GENERALISED FFT-IFFT STRUCTURE BASED FREQUENCY DIVISION MULTIPLEXING TRANSCEIVER

Abstract

A generalized frequency division multiplexing (GFDM) transceiver system includes a low complex GFDM transmitter with multiple sub-carriers and timeslots having IFFT based modulator for modulating data corresponding to a particular timeslot and different sub-carriers to corresponding sub-carrier frequencies and thereby generating transmittable GFDM data signal, multipath frequency selective fading GFDM channel having uncorrelated channel coefficients corresponding to different paths for transmitting the modulated GFDM data signal and a low complex GFDM receiver configured to operate with said multipath frequency selective fading channel involving channel equalization followed by self-interference equalization to receive the transmitted modulated GFDM data signal and thereby de-modulate the GFDM data signal to obtain the data.

Claims

1. A generalized frequency division multiplexing (GFDM) transceiver system comprising: low complex GFDM transmitter with multiple sub-carriers and timeslots having IFFT based modulator for modulating data corresponding to a particular timeslot and different sub-carriers to corresponding sub-carrier frequencies and thereby generating transmittable GFDM data signal; multipath frequency selective fading GFDM channel having uncorrelated channel coefficients corresponding to different paths for transmitting the modulated GFDM data signal; and low complex GFDM receiver configured to operate with said multipath frequency selective fading channel involving channel equalization followed by self-interference equalization to receive the transmitted modulated GFDM data signal and thereby de-modulate the GFDM data signal to obtain the data.

2. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 1, wherein the low complex GFDM transmitter includes: N-point IFFT operator to receive the data corresponding to a particular timeslot and different sub-carriers and modulate the same to corresponding sub-carrier frequencies; means for shuffling physical connections in the N-point IFFT operator for grouping the modulated data to sub-carrier numbers, whereby, in each group, the data is converted into frequency domain using M-point FFT operator and multiplied with a precomputed weight and thereafter converted back into time domain by using M-point IFFT operator; and means for shuffling physical connections in the M-point IFFT operator for grouping the data according to time slots and generate transmittable GFDM data signal.

3. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 1, wherein the low complex GFDM transmitter generated transmittable GFDM data signal is $x\left[n\right]=\frac{1}{\sqrt{N}}.Math.\underset{m=0}{\overset{M-1}{.Math.}}.Math..Math.\underset{k=0}{\overset{N-1}{.Math.}}.Math..Math.d_{m,k}.Math.{g\left[n-\mathrm{mN}\right]}_{\mathrm{MN}}.Math.e^{\frac{j.Math..Math.2.Math..Math..Math.\mathrm{nk}}{N}}$ for N sub-carriers and M timeslots, where g(n), n=0, 1, . . . , MN1 is MN length filter response and d.sub.m,kC, m=0, 1, . . . , M1, k=0, 1, . . . , N1 is QAM modulated data symbol.

4. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 3, wherein the GFDM transmitted signal is critically sampled Inverse Discrete Gabor Transform (IDGT) of d by using the IDGT matrix factorization whereby Modulation Matrix, A can be given as: $A=G{U}_N=\left(\begin{array}{cccc}{}_0& {}_{M-1}& \mathrm{.Math.}& {}_1\\ {}_1& {}_0& \mathrm{.Math.}& {}_2\\ \mathrm{.Math.}& \mathrm{.Math.}& & \mathrm{.Math.}\\ {}_{M-1}& {}_{M-2}& \mathrm{.Math.}& {}_0\end{array}\right).Math.\left(\begin{array}{ccc}{W}_N& & \\ & & \\ & & W_N\end{array}\right)$ where, .sub.m=diag{g[mN], g[mN+1], . . . , g[mN+N1]} for 0mM1, is NN diagonal matrix and W.sub.N is NN normalized IDFT matrix.

5. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 1, wherein the uncorrelated channel coefficients corresponding to different paths for transmitting the modulated GFDM data signal constitutes channel impulse response vector given as h=[h.sub.0, h.sub.1, . . . h.sub.L-1].sup.T where L is channel length and h.sub.i, for 0iL1, represents complex baseband channel coefficient of (i+1).sup.th path, which is assumed to be zero mean circular symmetric complex Gaussian whereby received vector of length N.sub.CP+NM+L1 (for N.sub.cpL) is given by,
Z.sub.cp=h*X.sub.cp+v.sub.cp where v.sub.cp is AWGN vector of length MN+N.sub.CP+L1 with elemental variance 6.sup.2v.

6. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 1, wherein the low complex GFDM receiver includes two staged receiver or Joint-MMSE Receiver whereby, the data obtained from the received GFDM data signal by involving channel equalization followed by self-interference equalization.

7. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 6, wherein the two-staged receiver includes two staged receiver includes: M-point FFT operator for grouping channel equalized received GFDM data signal according to sub-carrier numbers followed by: converting samples of each group into frequency domain using the M-point FFT operator; multiplying the converted samples with pre-computed weights by multiplier means; and converting back the multiplied samples into time domain by using M-point IFFT operator; means for shuffling physical connections in the M-point IFFT operator for regrouping the converted samples according to time slots followed by converting samples of each group into frequency domain using N-point FFT operator; and multiplying the converted samples with pre-computed weights by multiplier means to obtain self-interference equalized data signal.

8. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 6, wherein the joint-MMSE receiver includes: MN-point FFT operator to convert the equalized received GFDM data signal into frequency domain; means to multiply the received signal in frequency domain with complex valued channel information-based weights and then converting back to time domain using MN-point IFFT operator; means for reshuffling the physical connections in the MN-point IFFT operator for grouping the time domain converted samples according to subcarrier number followed by: converting the samples of each group into frequency domain using the M-point FFT operator; and multiplying the converted samples with pre-computed weights; means for reshuffling the physical connections in the M-point FFT operator for regrouping the converted samples according to time slots followed by converting samples of each group into frequency domain using the N-point FFT operator; processing the converted samples following Algorithm 1; means for reshuffling the physical connections in the N-point FFT operator for regrouping the processed samples according to sub-carrier number followed by: converting the samples of each group to time domain using the M-point IFFT operator; and multiplying the converted samples with complex weights computed using Algorithm 2; and means for reshuffling the physical connections in the M-point IFFT operator for regrouping the multiplied samples according to time slots to obtain equalized MN-point samples.

9. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 8, wherein the Algorithm 1 enabling development of the low complexity receiver structure including low complexity multiplication to obtain =E using Taylor Series expansion.

10. The generalized frequency division multiplexing (GFDM) transceiver system as claimed in claim 9, wherein the Algorithm 2 enabling development of the low complexity receiver structure including low complexity multiplication to obtain =E using CG method.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0051] FIG. 1 shows a wireless communication system involving GFDM based transceiver in accordance with an embodiment of the present invention.

[0052] FIG. 2 shows baseband block diagram of GFDM Transceiver in multipath fading channel with two staged receiver in accordance with an embodiment of the present invention.

[0053] FIG. 3 shows low complexity implementation of GFDM transmitter in accordance with an embodiment of the present invention.

[0054] FIG. 4 shows low complexity implementation of GFDM receiver in multipath fading channel in accordance with an embodiment of the present invention.

[0055] FIGS. 5A to 5D show ratio of power in constituent matrices of .sub.u i.e.

[00003]$\frac{{.Math.{}_u.Math.}_F}{{.Math.Z_u.Math.}_F}$

averaged over h and u[0 M1] i.e. (in dB). (Raised Cosine (RC) pulse shape is considered).

[0056] FIG. 6 shows implementation of present low-complexity joint-MMSE GFDM receiver in accordance with an embodiment of the present invention.

[0057] FIG. 7 shows BER Performance of proposed GFDM transceiver and direct implementation GFDM two stage transceiver for 16 QAM modulation in fading channel. N=128, M=8.

[0058] FIGS. 8A and 8B show BER Performance of proposed GFDM transceiver for (a) Roll off factor 0.3 and (b) Roll off factor 0.9.

DETAILED DESCRIPTION

[0059] In this specification, following notations are used. Vectors are represented by bold small letters (x), matrices are represented by bold capital letters (X) and scalars are represented as normal small letters (x). I.sub.N represents identity matrix with order N and j={square root over (1)}. W.sub.L represents L-order normalized IDFT matrix. Kronecker product operator is given by .Math.. diag{.} is a diagonal matrix whose diagonal elements are formed by the elements of the vector inside or diagonal elements of the matrix inside. circ{.} is a circulant matrix whose first column is given by the vector inside. E.sub.h{.} is expectation of expression inside with respect to random vector h. The round-down operator *. , rounds the value inside to the nearest integer towards minus infinity. The superscripts (.).sup.T and (.).sup.H indicate transpose and conjugate transpose operations, respectively.|.| operator computes absolute value of elements inside. trace{.} computes the trace of matrix inside. . is Frobenous norm of matrix inside. FFT.sub.(.) and IFFT.sub.(.) denote (.)-point FFT and IFFT respectively.

GFDM Transceiver System:

[0060] The accompanying FIG. 1 shows a block representation of the wireless communication system involving GFDM based transceiver in accordance with an embodiment of the present invention. As shown in the referred figure, the present transceiver system includes GFDM transmitter, wireless channel and GFDM receiver.

