Diversity and Freedom: A Fundamental Tradeoff in Multiple Antenna Channels

Transcription

1 Diversity and Freedom: A Fundamental Tradeoff in Multiple Antenna Channels Lizhong Zheng and David Tse Department of EECS, U.C. Berkeley Feb 26, 2002 MSRI Information Theory Workshop

2 Wireless Fading Channels Channel Quality Fundamental characteristic of wireless channels: multipath fading due to constructive and destructive interference. Channel varies over time as well as frequency. Time

3 Multiple Antennas Multi-antenna communication is a hot field in recent years. But the research community has a split personality. There are two very different views of how multiple antennas can be used.

4 Multiple Antennas Multi-antenna communication is a hot field in recent years. But the research community has a split personality. There are two very different views of how multiple antennas can be used.

5 Multiple Antennas Multi-antenna communication is a hot field in recent years. But the research community has a split personality. There are two very different views of how multiple antennas can be used.

6 Multiple Antennas Multi-antenna communication is a hot field in recent years. But the research community has a split personality. There are two very different views of how multiple antennas can be used.

7 Diversity Fading Channel: h 1 Additional independent signal paths increase diversity. Diversity: receive, transmit or both. Compensate against channel unreliability.

8 Diversity Fading Channel: h 1 Fading Channel: h 2 Additional independent signal paths increase diversity. Diversity: receive, transmit or both. Compensate against channel unreliability.

9 Diversity Fading Channel: h 1 Fading Channel: h 2 Additional independent signal paths increase diversity. Diversity: receive, transmit or both. Compensate against channel unreliability.

10 Diversity Fading Channel: h 1 Fading Channel: h 2 Additional independent signal paths increase diversity. Diversity: receive, transmit or both. Compensate against channel unreliability.

11 Diversity Fading Channel: h 1 Fading Channel: h 2 Fading Channel: h 3 Fading Channel: h 4 Additional independent signal paths increase diversity. Diversity: receive, transmit or both. Compensate against channel unreliability.

12 Diversity Fading Channel: h 1 Fading Channel: h 2 Fading Channel: h 3 Fading Channel: h 4 Additional independent signal paths increase diversity. Diversity: receive, transmit or both. Compensate against channel unreliability.

13 Freedom Another way to view a 2 2 system: Increases the degrees of freedom in the system Multiple antennas provide parallel spatial channels: spatial multiplexing Fading is exploited as a source of randomness.

14 Freedom Spatial Channel Spatial Channel Another way to view a 2 2 system: Increases the degrees of freedom in the system Multiple antennas provide parallel spatial channels: spatial multiplexing Fading is exploited as a source of randomness.

15 Freedom Spatial Channel Spatial Channel Another way to view a 2 2 system: Increases the degrees of freedom in the system Multiple antennas provide parallel spatial channels: spatial multiplexing Fading is exploited as a source of randomness.

16 Freedom Spatial Channel Spatial Channel Another way to view a 2 2 system: Increases the degrees of freedom in the system Multiple antennas provide parallel spatial channels: spatial multiplexing Fading is exploited as a source of randomness.

17 Diversity vs. Multiplexing Fading Channel: h 1 Fading Channel: h Fading Channel: h 2 3 Spatial Channel Spatial Channel Fading Channel: h 4 Multiple antenna channel provides two types of gains: Diversity Gain vs. Spatial Multiplexing Gain Existing schemes focus on one type of gain

18 Diversity vs. Multiplexing Fading Channel: h 1 Fading Channel: h Fading Channel: h 2 3 Spatial Channel Spatial Channel Fading Channel: h 4 Multiple antenna channel provides two types of gains: Diversity Gain vs. Spatial Multiplexing Gain Existing schemes focus on one type of gain.

19 A Different Point of View Both types of gains can be achieved simultaneously in a given multiple antenna channel, but there is a fundamental tradeoff. We propose a unified framework which encompasses both diversity and freedom and study the optimal tradeoff.

20 A Different Point of View Both types of gains can be achieved simultaneously in a given multiple antenna channel, but there is a fundamental tradeoff. We propose a unified framework which encompasses both diversity and freedom and study the optimal tradeoff.

