sec/Hz) is the ultimate concern from

system engineering’s viewpoint.

• Other general design criteria include:– Complexity (delay) – Cost– Weight– Heat dissipation– Fault tolerance

44

• Spectral Efficiency– Definition:

η = data rate/required bandwidth (bits/s/Hz)• The Shannon-Hartley capacity of a band-limited AWGN

channel of bandwidth W is given by

C = W log2(1+S/N)

where S/N=(EbR)/(N0W).

• Hence the maximum spectral (bandwidth) efficiency ηmax is equal to

ηmax = log2(1+S/N) =C/W≤R/W (bits/s/Hz)

45

Examples of simple channel codes

• Single-parity-check codes– add a single parity-check bit to a k-bit

message word– code rate k/n = k/(k+1) = (n-1)/n

• Repetition codes– repeat the information bit n times– code rate = 1/n

46

• Example: (3,2) parity check code

• Definition: Hamming weight of a binary codeword is

defined as the number of 1 in the codeword.• Definition: Hamming distance between two binary

codeword is the number of places where they differ.• For the above example, w(000)=0, w(011)=2, w(101)=2,

w(110)=2, d(011,101) = 2.

47

},11,10,01,00{m }110 101; 011; 000;{C

C0,111001,010,10

• Example: (3,1) Repetition code

– Repetition decoding :To determine the value of a particular bit, we look at the received copies of the bit in the bitstream and choose the value that occurs more frequently.

– Received bitstream c’ = 110, estimated m = 1.

48

},1,0{m }111 000,{C

• A good code is one whose minimum distance is large. For the previous example, the (3,2) parity check code has dmin = 2.

49

Convolutional Codes

• Encoding of information stream rather than information blocks

• Easy implementation using shift register

• Decoding is mostly performed by the Viterbi Algorithm– Errors in Viterbi decoding algorithms for

convolutional codes tend to occur in bursts because they result from taking a wrong path in a trellis.

50

Convolutional Codes: (n=2, k=1, M=2)Encoder

51

State diagram

52

Trellis

53

Interleaver

• Interleaving is a technique commonly used in communication systems to overcome correlated channel noise such as burst error or fading.

• The interleaver rearranges input data such that consecutive data are spaced apart. At the receiver end, the interleaved data is arranged back into the original sequence by the de-interleaver.

54

55

Example: Block interleaver

56

Information Capacity Theorem

• The information capacity (or channel capacity) C of a continuous channel with bandwidth B Hertz can be perturbed by additive Gaussian white noise of power spectral density N0/2, provided bandwidth B satisfies

– P is the average transmitted power P =EbRb (for an ideal system, Rb = C).

– Eb is the transmitted energy per bit.

– Rb is transmission rate.

57

Turbo Codes

• The turbo code concept was first introduced by C. Berrou in 1993. Today, Turbo Codes are considered as the most efficient coding schemes for forward error correcting (FEC).

• Scheme with components (simple convolutional or block codes, interleaver, soft-decision decoder, etc.)

58

59

Turbo encoder

60

Turbo decoder

Shannon Limit

61

II. Typical MIMO System– System and signal model– ML Detector– Linear Detector

1. ZF detector

2. MMSE detector

62

System and signal model

63

• Signal model : y = Hx + v where x C∈ m, y C∈ n, H ∈Cn×m is a known channel matrix, and n ≥ m.

• The elements of x belong to a finite alphabet S of size |S|.• Example: BPSK: S = {1,−1}, and |S| = 2. 4-PSK: S = {1 + j, 1 − j,−1 + j,−1 − j}, and |S| = 4

Typical MIMO System• Maximum Likelihood (ML) Detection

• QR-decomposition of H: H = QR, where Q C∈ n×m: orthogonal (QHQ = I), and R C∈ m×m: upper triangular.

• Equivalent problem:

where y′ = Qhy. 64

ML Detection

• Tree Representation• Calculation of ||y′ − Rx|| can be broken down into layers

and successively examined:

Note that

65

Sxi

• The problem can be written as

66

67

Linear Detectors

• Suboptimal but very low complexity• Signal model: y = Hx + v

• After Linear Detector:

where • Each estimated symbol is determined by

68

x Wy W( Hx v ) .nmCW

kx̂

Zero-Forcing (ZF) Detector– ZF Criterion: WH = I

– Since m ≥ n, there is in general no such solution.

