Communication Theory Workshop Joint Space-Division and Multiplexing: How to Achieve Massive MIMO Gains in FDD Systems Giuseppe Caire University of Southern California, Viterbi School of Engineering, Los Angeles, CA Phuket, Thailand, June 23-26, 2013 Channel estimation bottleneck on MU-MIMO • High-SNR capacity of Nt ×Nr single-user MIMO with coherence block-length T [Zheng-Tse, 2003]: C(SNR) = M ∗(1 − M ∗/T ) log SNR + O(1), M ∗ = min{Nt, Nr , T /2} • Trivial cooperative bound: for large M = Nt and N = KNr , the coherence block T is the limiting factor. • ⇒ Disappointing theoretical performance of “CoMP” (base station cooperation), in FDD. 18 Inter-cell Cooperation γ=1, τ=1/32 γ=2, τ=1/32 γ=4, τ=1/32 γ=8, τ=1/32 16 cluster controller Cell sum rate (bps/Hz) 14 BS 3 12 10 8 6 4 BS 2 2 BS 1 0 0 5 10 15 20 25 B Fully cooperative network MIMO/partially coordinated beamforming: Inter-cell interference mitigation Larger antenna array gain H. Huh (USC) Large System Analysis of Multi-cell MIMO Downlink 1 May 12, 2011 4 / 52 Channel model with antenna correlation • In FDD, for large macro-cellular base stations, we have to exploit channel dimensionality reduction while still exploiting the large number of antennas at the BS. • Idea: exploit the asymmetric spatial channel correlation at the BS and at the UTs. • Isotropic scattering, |u − u0| = λD: 1 ∗ 0 E [h(u)h (u )] = 2π Z π e−j2πD cos(α)dα = J0(2πD) −π • Two users separated by a few meters (say 10 λ) are practically uncorrelated. 2 • In contrast, the base station sees user groups at different AoAs under narrow AS ∆ ≈ arctan(r/s). r s scattering ring ✓ region containing the BS antennas • This leads to the Tx antenna correlation model h = UΛ1/2w, with [R]m,p 1 = 2∆ Z ∆ e R = UΛUH j kT (α+θ)(um −up ) dα. −∆ 3 Joint Space Division and Multiplexing (JSDM) • K users selected to form G groups, with ≈ same channel correlation. H = [H1, . . . , HG], with Hg = Ug Λ1/2 g Wg . • Two-stage precoding: V = BP. • B ∈ CM ×bg is a pre-beamforming matrix function of {Ug , Λg } only. • P ∈ Cbg ×Sg is a precoding matrix that depends on the effective channel. • The effective channel matrix is given by H H1 B1 HH1 B2 · · · HH2 B1 HH2 B2 · · · H H = .. ... .. HHGB1 HHGB2 · · · HH1 BG HH2 BG .. . HHGBG 4 • Per-Group Processing: If estimation and feedback of the whole H is still too costly, then each group estimates its own diagonal block Hg = BHg Hg , and P = diag(P1, · · · , PG). • This results in yg = HHg Bg Pg dg + X HHg Bg0 Pg0 dg0 + zg g 0 6=g 5 Achieving capacity with reduced CSIT PG • Let r = g=1 rg and suppose that the channel covariances of the G groups are such that U = [U1, · · · , UG] is M × r tall unitary (i.e., r ≤ M and UHU = Ir ). • Eigen-beamforming (let bg = rg and Bg = Ug ) achieves exact block diagonalization. • The decoupled MU-MIMO channel takes on the form yg = Hg HPg dg + zg = WgHΛg1/2Pg dg + zg , for g = 1, . . . , G, where Wg is a rg × Kg i.i.d. matrix with elements ∼ CN (0, 1). Theorem 1. For U tall unitary, JSDM with PGP achieves the same sum capacity of the corresponding MU-MIMO downlink channel with full CSIT. 6 Block Diagonalization • For given target numbers of streams per group {Sg } and dimensions {bg } satisfying Sg ≤ bg ≤ rg , we can find the pre-beamforming matrices Bg such that: UHg0 Bg = 0 ∀ g 0 6= g, and rank(UHg Bg ) ≥ Sg • Necessary condition for exact BD Span(Bg ) ⊆ Span⊥({Ug0 : g 0 6= g}). • When Span⊥({Ug0 : g 0 6= g}) has dimension smaller than Sg , the rank condition on the diagonal blocks cannot be satisfied. • In this case, Sg should be reduced (reduce the number of served users per group) or, as an alternative, approximated BD based on selecting rg? < rg dominant eigenmodes for each group g can be implemented. 