21 A Different Point of View Both types of gains can be achieved simultaneously in a given multiple antenna channel, but there is a fundamental tradeoff. We propose a unified framework which encompasses both diversity and multiplexing and study the optimal tradeoff.

22 Outline Problem formulation and main result on optimal tradeoff. Sketch of proof. Comparison of existing schemes.

23 Channel Model X 1 h 21 h 11 W 1 Y 1 W 2 X 2 h 22 Y 2 X M h N1 W N Y N h NM y = Hx + w, w i CN (0, 1) Rayleigh fading i.i.d. across antenna pairs (h ij CN (0, 1)). Focus on codes of T symbols, where H remains constant (slow, flat fading) H is known at the receiver but not the transmitter. SNR is the average signal-to-noise ratio at each receive antenna.

24 How to Define Diversity Gain Motivation: Binary Detection y = hx + w P e P ( h is small ) SNR 1 y 1 = h 1 x + w 1 y 2 = h 2 x + w 2 P e P ( h 1, h 2 are both small) SNR 2 Definition A scheme achieves diversity gain d, if P e SNR d Actual error probability instead of pairwise error probability (eg. Tarokh et al 98, Guey et al 99)

25 How to Define Diversity Gain Motivation: Binary Detection y = hx + w P e P ( h is small ) SNR 1 y 1 = h 1 x + w 1 y 2 = h 2 x + w 2 P e P ( h 1, h 2 are both small) SNR 2 Definition A scheme achieves diversity gain d, if P e SNR d Actual error probability instead of pairwise error probability (eg. Tarokh et al 98, Guey et al 99)

26 How to Define Diversity Gain Motivation: Binary Detection y = hx + w P e P ( h is small ) SNR 1 y 1 = h 1 x + w 1 y 2 = h 2 x + w 2 P e P ( h 1, h 2 are both small) SNR 2 Definition A scheme achieves diversity gain d, if P e SNR d Actual error probability instead of pairwise error probability (eg. Tarokh et al 98, Guey et al 99)

27 How to Define Diversity Gain Motivation: Binary Detection y = hx + w P e P ( h is small ) SNR 1 y 1 = h 1 x + w 1 y 2 = h 2 x + w 2 P e P ( h 1, h 2 are both small) SNR 2 Definition A scheme achieves diversity gain d, if P e SNR d Acutal error probability instead of pairwise error probability. (eg. Tarokh et al 98, Guey et al 99)

28 How to Define Spatial Multiplexing Gain Motivation: (Telatar 95, Foschini 96) Ergodic capacity: C(SNR) min{m, N} log SNR (bps/hz), Equivalent to min{m, N} parallel spatial channels. A scheme is a sequence of codes, one at each SNR level. Definition A scheme achieves spatial multiplexing gain r, if R = r log SNR (bps/hz) Increasing data rates instead of fixed data rate. (cf. Tarokh et al 98)

29 How to Define Spatial Multiplexing Gain Motivation: (Telatar 95, Foschini 96) Ergodic capacity: C(SNR) min{m, N} log SNR (bps/hz), Equivalent to min{m, N} parallel spatial channels. A scheme is a sequence of codes, one at each SNR level. Definition A scheme achieves spatial multiplexing gain r, if R = r log SNR (bps/hz) Increasing data rates instead of fixed data rate. (cf. Tarokh et al 98)

30 How to Define Spatial Multiplexing Gain Motivation: (Telatar 95, Foschini 96) Ergodic capacity: C(SNR) min{m, N} log SNR (bps/hz), Equivalent to min{m, N} parallel spatial channels. A scheme is a sequence of codes, one at each SNR level. Definition A scheme achieves spatial multiplexing gain r, if R = r log SNR (bps/hz) Increasing data rates instead of fixed data rate. (cf. Tarokh et al 98)

31 Fundamental Tradeoff A scheme achieves Spatial Multiplexing Gain r : R = r log SNR (bps/hz) and Diversity Gain d : P e SNR d Fundamental tradeoff: for any r, the maximum diversity gain achievable: d (r). r d (r)

32 Fundamental Tradeoff A scheme achieves Spatial Multiplexing Gain r : R = r log SNR (bps/hz) and Diversity Gain d : P e SNR d Fundamental tradeoff: for any r, the maximum diversity gain achievable: d (r). r d (r)

33 Main Result: Optimal Tradeoff As long as T M + N 1: (0,MN) Diversity Gain: d * (r) (min{m,n},0) Spatial Multiplexing Gain: r=r/log SNR For integer r, it is as though r transmit and r receive antennas were dedicated for multiplexing and the rest provide diversity.