– Least square solution: WZF = (HHH)−1HH

• ZF detection:

• Apply the principle of the linear detector, we can find each estimated symbol

69

v)( x ~ H-1H HHHW yx ZFZF

kZFx ,ˆ

Minimum Mean Square Error (MMSE) Detector

• Assume E[x] = ,E[xxH] = σx2I; E[v] = 0 and E[vvH] = σv

2I

• MMSE criterion:• Optimal W :

– Compute– Let

– Apply two formulas

Fine the optimal W

70

yxxxEJ WW ~ Note ].||~[||min 2

]||~[|| 2xxE

0W

J

XBXBXBXX

BAAXBX

HHHH trtr }{ and }{

Hv

Hxx HIHHW 1222

MMSE )(

• The solution is Wiener solution.

• MMSE detection

• Common use since simple and analytic.

71

III. Wireless Local Area Network (WLAN)– 802.11 Physical Layer

1. 802.11b (1999)

2. 802.11a (1999)

3. 802.11g (2003)

72

Wireless Local Area Network(WLAN)

• IEEE 802.11 is a set of standards for implementing WLAN computer communication in the 2.4, 3.6 and 5 GHz frequency bands.

• They are created and maintained by the IEEE LAN/MAN Standards Committee (IEEE 802).

73

• (1999) 802.11b (HR/DSSS at 2.4GHz)– 5.5 Mbps and 11 Mbps

• (1999) 802.11a (OFDM at 5GHz)– 6 Mbps to 54 Mbps

• (2003) 802.11g (OFDM at 2.4 GHz)– Maximal rate: 54 Mbps– Maximal rate: get up to 108 Mbps (Atheros’ Super G)

74

802.11 Physical Layer

802.11b

• 802.11b - Data Transmission– Transmit 300 to 500 feet– Frequency-hopping spread-spectrum (FHSS)

• 1 or 2 Mbps– Direct-sequence spread-spectrum (DSSS)

• 1, 2, 5.5, or 11 Mbps• 802.11b - Frequencies and Bandwidth

– 2.4000 to 2.4835 GHz frequency– 22 MHz bandwidth per channel– 3 MHz guard bands

75

• Transmission– 1 and 2 Mbps speeds

• Use 11-bit Barker sequence• A Barker sequence is a string of digits of length

such that

for all

– Higher data rate, 5.5 and 11 Mbps speeds• Use complementary code keying (CCK)• The CCK is a variation and improvement on M-ary

Orthogonal Keying Modulation.

76

,1||1

ki

kl

iiaa

.1 lk

CCK

• CCK is a “single carrier” system, meaning that all data is transmitted by modulating a single radio frequency or carrier.

77

Signal bandwidth

frequency

Single carrier

78

表 IEEE 802.11b資料率特性

傳輸速率（ Data Rate ）

展頻碼長度（ Code

Length ）調變方式

（ Modulation ）

符元率（ Symb

ol Rate ）

符元內之位元數（ Bits/ Symbol

）

1Mbps11 （ Barker sequence ）

直接序列展頻 / 差分二相位移鍵調變

（ DSSS/DBPSF ）1Msps 1

2 Mbps11 （ Barker sequence ）

直接序列展頻 / 差分四相位移鍵調變

（ DSSS/DQPSF ）1Msps 2

5.5 Mbps 8 （ CCK ）互補碼調變 / 差分四

相位移鍵調變（ CCK/DQPSF ）

1.375Msps 4

11 Mbps 8 （ CCK ）互補碼調變 / 差分四

相位移鍵調變（ CCK/DQPSF ）

1.375Msps 8

 

79

802.11a

• 802.11a - Data Transmission– Transmit 100 to 150 feet– Orthogonal Frequency-Division Multiplexing (OFDM)

• 6 to 54 Mbps• OFDM is a “multi-carrier” modulation scheme.• Data is split up among several closely spaced

“subcarriers”, increasing reliability and speed• 802.11a - Frequencies and Bandwidth

– 12 channels– 20 MHz bandwidth per channel– Broken into 52 separate channels

• 48 transmit, 4 used for control80

Orthogonal Frequency Division Multiplexing– was first implemented in 802.11a.– OFDM is a “multi-carrier” modulation scheme.– Data is split up among several closely spaced