Transmitter:

[0061] The present invention discloses a GFDM system with N sub-carriers and M timeslots. The MN length prototype filter is g(n), n=0, 1, . . . , MN1. QAM modulated data symbol is d.sub.m,kC,m=0, 1, . . . , M1, k=0, 1, . . . , N1. It is assumed that data symbols are independent and identical i.e. E[d.sub.m,kd.sub.m,k*]=.sub.d.sup.2.sub.m-m,k-k. The transmitted GFDM signal can be written as,

[00004]$\begin{array}{cc}x\left[n\right]=\frac{1}{\sqrt{N}}.Math.\underset{m=0}{\overset{M-1}{.Math.}}.Math..Math.\underset{k=0}{\overset{N-1}{.Math.}}.Math..Math.d_{m,k}.Math.{g\left[n-\mathrm{mN}\right]}_{\mathrm{MN}}.Math.e^{\frac{j.Math..Math.2.Math..Math..Math..Math.\mathrm{nk}}{N}}.& \left(1\right)\end{array}$

[0062] The transmitted signal can also be written as,

x=A.sub.MNMNd.sub.MN1,(2)

[0063] where d=[d.sub.0 d.sub.1 . . . d.sub.M-1].sup.T is the data vector, where d.sub.m=[d.sub.m,0 d.sub.m,1 . . . d.sub.m,N-1].sup.T, where, m=0, 1 . . . M1, is the N length data vector for m.sup.th time slot and A is the modulation matrix which can be given as,

A=[g M.sub.1g . . . M.sub.N-1g|T.sub.1g T.sub.1M.sub.1g . . . T.sub.1M.sub.N-1g| . . . |T.sub.M-1M.sub.1g . . . T.sub.M-1M.sub.N-1g],(3)

[0064] where, g=[g[0] g[1] . . . g[MN1]].sup.T is MN length vector which holds the prototype filter coefficients,

[00005]$M_l.Math.g\left[n\right]=g\left[n\right].Math.e^{\frac{j.Math..Math.2.Math..Math..Math..Math.\ln }{N}}$

is the modulation operator and T.sub.r=g(nrN).sub.MN is the cyclic shift operator.

[0065] CP of length N.sub.CP is prepended to x. After adding CP, transmitted vector, x.sub.cp, can be given as,

x.sub.cp=[x(MNN.sub.cp+1:MN);x].(4)

Channel:

[0066] Let, h=[h.sub.0, h.sub.1, . . . h.sub.L-1].sup.T be L length channel impulse response vector, where, h.sub.i, for 0iL1, represents the complex baseband channel coefficient of (i+1).sup.th path [27], which is assumed to be zero mean circular symmetric complex Gaussian (ZMCSC). It is also assumed that channel coefficients related to different paths are uncorrelated. It is considered, N.sub.cpL. Received vector of length N.sub.cp+NM+L1 is given by,

z.sub.cp=h*x.sub.cp+v.sub.cp,(5)

where v.sub.cp is AWGN vector of length MN+N.sub.cp+L1 with elemental variance .sub.v.sup.2.

Receiver:

[0067] The first N.sub.cp samples and last L1 samples of y.sub.cp are removed at the receiver i.e. y=[y.sub.cp(N.sub.cp+1: N.sub.cp+MN)]. Use of cyclic prefix converts linear channel convolution to circular channel convolution when N.sub.cpL. The MN length received vector after removal of CP can be written as,

z=HAd+v,(6)

[0068] where H is circulant convolution matrix of size MNMN and v is WGN vector of length MN with elemental variance .sub.v.sup.2. Since H is a circulant matrix, y can be further written as,

z=W.sub.MNW.sub.MN.sup.HAd+v,(7)

[0069] where, A=diag{{tilde over (h)}(0), {tilde over (h)}(1) . . . {tilde over (h)}(MN1)} is a diagonal channel frequency coefficients matrix whose r.sup.th coefficient can be given as,

[00006]$\tilde{h}\left(r\right)=\underset{s=0}{\overset{L-1}{.Math.}}.Math..Math.h\left(s\right).Math.e^{\frac{j.Math..Math.2.Math..Math.s.Math..Math.r}{\mathrm{MN}}}$

where, r=0,1 . . . MN1.

[0070] In this invention, two stage as well as one stage receiver is considered.

[0071] Two Stage Receiver.