34 Main Result: Optimal Tradeoff As long as T M + N 1: (0,MN) Diversity Gain: d * (r) (1,(M 1)(N 1)) (min{m,n},0) Spatial Multiplexing Gain: r=r/log SNR For integer r, it is as though r transmit and r receive antennas were dedicated for multiplexing and the rest provide diversity.

35 Main Result: Optimal Tradeoff As long as T M + N 1: (0,MN) Diversity Gain: d * (r) (1,(M 1)(N 1)) (2, (M 2)(N 2)) (min{m,n},0) Spatial Multiplexing Gain: r=r/log SNR For integer r, it is as though r transmit and r receive antennas were dedicated for multiplexing and the rest provide diversity.

36 Main Result: Optimal Tradeoff As long as T M + N 1: (0,MN) Diversity Gain: d * (r) (1,(M 1)(N 1)) (2, (M 2)(N 2)) (r, (M r)(n r)) (min{m,n},0) Spatial Multiplexing Gain: r=r/log SNR For integer r, it is as though r transmit and r receive antennas were dedicated for multiplexing and the rest provide diversity.

37 Main Result: Optimal Tradeoff As long as T M + N 1: (0,MN) Diversity Gain: d * (r) (1,(M 1)(N 1)) (2, (M 2)(N 2)) (r, (M r)(n r)) (min{m,n},0) Spatial Multiplexing Gain: r=r/log SNR For integer r, it is as though r transmit and r receive antennas were dedicated for multiplexing and the rest provide diversity.

38 Main Result: Optimal Tradeoff As long as T M + N 1: (0,MN) Single Antenna System M x N System Diversity Gain: d * (r) (0,1) (1,(M 1)(N 1)) (2, (M 2)(N 2)) (r, (M r)(n r)) (0,1) (min{m,n},0) Spatial Multiplexing Gain: r=r/log SNR For integer r, it is as though r transmit and r receive antennas were dedicated for multiplexing and the rest provide diversity.

39 Revisit the 2 2 Example Fading Channel: h 1 Fading Channel: h Fading Channel: h 2 3 Spatial Channel Spatial Channel Fading Channel: h 4

40 Revisit the 2 2 Example (ctd.) (0,4) Diversity Gain: d * (r) (1,1) (2,0) Spatial Multiplexing Gain: r=r/log SNR Tradeoff bridges the gap between the two types of approaches.

41 Revisit the 2 2 Example (ctd.) (0,4) Diversity Gain: d * (r) (1,1) (2,0) Spatial Multiplexing Gain: r=r/log SNR Tradeoff bridges the gap between the two types of approaches.

42 Adding More Antennas Diversity Advantage: d * (r) Spatial Multiplexing Gain: r=r/log SNR Capacity result: increasing min{m, N} by 1 adds 1 more degree of freedom. Tradeoff curve: increasing both M and N by 1 yields multiplexing gain +1 for any diversity requirement d.

43 Adding More Antennas Diversity Advantage: d * (r) Spatial Multiplexing Gain: r=r/log SNR Capacity result : increasing min{m, N} by 1 adds 1 more degree of freedom. Tradeoff curve : increasing both M and N by 1 yields multiplexing gain +1 for any diversity requirement d.

44 Adding More Antennas Diversity Advantage: d * (r) d Spatial Multiplexing Gain: r=r/log SNR Capacity result: increasing min{m, N} by 1 adds 1 more degree of freedom. Tradeoff curve: increasing both M and N by 1 yields multiplexing gain +1 for any diversity requirement d.