7 Performance analysis with regularized ZF • The transformed channel matrix H has dimension b × S, with blocks Hg of dimension bg × Sg . • For simplicity we allocate to all users the same fraction of the total transmit power, pgk = PS . • For PGP, the regularized zero forcing (RZF) precoding matrix for group g is given by ¯ g Hg , Pg,rzf = ζ¯g K where ¯g = K and where ζ¯g2 = h Hg HHg + bg αIbg i−1 S0 tr(HHg KHg BHg Bg Kg Hg ) . 8 • The SINR of user gk given by γgk ,pgp = P ¯2 H ¯ g BHhg |2 ζ |h B K g g k S g gk P P P P H 2 ¯2|hH Bg K ¯2 H ¯ ¯ g BHhg |2 + P ζ g j j6=k g gk g 0 6=g j ζg 0 |hgk Bg 0 Kg 0 Bg 0 hgj0 | S S +1 • Using the “deterministic equivalent” method of [Wagner, Couillet, Debbah, Slock, 2011], we can calculate γgok ,pgp such that M →∞ γgk ,pgp − γgok ,pgp −→ 0 9 Example • M = 100, G = 6 user groups, Rank(Rg ) = 21, effective rank rg∗ = 11. • We serve S 0 = 5 users per group with b0 = 10, r? = 6 and r? = 12. • For rg∗ = 12: 150 bit/s/Hz at SNR = 18 dB: 5 bit/s/Hz per user, for 30 users served simultaneously on the same time-frequency slot. 350 300 250 250 200 200 Sum Rate Sum Rate 300 350 Capacity ZFBF, JGP RZFBF, JGP ZFBF, PGP RZFBF, PGP 150 150 100 100 50 50 0 0 5 10 15 SNR (in dBs) 20 25 30 Capacity ZFBF, JGP RZFBF, JGP ZFBF, PGP RZFBF, PGP 0 0 5 10 15 20 25 30 SNR (in dBs) 10 Training, Feedback and Computations Requirements • Full CSI: 100 × 30 channel matrix ⇒ 3000 complex channel coefficients per coherence block (CSI feedback), with 100 × 100 unitary “common” pilot matrix for downlink channel estimation. • JSDM with PGP: 6 × 10 × 5 diagonal blocks ⇒ 300 complex channel coefficients per coherence block (CSI feedback), with 10 × 10 unitary “dedicated” pilot matrices for downlink channel estimation, sent in parallel to each group through the pre-beamforming matrix. • One order of magnitude saving in both downlink training and CSI feedback. • Computation: 6 matrix inversions of dimension 5 × 5, with respect to one matrix inversion of dimension 30 × 30. 11 Non-ideal CSIT • Parallel downlink training in all groups: a scaled unitary training matrix Xtr of dimension b0 ×b0 is sent, simultaneously, to all groups in the common downlink training phase. • Received signal at group g receivers is given by Yg = HHg Xtr + X Hg HBg0 Xtr + Zg . g 0 6=g • Multiplying from the right by XHtr and letting ρtr denote the power allocated to training, we obtain Yg XHtr = ρtrHHg + ρtr X Hg HBg0 + Zg XHtr. g 0 6=g 12 • The relevant observation for the gk -th user effective channel is: eg h k √ √ X H = ρtrhgk + ρtr zgk . Bg0 hgk + e g 0 6=g • The corresponding MMSE estimator is given by bg = E h k = √ h eH hg k h gk i h E ρtr BHg Rg i H −1 eg h e eg h h k gk k G X g 0 =1 Bg0 ρtr G X −1 BHg0 Rg Bg00 + Ib0 eg h k g 0 ,g 00 =1 −1 1 1 T T eg ˜ ˜ =√ Mg Rg O ORg O + Ib 0 h k ρtr ρtr 13 where we used the fact that hgk = BHg hgk , and we introduced the b0 × b block matrices Mg = [0, . . . , 0, |{z} Ib0 , 0, . . . , 0] block g O = [Ib0 , Ib0 , . . . , Ib0 ]. • Notice that in the case of perfect