[0072] For two stage receiver, channel equalized vector, y, can be given as,

[00007]$\begin{array}{cc}y=W_{\mathrm{MN}}.Math.{}_{\mathrm{eq}}.Math.W_{\mathrm{MN}}^H.Math.z=\mathrm{aAd}+b+v,& \left(8\right)\\ \mathrm{where},.Math.{}_{\mathrm{eq}}=\{\begin{array}{cc}.Math.{}^{-1}& \mathrm{for}.Math..Math.\mathrm{ZF}.Math..Math.\mathrm{FDE}\\ {\left[{}^H.Math.+\frac{{}_v^2}{{}_d^2}.Math.I_{\mathrm{MN}}\right]}^{-1}& {}^H.Math.\mathrm{for}.Math..Math.\mathrm{MMSE}.Math..Math.\mathrm{FDE}\end{array}& \\ \mathrm{where},.Math.v=W_{\mathrm{MN}}.Math.{}_{\mathrm{eq}}.Math.W_{\mathrm{MN}}^H.Math.v,& \\ a=\{\frac{1}{\mathrm{MN}}.Math.\underset{r=0}{\overset{\mathrm{MN}-1}{.Math.}}.Math..Math.\frac{\begin{array}{c}1.Math..Math.\mathrm{for}.Math..Math.\mathrm{ZF}-\mathrm{FDE}\\ \tilde{h}\left(r\right).Math.{}^2\end{array}}{\tilde{h}\left(r\right).Math.{}^2.Math.+\frac{{}_v^2}{{}_d^2}}.Math.\mathrm{for}.Math..Math.\mathrm{MMSE}.Math..Math.\mathrm{FDE},& \end{array}$

[0073] b is residual interference, given in (9) and v=W.sub.MN.sub.eqW.sub.MN.sup.Hv is post-processing noise.

[00008]$\begin{array}{cc}b=\{\begin{array}{c}0.Math..Math.\mathrm{for}.Math..Math.\mathrm{ZF}.Math..Math.\mathrm{FDE}\\ \mathrm{Ad}.Math..Math.\mathrm{for}.Math..Math.\mathrm{MMSE}.Math..Math.\mathrm{FDE}\end{array}& \left(9\right)\end{array}$

[0074] Channel equalized vector, y, is further equalized to remove the effect of self-interference. Estimated data, d, can be given as,

d=A.sub.eqy,(10)

[0075] where, A.sub.eq is GFDM equalization matrix which can be given as,

[00009]$A_{\mathrm{eq}}=\{\begin{array}{c}{A}^H.Math..Math.\mathrm{for}.Math..Math.\mathrm{MF}.Math..Math.\mathrm{Equalizer}\\ A^{-1}.Math..Math.\mathrm{for}.Math..Math.\mathrm{ZF}.Math..Math.\mathrm{Equalizer}\\ {\left[R_v+A^H.Math.A\right]}^{-1}.Math.A^H\\ \mathrm{for}.Math..Math.\mathrm{biased}.Math..Math.\mathrm{MMSE}.Math..Math.\mathrm{Equalizer}\\ {}^{}.Math.{{\mathrm{gfdm}}^{-1}\left[R_v+A^H.Math.A\right]}^{-1}.Math.A^H\\ \mathrm{for}.Math..Math.\mathrm{unbiased}.Math..Math.\mathrm{MMSE}.Math..Math.\mathrm{Equalizer},\end{array}$

[0076] for unbiased MMSE Equalizer, where, R.sub.v=E[vv.sup.H] is noise correlation matrix after channel equalization. In the case of AWGN,

[00010]$R_v=\frac{{}_v^2}{{}_d^2}.Math.I.$

For multipath fading channel, R.sub.v is a full matrix since the noise after channel equalization is colored. .sub.gfdm.sup.1 is a diagonal bias correction matrix for GFDM-MMSE equalizer, where,

[00011]${}_{\mathrm{gfdm}}=\mathrm{diag}.Math.\left\{{\left[\frac{{}_v^2}{{}_d^2}.Math.I+A^H.Math.A\right]}^{-1}.Math.A^H.Math.A\right\}.$

One-Stage Receiver (Joint-MMSE Receiver):

[0077] Joint-MMSE equalizer can be of two types, namely, (1) biased-Joint MMSE and (2) unbiased-Joint-MMSE. Equalized data symbol vector, d.sub.JP, can be given as, d.sub.JP=B.sub.eqy, where, B.sub.eq is Joint-MMSE equalizer matrix and can be given as,