45 Increasing vs Fixed Code Rate Channel SNR 1 Channel SNR 2 Channel SNR SNR increases

46 Increasing vs Fixed Code Rate code Channel SNR 1 Channel SNR 2 Channel SNR SNR increases

47 Increasing vs Fixed Code Rate code code code Channel SNR 1 Channel SNR 2 Channel SNR SNR increases

48 Increasing vs Fixed Code Rate code code code Channel SNR 1 Channel SNR 2 Channel SNR log(p ) e SNR increases -d P e= SNR Slope= Diversity Gain log(snr)

49 Increasing vs Fixed Code Rate Slope= Spatial Multiplexing Gain code 1 code 2 code Channel Channel SNR SNR 1 2 Channel SNR log(p ) e SNR increases Slope= Diversity Gain log(snr)

50 Outline Problem formulation and main result on optimal tradeoff. Sketch of proof. Comparison of existing schemes.

51 Converse: Outage Bound Outage formulation for quasi-static scenarios. (Ozarow et al 94, Telatar 95) Look at the mutual information per symbol I(Q, H) as a function of the input distribution and channel realization. Error probability for finite block length T is asymptotically lower bounded by the outage probability: inf Q P H [I(Q, H) < R]. At high SNR, i.i.d. Gaussian input Q is asymptotically optimal, and I(Q, H) = log det [I + SNRHH ].

52 Converse: Outage Bound Outage formulation for quasi-static scenarios. (Ozarow et al 94, Telatar 95) Look at the mutual information per symbol I(Q, H) as a function of the input distribution and channel realization. Error probability for finite block length T is asymptotically lower bounded by the outage probability: inf Q P H [I(Q, H) < R]. At high SNR, i.i.d. Gaussian input Q is asymptotically optimal, and I(Q, H) = log det [I + SNRHH ].

53 Converse: Outage Bound Outage formulation for quasi-static scenarios. (Ozarow et al 94, Telatar 95) Look at the mutual information per symbol I(Q, H) as a function of the input distribution and channel realization. Error probability for finite block length T is asymptotically lower bounded by the outage probability: inf Q P H [I(Q, H) < R]. At high SNR, i.i.d. Gaussian input Q is asymptotically optimal, and I(Q, H) = log det [I + SNRHH ].

54 Outage Analysis Scalar 1 1 channel: For target rate R = r log SNR, r < 1, P {log(1 + SNR h 2 ) < r log SNR} P { h 2 < SNR (1 r)} SNR (1 r) = d out (r) = 1 r Outage occurs when the channel gain h 2 is small. More generally, outage occurs for the multi-antenna channel when some or all of the singular values of H are small. But unlike the scalar channel, there are many ways for this to happen in a vector channel.

55 Outage Analysis Scalar 1 1 channel: For target rate R = r log SNR, r < 1, P {log(1 + SNR h 2 ) < r log SNR} P { h 2 < SNR (1 r)} SNR (1 r) = d out (r) = 1 r Outage occurs when the channel gain h 2 is small. More generally, outage occurs for the multi-antenna channel when some or all of the singular values of H are small. But unlike the scalar channel, there are many ways for this to happen in a vector channel.

56 Typical Outage Behavior v = vector of singular values of H. Laplace Principle: p out = min v Out SNR f(v) Result: At target rate R = r log SNR, outage typically occurs when H is near a rank r matrix, i.e. out of the min{m, N} non-zero squared singular values: r of them are order 1; min{m, N} r + 1 of them are are order SNR 1 ; 1 of them is order SNR (r r ) ( just small enough to cause outage) When r is integer, exactly r squared singular values are order 1 and min{m, N} are order SNR 1.

57 Typical Outage Behavior v = vector of singular values of H. Laplace Principle: p out = min v Out SNR f(v) Result: At target rate R = r log SNR, outage typically occurs when H is near a rank r matrix, i.e. out of the min{m, N} non-zero squared singular values: r of them are order 1; min{m, N} r + 1 of them are are order SNR 1 ; 1 of them is order SNR (r r ) ( just small enough to cause outage) When r is integer, exactly r squared singular values are order 1 and min{m, N} are order SNR 1.

58 Geometric Picture (integer r) Scalar Channel 0 p out SNR (M r)(n r), (M r)(n r) is the dimension of the normal space to the sub-manifold of rank r matrices within the set of all M N matrices.