“subcarriers” or frequencies, increasing reliability and speed

81

• Transmission– 6 and 9 Mbps speeds

• Use 24-bit Barker sequence• Lowest rate: 6 Mbps

– BPSK - 48 bits per symbol, Rate 1/2 coding» 24 data bits per symbol» 24 x 250 ksps = 6 Mbps

– 12, 24 and 28 Mbps speeds• Use binary phase shift keying (BPSK)

82

83

Comparison

84

• Physical Layer

802.11b 802.11aDSSS• 3 - 22 MHz channels• Data Rates: up to 11 Mbps (5.5 is norm) • Frequency Range up to 300 Feet

OFDM• 12 – 20 MHz channels• Data rates: up to 54 Mbps (12-24 is norm) • Frequency Range up to 300 Feet

Range Comparison

85

802.11g (Atheros’ Super G)

1. Compression– Link-level hardware compression utilizes the

connection more efficiently and maximizes bandwidth.

– In hardware compression, the compression computations are offloaded to a secondary hardware module. This frees the central CPU from the computationally intensive task of compression calculations.

86

How does Atheros’ Super G make 802.11g faster?

Push Toward High Data-Rate Systems

87year2000 2003 2007 2012

Data rate

30-40 kbps

56-114 kbps

802.11 a/b/g802.11 a/b/g

802.11n 802.11n

GSMGSM

GPRSGPRS

WiMaxWiMax

LTELTE

Multiple Antenna Systems

Multiple Antenna Systems

56-128 Mbps

11-54 Mbps

300 Mbps

199X 2009

1 Gbps

(4x4 MIMO)

(8 x 8 MIMO)

(8 x 8 MIMO)

Far below theoretical bound

Far below theoretical bound

WhyCongested

?

WhyCongested

?

全球無線寬頻通訊標準之演進

88

IV. WiMAX• WiMAX Network Architecture• Recent Technologies in WiMAX

– 802.16j – 802.16m

• Overview of IEEE 802.16m• Advanced Features & Challenges of IEEE 802.16m

89

WiMax

• WiMAX Forum– WiMAX: Worldwide Interoperability for Microwave

Access– Formed in Apr 2001, by Intel, Proxim, Airspan, Fujitsu,

etc.– 500+ members including Intel, R&S, Alvarion, Wavesat,

PicoChip, Sony, Samsung, Nokia, TI, ADI, III, ITRI, etc.– Support IEEE 802.16 standards

• IEEE 802.16 Standards– 802.16 d/e define data and control plane functions in

wireless PHY/MAC– 802.16 f/g/i define management plane functions

90

91

WiMAX Network Architecture

ASN GWASN GW

WiMAX BS

WiMAX BS

WiMAX SSWiMAX MSWiMAX MS

R1

R6

R8

R3 R3

R6 R6 R6

R1 R1R1 R1

R4

R8

MS: Mobile StationSS: Stationary StationRx: Reference point xASN GW: Access Service Network Gateway

Intra-ASN Mobility: R6, R8Inter-ASN Mobility: R3, R4

Recent Technologies in WiMAX• 802.16j Multi-hop Relay

– Aiming at developing relay based on IEEE802.16e, to gain:• Coverage extension• Throughput enhancement

– Scope• To specify OFDMA PHY and MAC enhancement to IEEE Std.

802.16 for licensed bands to enable the operation of relay stations• Subscriber station specifications are not changed

• 802.16m– Advanced Air Interface for BWA in the future

• Moving towards 4G– Scope:

• To amend the IEEE 802.16 WirelessMAN-OFDMA to provide an advanced air interface for operation in licensed bands

• To meet the cellular layer requirements of IMT-Advanced next generation mobile networks

• To provide continuing support for legacy WirelessMAN-OFDMA equipment

92

Overview of IEEE 802.16m

• IEEE 802.16m provides the performance improvements necessary to support future advanced services and applications for 4G communications.

• Major worldwide governmental and industrial organizations, including ARIB, TTA, and the WiMAX Forum, are adopting this standard.