BD we have that Rg Bg0 = 0 for g 0 6= g. Therefore, the MMSE estimator reduces to bg h k −1 1 1 ¯ ¯ eg h Ib0 = √ Rg Rg + k ρtr ρtr ¯ g = BHRg Bg . where R g 14 • Also in this case, the deterministic equivalent approximations of the SINR terms for RZFBF and ZFBF precoding can be be computed. • Eventually, the achievable rate of user gk is given by Rgk ,pgp,csit b = max 1 − , 0 × log 1 + γ bgok ,pgp,csit . T 0 15 Tradeoff parameter b0 • b0 large yields better conditioned matrices, but it “costs” more in terms of training phase dimension. SNR = 30 dB SNR = 10 dB 230 220 80 210 RZFBF, PGP, ICSI ZFBF, PGP, ICSI RZFBF, PGP ZFBF, PGP 70 RZFBF, PGP, ICSI ZFBF, PGP, ICSI RZFBF, PGP ZFBF, PGP 200 Sum Rates Sum Rates 190 60 50 180 170 160 40 150 140 30 130 4 6 8 10 12 14 b‘ (a) S’ = 4, SNR = 10dB 16 4 6 8 10 12 14 16 b‘ (b) S’ = 8, SNR = 30dB 16 Impact of non-ideal CSIT 300 250 400 Full CSI, RZFBF Full CSI, ZFBF JGP, RZFBF JGP, ZFBF PGP, RZFBF PGP, ZFBF PGP ICSI, RZFBF PGP ICSI, ZFBF 350 Full CSI, RZFBF Full CSI, ZFBF JGP, RZFBF JGP, ZFBF PGP, RZFBF PGP, ZFBF PGP ICSI, RZFBF PGP ICSI, ZFBF 300 200 Sum Rate Sum Rate 250 150 200 150 100 100 50 50 0 0 5 10 15 20 25 30 0 0 5 10 15 SNR (in dBs) SNR (in dBs) (c) S’ = 4 (d) S’ = 8 20 25 30 17 Discussion: is the tall unitary realistic? • For a Uniform Linear Array (ULA), R is Toeplitz, with elements [R]m,p 1 = 2∆ Z ∆ e−j2πD(m−p) sin(α+θ)dα, m, p ∈ {0, 1, . . . , M − 1} −∆ • We are interested in calculating the asymptotic rank, eigenvalue CDF and structure of the eigenvectors, for M large, for given geometry parameters D, θ, ∆. • Correlation function rm 1 = 2∆ Z ∆ e−j2πDm sin(α+θ)dα. −∆ 18 • As M → ∞, the eigenvalues of R tend to the “power spectral density” (i.e., the DT Fourier transform of rm), S(ξ) = ∞ X rme−j2πξm m=−∞ sampled at ξ = k/M , for k = 0, . . . , M − 1. • After some algebra, we arrive at 1 S(ξ) = 2∆ X 1 p . 2 2 D − (m − ξ) m∈[D sin(−∆+θ)+ξ,D sin(∆+θ)+ξ] 19 Szego’s Theorem: eigenvalues Theorem 2. The empirical spectral distribution of the eigenvalues of R, M X 1 (M ) FR (λ) = 1{λm(R) ≤ λ}, M m=1 converges weakly to the limiting spectral distribution (M ) lim FR M →∞ (λ) = F (λ) = Z dξ. S(ξ)≤λ 20 Example: M = 400, θ = π/6, D = 1, ∆ = π/10. Exact empirical eigenvalue cdf of R (red), its approximation the circulant matrix C (dashed blue) and its approximation from the samples of S(ξ) (dashed green). 1 0.9 Toeplitz Circulant, M finite Circulant, M ∞ 0.8 0.7 CDF 0.6 0.5 0.4 0.3 0.2 0.1 0 0 0.5 1 1.5 2 2.5 Eigen Values 21 A less well-known Szego’s Theorem: eigenvectors Theorem 3. Let λ0(R) ≤ . . . , ≤ λM −1(R) and λ0(C) ≤ . . . , ≤ λM −1(C) denote the set of ordered eigenvalues of R and C, and let U = [u0, . . . , uM −1] and F = [f0, . . . , fM −1] denote the corresponding eigenvectors. For any interval [a, b] ⊆ [κ1, κ2] such that F (λ) is continuous on [a, b], consider the eigenvalues index sets I[a,b] = {m : λm(R) ∈ [a, b]} and J[a,b] = {m : λm(C) ∈ [a, b]}, and define U[a,b] = (um : m ∈ I[a,b]) and F[a,b] = (fm : m ∈ J[a,b]) be the submatrices of U and F formed by the columns whose indices belong to the sets I[a,b] and J[a,b], respectively. Then, the eigenvectors of C approximate the eigenvectors of R in the sense that 2 1 H H lim U[a,b]U[a,b] − F[a,b]F[a,b] = 0. M →∞ M F Consequence 1: Ug is well approximated by a “slice” of the DFT matrix. Consequence 2: DFT pre-beamforming is near optimal for large M . 