59 Geometric Picture (integer r) Scalar Channel SNR -1/2 Bad H Good H p out SNR (M r)(n r), (M r)(n r) is the dimension of the normal space to the sub-manifold of rank r matrices within the set of all M N matrices.

60 Geometric Picture (integer r) Scalar Channel Vector Channel -1/2 SNR All MxN Matrices Bad H Good H Rank(H)=r p out SNR (M r)(n r), (M r)(n r) is the dimension of the normal space to the sub-manifold of rank r matrices within the set of all M N matrices.

61 Geometric Picture (integer r) Scalar Channel Vector Channel Typical Bad H SNR -1/2 Bad H Good H Good H Full Rank -1/2 SNR Rank(H)=r p out SNR (M r)(n r), (M r)(n r) is the dimension of the normal space to the sub-manifold of rank r matrices within the set of all M N matrices.

62 Geometric Picture (integer r) Scalar Channel Vector Channel Typical Bad H SNR -1/2 Bad H Good H Good H Full Rank -1/2 SNR Rank(H)=r p out SNR (M r)(n r), (M r)(n r) is the dimension of the normal space to the sub-manifold of rank r matrices within the set of all M N matrices.

63 Piecewise Linearity of Tradeoff Curve (0,MN) Single Antenna System M x N System Diversity Gain: d * (r) (0,1) (1,(M 1)(N 1)) (2, (M 2)(N 2)) (r, (M r)(n r)) (0,1) (min{m,n},0) Spatial Multiplexing Gain: r=r/log SNR Scalar channel: qualitatively same outage behavior for all r. Vector channel: qualitatively different outage behavior for different r.

64 Achievability: Random Codes Outage performance achievable as codeword length T. But what about for finite T? Look at the performance of i.i.d Gaussian random codes. Can the outage behavior be achieved?

65 Analysis of Random Codes Errors can occur due to three events: Channel H is aytically bad (outage) Additive Gaussian noise atypically large. Random codewords are atypically close together. Outage analysis only needs to focus on the first event, but for finite T all three effects come into play.

66 Analysis of Random Codes Errors can occur due to three events: Channel H is aytically bad (outage) Additive Gaussian noise atypically large. Random codewords are atypically close together. Outage analysis only needs to focus on the first event, but for finite T all three effects come into play.

67 Analysis of Random Codes Errors can occur due to three events: Channel H is aytically bad (outage) Additive Gaussian noise atypically large. Random codewords are atypically close together. Outage analysis only needs to focus on the first event, but for finite T all three effects come into play.

68 Analysis of Random Codes Errors can occur due to three events: Channel H is aytically bad (outage) Additive Gaussian noise atypically large. Random codewords are atypically close together. Outage analysis only needs to focus on the first event, but for finite T all three effects come into play.

69 Analysis of Random Codes Errors can occur due to three events: Channel H is aytically bad (outage) Additive Gaussian noise atypically large. Random codewords are atypically close together. Outage analysis only needs to focus on the first event, but for finite T all three effects come into play.

70 Multiplicative Fading vs Additive Noise Look at two codewords at Euclidean distance x. Error Event A: H typical, AWGN large P (A) exp( x). Error Event B: H near singular, AWGN typical At high SNR, x P (B) x α. = P (B) P (B).

71 Multiplicative Fading vs Additive Noise Look at two codewords at Euclidean distance x. Error Event A: H typical, AWGN large P (A) exp( x). Error Event B: H near singular, AWGN typical At high SNR, x P (B) x α. = P (B) P (B).

72 Multiplicative Fading vs Additive Noise Look at two codewords at Euclidean distance x. Error Event A: H typical, AWGN large P (A) exp( x). Error Event B: H near singular, AWGN typical At high SNR, x P (B) x α. = P (B) P (B).

73 Multiplicative Fading vs Code Randomness Distance between random codewords may deviate from typical distance x. Error Event C: codewords atypically close P (C) x β, Also polynomial in x, just like the effect due to channel fading. As long as T M + N 1, the typical error event is due to bad channel rather than bad codewords. For T < M + N 1, random codes are not good enough. (ISIT 02)

74 To Fade or Not to Fade?

75 Line-of-Sight vs Fading Channel d LOS Non-Fading Channel 1 1 Scalar Fading Channel r In a scalar 1 1 system, line-of-sight AWGN is better. In a vector M N system, it depends.