• IEEE 802.16m Support – low to high mobility applications– a wide range of data rates in multiple user environments– high-quality multimedia applications – significant improvements in performance and quality of service

93

94

MIMO architecture for the downlink of 802.16m systems.

• Aggregate Data Rate: – 100 Mbps for mobile stations, – 1 Gbps for fixed

• Operating Radio Frequency: < 6 GHz• MIMO support: 4 or 8 streams, no limit on antennas• Coverage: 3 km, 5-30 km and 30-100 km

95

Features of 802.16m

Advanced Features & Challenges of 16m

1. Unified single-user/multi-user MIMO Architecture– support various advanced multi-antenna processing techniques

including open-loop and closed single-user/multi-user MIMO schemes (single stream and multi-stream)

– Support multi-cell MIMO techniques

2. Multi-carrier support– The RF carriers may be of different bandwidths and can be non-

contiguous or belong to different frequency bands

– The channels may be of different duplexing modes, e.g. FDD, TDD

– Support wider band (up to 100MHz) by BW aggregation across contiguous or non-contiguous channels

96

3. Multi-hop relay-enabled architecture– Improve the SINR in the cell for coverage extension and

throughput enhancement

4. Support of femto-cells and self-organization

– Femto-cells are low power BS at homes achieving FMC – Self-configuration by allowing real plug and play installation of

network nodes and cells– Self-optimization by allowing automated or autonomous

optimization of network performance with respect to service availability, QoS, network efficiency and throughput

97

5. Enhanced multicast and broadcast service– Multi-carriers with dedicated broadcast only carriers – Single/multi-BS MBS

6. Multi-rate operation and handover – Support interworking with IEEE 802.11, GSM/EDGE, 3GPP,

3GPP2, CDMA2000etc.

7. Multi-radio coexistence– MS reports its co-located radio activities to BS– Accordingly, BS can operates properly via scheduling to support

multi-radio coexistence

98

References1. WiMAX 無線寬頻技術與垂直 智慧網通系統研究所 財團法人資訊工業策進

會 2. Digital Signal Processing (4th Edition): John G. Proakis

3. Ioannis Papapanagiotou, Dimitris Toumpakaris, Jungwon Lee, Michael Devetsikiotis, “A Survey of Mobile WiMAX IEEE 802.16m Standard” IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 11, NO. 4, FOURTH QUARTER 2009

4. IEEE 802.11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications

5. "802.11a-1999 High-speed Physical Layer in the 5 GHz band“

6. "802.11b-1999 Higher Speed Physical Layer Extension in the 2.4 GHz band“

7. "IEEE 802.11g-2003: Further Higher Data Rate Extension in the 2.4 GHz Band“

8. http://zh.wikipedia.org/wiki/IEEE_802.11

9. Shu Lin, Daniel J. Costello, (1983). “Error Control Coding: Fundamentals and Applications”

10.Shannon, C.E. (1948), "A Mathematical Theory of Communication", Bell System Technical Journal, 27, pp. 379–423 & 623–656, July & October, 1948.

11.John G. Proakis, Dimitris Manolakis: Digital Signal Processing - Principles, Algorithms and Applications

12.Lung-Sheng Tsai, Wei-Ho Chung*, and Da-Shan Shiu, "Lower Bounds on the Correlation Property for OFDM Sequences with Spectral-Null Constraints," IEEE Transactions on Wireless Communications, Volume 10, Issue 8, pp. 2652-2659, August 2011.

13.Lung-Sheng Tsai, Wei-Ho Chung*, and Da-Shan Shiu, "Synthesizing Low Autocorrelation and Low PAPR OFDM Sequences Under Spectral Constraints Through Convex Optimization and GS Algorithm," IEEE Transactions on Signal Processing, Volume 59, Issue 5, pp. 2234-2243, May 2011.

14.Ronald Y. Chang, Sian-Jheng Lin, and Wei-Ho Chung, "Efficient Implementation of the MIMO Sphere Detector: Architecture and Complexity Analysis," IEEE Transactions on Vehicular Technology, 2012

15.Ronald Y. Chang and Wei-Ho Chung*, "Best-First Tree Search with Probabilistic Node Ordering for MIMO Detection: Generalization and Performance-Complexity Tradeoff," IEEE Transactions on Wireless Communications, 2012.

100

101

Thank you

whc@citi.sinica.edu.tw