22 Theorem 4. The asymptotic normalized rank of the channel covariance matrix R, with antenna separation λD, AoA θ and AS ∆, is given by ρ = min{1, B(D, θ, ∆)}, with B(D, θ, ∆) = |D sin(−∆ + θ) − D sin(∆ + θ)|. Theorem 5. Groups g and g 0 with angle of arrival θg and θg0 and common angular spread ∆ have spectra with disjoint support if their AoA intervals [θg − ∆, θg + ∆] and [θg0 − ∆, θg0 + ∆] are disjoint. 23 DFT Pre-Beamforming 1500 8 θ = −45 θ=0 θ = 45 7 RZFBF, Full ZFBF, Full RZFBF, DFT ZFBF, DFT 6 Sum Rate Eigen Values 1000 5 4 3 500 2 1 0 −0.5 0 ξ • ULA with M = 400, G = 3, θ1 = 0.5 0 0 5 10 15 20 25 30 SNR −π 4 , θ2 = 0, θ3 = π4 , D = 1/2 and ∆ = 15 deg. 24 Super-Massive MIMO 25 • Idea: produce a 3D pre-beamforming by Kronecker product of a “vertical” beamforming, separating the sector into L concentric regions, and a “horizontal” beamforming, separating each `-th region into G` groups. • Horizontal beam forming is as before. • For vertical beam forming we just need to find one dominating eigenmode per region, and use the BD approach. • A set of simultaneously served groups forms a “pattern”. • Patterns need not cover the whole sector. • Different intertwined patterns can be multiplexed in the time-frequency domain in order to guarantee a fair coverage. 26 An example • Cell radius 600m, group ring radius 30m, array height 50m, M = 200 columns, N = 300 rows. • Pathloss g(x) = 1 1+( dx )δ with δ = 3.8 and d0 = 30m. 0 • Same color regions are served simultaneously. Each ring is given equal power. 0 120 degree sector 1000 BD, RZFBF BD, ZFBF DFT, RZFBF DFT, ZFBF 600 m 50 m Sum rate of annular regions 900 800 700 600 500 400 1 2 3 4 5 6 7 8 Annular Region Index l 27 Sum throughput (bit/s/Hz) under PFS and Max-min Fairness Scheme PFS, RZFBF PFS, ZFBF MAXMIN, RZFBF MAXMIN, ZFBF Approximate BD 1304.4611 1298.7944 1273.7203 1267.2368 DFT based 1067.9604 1064.2678 1042.1833 1037.2915 1000 bit/s/Hz × 40 MHz of bandwidth = 40 Gb/s per sector. 28 Our on-going work • Compatibility with an in-band Small-Cell tier: eICIC in the spatial domain: turn on and off the “spotbeams”. • Multi-cell strategies: activate mutually compatible patterns of groups in adjacent sectors. • User grouping: we developed a very efficient way to cluster users according to their dominant subspaces (quantization according to chordal distance). See [Adhikary, Caire, arXiv:1305.7252]. • Hybrid Beamforming and mm-wave application: the DFT pre-beamforming can be implemented by phase shifters in analog domain. • Estimation of the long-term channel statistics: revamped interest in superresolution methods (MUSIC, ESPRIT) especially for the mm-wave case. 29 Conclusions • Exploiting transmit antenna correlation reduces the channel to a simpler ≈ block diagonal structure. • This is generalized sectorization! with MU-MIMO independently in each “sector” (group). • We need only very coarse information on AoA and AS for the users .... DFT pre-beamforming. • The idea can be easily extended to 3D beamforming (introducing elevation direction, Kronecker product structure). • Downlink training, CSIT feedback and computation are greatly reduced (suitable for FDD). • JSDM lends itself naturally to spatial-domain eICIC, simple inter-cell coordination, hybrid beamforming for mm-wave applications. 30 Thank You 31

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