76 Line-of-Sight vs Fading Channel d MN LOS Non-Fading Channel 1 1 Scalar Fading Channel Vector Fading Channel min{m,n} r In a scalar 1 1 system, line-of-sight AWGN is better. In a vector M N system, it depends.

77 Outline Problem formulation and main result on optimal tradeoff. Sketch of proof. Comparison of existing schemes.

78 Using the Optimal Tradeoff Curve Provide a unified framework to compare different schemes. For a given scheme, compute Compare with d (r) r d(r)

79 Use the Optimal Tradeoff Curve Provide a unified framework to compare different schemes. For a given scheme, compute Compare with d (r) r d(r)

80 Two Diversity-Based Schemes Focus on two transmit antennas. Y = HX + W Repetition Scheme: X = x x 1 r = H x 1 + w Alamouti Scheme X = x 1 x 2 x 2 x 1 [r 1 r 2 ] = H [x 1 x 2 ] + w

81 Two Diversity-Based Schemes Focus on two transmit antennas. Y = HX + W Repetition Scheme: X = x x 1 r = H x 1 + w Alamouti Scheme X = x 1 x 2 x 2 x 1 [r 1 r 2 ] = H [x 1 x 2 ] + w

82 Two Diversity-Based Schemes Focus on two transmit antennas. Y = HX + W Repetition Scheme: X = x x 1 r = H x 1 + w Alamouti Scheme X = x 1 x 2 x 2 x 1 [r 1 r 2 ] = H [x 1 x 2 ] + w

83 Two Diversity-Based Schemes Focus on two transmit antennas. Y = HX + W Repetition Scheme: X = x x 1 r = H x 1 + w Alamouti Scheme X = x 1 x 2 x 2 x 1 [r 1 r 2 ] = H [x 1 x 2 ] + [w 1 w 2 ]

84 Comparison: 2 1 System Repetition: r 1 = H x 1 + w Alamouti: [r 1 r 2 ] = H [x 1 x 2 ] + [w 1 w 2 ] Diversity Gain: d * (r) (0,2) (1/2,0) Spatial Multiplexing Gain: r=r/log SNR

85 Comparison: 2 1 System Repetition: r 1 = H x 1 + w Alamouti: [r 1 r 2 ] = H [x 1 x 2 ] + [w 1 w 2 ] Diversity Gain: d * (r) (0,2) (1/2,0) (0,1) Spatial Multiplexing Gain: r=r/log SNR

86 Comparison: 2 1 System Repetition: r 1 = H x 1 + w Alamouti: [r 1 r 2 ] = H [x 1 x 2 ] + [w 1 w 2 ] Repetitive Alamouti Optimal Diversity Gain: d * (r) (0,2) (1/2,0) (0,1) Spatial Multiplexing Gain: r=r/log SNR

87 Comparison: 2 2 System Repetition: r 1 = H x 1 + w Alamouti: [r 1 r 2 ] = H [x 1 x 2 ] + [w 1 w 2 ] (0,4) Diversity Gain: d * (r) (1/2,0) Spatial Multiplexing Gain: r=r/log SNR

88 Comparison: 2 2 System Repetition: r 1 = H x 1 + w Alamouti: [r 1 r 2 ] = H [x 1 x 2 ] + [w 1 w 2 ] (0,4) Diversity Gain: d * (r) (1/2,0) (1,0) Spatial Multiplexing Gain: r=r/log SNR

89 Comparison: 2 2 System Repetition: r 1 = H x 1 + w Alamouti: [r 1 r 2 ] = H [x 1 x 2 ] + [w 1 w 2 ] (0,4) Repetitive Alamouti Optimal Diversity Gain: d * (r) (1,1) (1/2,0) (1,0) (2,0) Spatial Multiplexing Gain: r=r/log SNR