[00012]$\begin{array}{cc}{B}_{\mathrm{eq}}=\{\begin{array}{c}{\left[\frac{{}_v^2}{{}_d^2}.Math.I+{\left(\mathrm{HA}\right)}^H.Math.\mathrm{HA}\right]}^{-1}.Math.{\left(\mathrm{HA}\right)}^H.Math..Math.\mathrm{for}.Math..Math.\mathrm{biased}.Math..Math.\mathrm{Joint}-\mathrm{MMSE}.Math..Math.\mathrm{Equalizer}\\ {{}^{-1}\left[\frac{{}_v^2}{{}_d^2}.Math.I+{\left(\mathrm{HA}\right)}^H.Math.\mathrm{HA}\right]}^{-1}.Math.\begin{array}{c}{\left(\mathrm{HA}\right)}^H.Math..Math.\mathrm{for}.Math..Math.\mathrm{unbiased}.Math..Math.\mathrm{Joint}-\\ \mathrm{MMSE}.Math..Math.\mathrm{Equalizer}\end{array},\end{array}& \left(12\right)\end{array}$

[0078] where, .sup.1 is diagonal bias correction matrix for joint-processing, where,

[00013]$=\mathrm{diag}.Math.\left\{{\left[\frac{{}_v^2}{{}_d^2}.Math.I+{\left(\mathrm{HA}\right)}^H.Math.\mathrm{HA}\right]}^{-1}.Math.{\left(\mathrm{HA}\right)}^H.Math.\mathrm{HA}\right\}.$

Low Complexity GFDM Transmitter:

[0079] In this section, low complex GFDM transmitter is presented. A matrix is factorized into special matrices to obtained low complexity transmitter without incurring any assumptions related to GFDM parameters. The stepwise operation of the GFDM transmitter is provided hereunder:

[0080] Step 1: Complex valued data symbols corresponding to a particular timeslot and different sub-carriers are modulated to corresponding sub-carrier frequencies using N-point IFFT operation.

[0081] Step 2: Modulated data symbols in step 1 are grouped according to sub-carrier numbers, whereby, in each group,

[0082] step a: Samples are converted into frequency domain using M-point FFT.

[0083] step b: Samples computed in 2(a) are multiplied with a precomputed weight.

[0084] step c: Samples in 2(b) are converted back into time domain by using M-point IFFT.

[0085] Step 3: Samples obtained after step 2 are regrouped according to time slots.

[0086] Signal obtained after step 3 is GFDM transmitted signal.

[0087] In the following subsections, the design and implementation of the transmitter is explained.

Low Complexity Transmitter Design

[0088] The GFDM modulation matrix A can be given as,

A=P.sup.TU.sub.MDU.sub.M.sup.HPU.sub.N,(13)

[0089] where, D=diag{(0), (1) . . . (MN1)} is diagonal matrix, whose r.sup.th element can be given as,

[00014]$\begin{array}{cc}\stackrel{\_}{}\left(r\right)=\underset{m=0}{\overset{M-1}{.Math.}}.Math..Math.g\left(\mathrm{mN}+.Math.\frac{r}{M}.Math.\right).Math.{}^{m\left(r.Math..Math.\mathrm{mod}.Math..Math.M\right)}.& \left(14\right)\end{array}$

[0090] and P is a subset of perfect shuffle permutation matrix, which can be defined as, P=[p.sub.l,q]0l,qMN1, where the matrix element p.sub.l,q can be given as,

[00015]$\begin{array}{cc}{P}_{l,q}=\{\begin{array}{c}1.Math..Math.\mathrm{if}.Math..Math.q=\mathrm{lN}+.Math.\frac{1}{M}.Math.\\ 0.Math..Math.\mathrm{otherwise}\end{array}.& \left(15\right)\end{array}$

[0091] GFDM transmitted signal, x can be given as,

x=P.sup.TU.sub.MDU.sub.M.sup.HPU.sub.Nd.(16)

[0092] Lemma 1 Let =[(0) (1) . . . (MN1)].sup.T be a MN length complex valued vector. The vector, {tilde over ()}=P=[(0) (1) . . . {tilde over ()}(MN1)].sup.T. The i.sup.th element of the vector can be given as,

[00016]$\begin{array}{cc}\stackrel{~}{}\left(i\right)=\left(\left(i.Math..Math.\mathrm{mod}.Math..Math.M\right).Math.N+.Math.\frac{i}{M}.Math.\right),0i\mathrm{MN}-1.& \left(17\right)\end{array}$

[0093] The vector, =P.sup.T=[(0) (1) . . . (MN1)].sup.T. The i.sup.th element of the vector can be given as,

[00017]$\begin{array}{cc}\stackrel{\_}{}\left(i\right)=\left(\left(i.Math..Math.\mathrm{mod}.Math..Math.N\right).Math.M+.Math.\frac{i}{N}.Math.\right),0i\mathrm{MN}-1.& \left(18\right)\end{array}$

Low Complexity Transmitter Implementation:

[0094] The low complexity transmitter can be obtained using Corollary 1 and Lemma 2. FIG. 3 presents low complexity transmitter implementation.