90 V-BLAST Antenna 1: Antenna 2: T Nulling and Canceling Independent data streams transmitted over antennas

91 V-BLAST Receive Antenna 1: Antenna 2: Null Nulling and Canceling Independent data streams transmitted over antennas

92 V-BLAST Receive Antenna 1: Antenna 2: Null Nulling and Canceling Independent data streams transmitted over antennas

93 V-BLAST Antenna 1: Antenna 2: Nulling and Canceling Independent data streams transmitted over antennas

94 V-BLAST Cancel Antenna 1: Antenna 2: Nulling and Canceling Independent data streams transmitted over antennas

95 V-BLAST Antenna 1: Antenna 2: T Nulling and Canceling Independent data streams transmitted over antennas

96 Tradeoff Performance of V-BLAST (N N) Original V-BLAST 1 (N,0) Low diversity due to lack of coding over space

97 Tradeoff Performance of V-BLAST (N N) V-BLAST with optimal rate allocation (0,N) 1 (N,0) Low diversity due to lack of coding over space

98 Tradeoff Performance of V-BLAST (N N) Compare to the optimal tradeoff (0,N 2 ) (0,N) 1 (N,0) Low diversity due to lack of coding over space

99 Tradeoff Performance of V-BLAST (N N) Compare to the optimal tradeoff (0,N 2 ) (0,N) 1 (N,0) Low diversity due to lack of coding over space

100 D-BLAST Antenna 1: Antenna 2: T Assume T =, ignore the overhead.

101 D-BLAST Antenna 1: Antenna 2: Receive Assume T =, ignore the overhead.

102 D-BLAST Receive Antenna 1: Antenna 2: Null Assume T =, ignore the overhead.

103 D-BLAST Antenna 1: Antenna 2: Assume T =, ignore the overhead.

104 D-BLAST Cancel Antenna 1: Antenna 2: Receive Assume T =, ignore the overhead.

105 D-BLAST Receive Antenna 1: Antenna 2: Null Assume T =, ignore the overhead.

106 D-BLAST Antenna 1: Antenna 2: Assume T =, ignore the overhead.

107 D-BLAST Cancel Antenna 1: Antenna 2: Receive Assume T =, ignore the overhead.

108 D-BLAST Receive Antenna 1: Antenna 2: Null Ignore the overhead for now.

109 D-BLAST: Square System Diversity Advantage: d * (r) (1,N(N+1)/2) (N 1,1) (N,0) (N 2,3) Spatial Multiplexing Gain: r=r/log SNR Can achieve full multiplexing gain Maximum diversity gain d = N(N+1) 2.

110 D-BLAST: Square System (0,N 2 ) DBLAST Optimal Diversity Advantage: d * (r) (1,N(N+1)/2) (N 1,1) (N,0) Spatial Multiplexing Gain: r=r/log SNR Can achieve full multiplexing gain Maximum diversity gain d = N(N+1) 2.

111 Replace Nulling by MMSE?

112 D-BLAST+MMSE (0,N 2 ) DBLAST+Nulling DBLAST+MMSE Diversity Advantage: d * (r) (1,N(N+1)/2) (N 1,1) (N,0) Spatial Multiplexing Gain: r=r/log SNR Achieve the optimal: successive cancellation + MMSE has the optimal outage performance. Difference between MMSE and Nulling.

113 D-BLAST+MMSE (0,N 2 ) DBLAST+Nulling DBLAST+MMSE Optimal Diversity Advantage: d * (r) (1,N(N+1)/2) (N 1,1) (N,0) Spatial Multiplexing Gain: r=r/log SNR Achieve the optimal: successive cancellation + MMSE has the optimal outage performance. Difference between MMSE and Nulling.

114 D-BLAST+MMSE (0,N 2 ) DBLAST+Nulling DBLAST+MMSE Optimal Diversity Advantage: d * (r) (1,N(N+1)/2) (N 1,1) (N,0) Spatial Multiplexing Gain: r=r/log SNR Achieve the optimal: successive cancellation + MMSE has the optimal outage performance. Difference between MMSE and Nulling.

115 Penalty due to Overhead DBLAST+Nulling DBLAST+MMSE Optimal Diversity Advantage: d * (r) Spatial Multiplexing Gain: r=r/log SNR

116 Conclusion The diversity-multiplexing tradeoff is a fundamental way of looking at fading channels. Same framework can be applied to other scenarios: multiuser, non-coherent, more complex channel models, etc.