[0095] The vector e=U.sub.Nd, can be obtained by M, N point IFFT. The vector {tilde over (e)}=Pe can be obtained by shuffling the vector e using (17). The vector c=U.sup.H.sub.M{tilde over (e)} can be obtained using N, M-point FFT's. Using (13), the matrix D, can be pre-computed at the transmitter. The MN length vector, z=Dc, can be obtained by MN-point complex multiplication. The MN length vector, {tilde over (x)}=U.sub.MZ can be implemented using N, M-point IFFT. Finally, the transmitted signal, x=P.sup.T{tilde over (x)} can be obtained by shuffling {tilde over (x)} according to (18).

Low Complexity Two Stage GFDM Receiver:

[0096] In this section, the low complexity linear GFDM receivers is disclosed i.e. (1) MF (2) ZF and (3) Biased MMSE and (4) Unbiased MMSE. The stepwise operations of the GFDM receivers are provided hereunder:

[0097] step 1: Channel equalized samples are grouped according to sub-carrier numbers. A total N groups are made having M samples and for each group,

[0098] step 1a: Samples in a group are converted into frequency domain using M-point FFT;

[0099] step 1b: Samples computed in 1a is multiplied with pre-computed weights.

[0100] step 1c: Samples in 1(b) are converted back into time domain by using M-point IFFT.

[0101] step 2: samples obtained after step 1 are regrouped according to time slots. A total M groups are made having N samples and for each group

[0102] step 2a: Samples are converted into frequency domain using N-point FFT;

[0103] step 2b: Samples computed in 2a are multiplied with pre-computed weights.

[0104] Signal obtained after step 2 is self-interference equalized signal.

Low Complexity GFDM Receiver Design

[0105] Receiver in AWGN channel is self-interference equalization. For multipath fading channel, channel equalization is followed by self-interference equalization. Theorem 1 relates to unified low complexity GFDM linear self-interference equalizers. Corollary 1 gives unified implementation of GFDM receivers in AWGN as well as multipath fading channel.

[0106] Theorem 1 GFDM equalization matrix A.sub.eq can be written in a unified manner as,

A.sub.eq=U.sub.N.sup.HP.sup.TU.sub.MD.sub.eqU.sub.M.sup.HP,(19)

[0107] where, D.sub.eq is a diagonal MN-order matrix, which can be given as,

[00018]$D_{\mathrm{eq}}=\{\begin{array}{c}{D}^H.Math..Math.\mathrm{for}.Math..Math.\mathrm{MF}.Math..Math.\mathrm{Equalizer}\\ D^{-1}.Math..Math.\mathrm{for}.Math..Math.\mathrm{ZF}.Math..Math.\mathrm{Equalizer}\\ {\left[R_v+D^H.Math.D\right]}^{-1}.Math.D^H\\ \mathrm{for}.Math..Math.\mathrm{biased}.Math..Math.\mathrm{MMSE}.Math..Math.\mathrm{Equalizer}\\ {\left[R_v+D^H.Math.D\right]}^{-1}.Math.D^H\\ \mathrm{for}.Math..Math.\mathrm{unbiased}.Math..Math.\mathrm{MMSE}.Math..Math.\mathrm{Equalizer},\end{array}$

[0108] and =.sub.gfdm.sup.1 for unbiased MMSE and =I.sub.MN for other equalizers. Further, .sub.gfdm can be given as,

[00019]${}_{\mathrm{gfdm}}=\frac{1}{\mathrm{MN}}.Math.\underset{r=0}{\overset{\mathrm{MN}-1}{.Math.}}.Math..Math.\frac{{.Math.{}_r.Math.}^2}{{.Math.{}_r.Math.}^2+\frac{{}_v^2}{{}_d^2}}.Math.I_{\mathrm{MN}}.$

[0109] Corollary 1 The estimated data, d, can be given as,

[00020]$\begin{array}{cc}d=\{\begin{array}{c}.Math..Math.U_N^H.Math.P^T.Math.U_M.Math.D_{\mathrm{eq}}.Math.U_M^H.Math.\mathrm{Pz}.Math..Math.\mathrm{for}.Math..Math.\mathrm{AWGN}.Math..Math.\mathrm{Channel}\\ .Math..Math.U_N^H.Math.P^T.Math.U_M.Math.D_{\mathrm{eq}}.Math.U_M^H.Math.{\mathrm{PW}}_{\mathrm{MN}}.Math.{}_{\mathrm{eq}}.Math.W_{\mathrm{MN}}^H.Math.z\\ \mathrm{for}.Math..Math.\mathrm{Multipath}.Math..Math.\mathrm{Fading}.Math..Math.\mathrm{Channel}\end{array}& \left(20\right)\end{array}$

Low Complexity Receiver Implementation:

[0110] The low complexity structure of GFDM self-interference cancellation can be obtained by using Corollary 1 and Lemma 1.

[0111] Channel Equalization.

[0112] To implement y.sup.1=.sub.eqW.sub.MN.sup.Hz, MN-point FFT of z is multiplied with .sub.eq. Finally, MN-point IFFT of y.sup.1 is taken to implement y=W.sub.MNy.sup.1.

Self-interference Equalization.

[0113] The vector y=Py, can be obtained by shuffling the y vector using (16). The MN1 vector =U.sub.M.sup.Hy can be implemented by using N, M-point IFFT's. The vector is then multiplied to the diagonal matrix D.sub.eq to obtain . The vector =U.sub.M can be implemented using N, M-point FFTs. The vector, {tilde over ()}=P.sup.T, can be implemented by shuffling the vector using (17). Now, the vector, d=U.sub.N{tilde over ()} can be implemented using M, N-point FFTs. Finally, d=d can be obtained by using MN-point multiplier.

Low complexity Joint-MMSE Receiver:

[0114] Step 1: Received signal is converted into frequency domain using M N-point FFT.

[0115] Step 2: Received signal in frequency domain is multiplied with complex valued channel information-based weights and then converted back to time domain using MN-point IFFT.

[0116] Step 3: Samples obtained in step 2 are grouped according to subcarrier number. N such groups are formed having M samples each and for each group

[0117] Step 3a: Samples are converted into frequency domain using M-point FFT and multiplied with pre-computed weights;

[0118] Step 4 Samples obtained in step 3a are regrouped according to time slot number. M such groups are formed having N samples and for each group

[0119] Step 4a: Samples are converted into frequency domain using N-point FFT.

[0120] Step 4b: Samples obtained in step 4a are processed as explained in Algorithm 1 or Algorithm 2.

[0121] Step 5 Samples obtained after step 4 are regrouped according to sub-carrier number. N such groups are formed having M samples each and for each group, repeat

[0122] Step 5a Samples are converted back to time domain using M-point IFFT and multiplied with complex weights which are computed using Algorithm 3.

[0123] Step 6 Samples obtained after step 5 are regrouped according to time slots to obtain equalized MN-point samples.

Joint MMSE Low Complexity Receiver Design

[0124] Theorem 1 The estimated data vector for joint-MMSE receiver, d, can be given as,

[00021]$\begin{array}{cc}\begin{array}{c}d={}_{J.Math..Math.P}^{-1}.Math.F_b.Math.{\mathrm{EU}}_N^H.Math.D^H.Math.F_b^H.Math.W_{\mathrm{MN}}.Math.{}^H.Math.W_{\mathrm{MN}}^H.Math.y\\ ={}_{J.Math..Math.P}^{-1}.Math.P^T.Math.U_M.Math.{\mathrm{PEU}}_N^H.Math.D^H.Math.P^T.Math.U_M^H.Math.{\mathrm{PW}}_{\mathrm{MN}}.Math.{}^H.Math.W_{\mathrm{MN}}^H.Math.y\left(\mathrm{UsingLemma}.Math..Math.1\right)\end{array}& \left(21\right)\end{array}$

[0125] where, E=diag{E.sub.0, E.sub.1, . . . E.sub.M-1}, is a MNMN size block diagonal matrix with blocks of size NN. A block of E,

[00022]$E_u={\left[\frac{{}_v^2}{{}_d^2}.Math.I+C_u\right]}^{-1}$

where, 0uM1, of size NN, where, .sub.JP= for unbiased receiver and I.sub.MN for biased MMSE receiver. Further, C.sub.u can be given as,

C.sub.u=L.sub.u.sup.H.sub.uL.sub.u, 0uM1,(22)

[0126] where, L.sub.u is a circulant NN matrix which can be represented in terms of its first column as, L.sub.u=circ{l.sub.u(0), l.sub.u(1), . . . l.sub.u(N1)}, where, p.sup.th element of the first column can be given as,

[00023]$l_u\left(p\right)=\frac{1}{N}.Math.\underset{r=0}{\overset{N-1}{.Math.}}.Math..Math.\left(\mathrm{uN}+r\right).Math.e^{\frac{j.Math..Math.2.Math..Math.\left(-p\right).Math.r}{N}}.Math.e^{\frac{-j.Math..Math.2.Math..Math..Math..Math.\mathrm{ur}}{\mathrm{MN}}},$

0uM1, and, .sub.u is a NN diagonal matrix which can be given as,

.sub.u=diag{{tilde over (h)}(u)|.sup.2,|{tilde over (h)}(M+u)|.sup.2 . . . |{tilde over (h)}((N1)M+u)|.sup.2},0uM1.(23)

[0127] Further, can be given as,

=I.sub.M.Math.S,(24)

[0128] where,

[00024]$S=\frac{1}{M}.Math.\underset{u=0}{\overset{M-1}{.Math.}}.Math..Math.\mathrm{diag}.Math.\left\{{\tilde{E}}_u\right\},$

where,

[00025]${\tilde{E}}_u={\left[\frac{{}_v^2}{{}_d^2}.Math.I_N+C_u\right]}^{-1}.Math.C_u$

is a NN matrix, 0uM1.
Proof. The theorem can be proved using the fact that (H A).sup.H H A is block circulant matrix with blocks of size NN.

[0129] The implementation of joint-MMSE receiver based on (15), requires the inversion of M number of, NN size

[00026]$\left[\frac{{}_v^2}{{}_d^2}.Math.I+C_u\right]$

matrices. The direct implementation of this inversion requires O(MN.sup.3). To see the possibility of further reduction in complexity, E.sub.u is factorized for 0uM1,
Factorization of E.sub.u for 0uM1:

[0130] Using (16), E.sub.u can be written as,

[00027]$E_u-{\left[\frac{{}_v^2}{{}_d^2}.Math.I+L_u^H.Math.{}_u.Math.L_u\right]}^{-1}={L_u^{-1}\left[{}_u+\frac{{}_v^2}{{}_d^2}.Math.{\left(L_u.Math.L_u^H\right)}^{-1}\right]}^{-1}.Math.{\left(L_u^H\right)}^{-1}.$

[0131] Since L.sub.u is a circulant matrix, it can be further factorized as,

L.sub.u=W.sub.NR.sub.uW.sub.N.sup.H,

[0132] where, R.sub.u is a diagonal matrix of order N, which can be computed as, R.sub.u=W.sub.N.sup.HL.sub.uW.sub.N. Using this, (19) can be written as,

E.sub.u=W.sub.NR.sub.u.sup.1W.sub.N.sup.H.sub.uW.sub.N(R.sub.u.sup.H).sup.1W.sub.N.sup.H,

[0133] where,

[00028]${}_u={}_u^{-1}={\left[{}_u+\frac{{}_v^2}{{}_d^2}.Math.{\left(L_u.Math.L_u^H\right)}^{-1}\right]}^{-1}.$

Using the properties of circulant matrices, it can be shown that L.sub.uL.sub.u.sup.H is a circulant matrix because L.sub.u is a circulant matrix. Further, L.sub.uL.sub.u.sup.H=W.sub.N|R.sub.u|.sup.2W.sub.N.sup.H. Using this, (L.sub.uL.sub.u.sup.H).sup.1=W.sub.N|R.sub.u|.sup.2W.sub.N.sup.H. Hence, .sub.u is a circulant-plus-diagonal matrix. It can be easily seen that elements of .sub.u and |R.sub.u|.sup.2 are positive. It can be concluded that .sub.u is a positive definite matrix too.

[0134] Since .sub.u is a positive definite matrix, inversion of .sub.u can be computed using Conjugate Gradient (CG) algorithm. CG algorithm gives exact solution in N iterations. Hence a direct implementation of joint-MMSE receiver can be obtained using CG method. In each iteration, a matrix-vector multiplication is required. In our case this matrix is a circulant-plus-diagonal matrix. Using the properties of circulant matrix, matrix-vector multiplication can be implemented using N-point FFT and IFFT. Thus, direct implementation of joint-MMSE receiver requires O(MN.sup.2 log.sub.2N) computations.

[0135] It has been showed that to implement the receiver, the most computationally complex operation is to invert N-order .sub.u matrix, 0uM1. First the structure of .sub.u matrix is investigated. The low complexity bias correction is also investigated.

[0136] Structure of .sub.u matrix, 0uM1:

[0137] Using the properties of circulant matrices, it can be shown that diagonal values of (L.sub.uL.sub.u.sup.H).sup.1 are equal and can be given as, diag{(L.sub.uL.sub.u.sup.H).sup.1}=.sub.uI.sub.N, where, .sub.u can be given as,

[00029]${}_u=\frac{\underset{r=0}{\overset{N-1}{.Math.}}.Math..Math.{\left[{.Math.R_u.Math.}^{-2}\right]}_{r,r}}{N}.$

Next, two matrices are defined,

[00030]${}_u=\frac{{}_v^2}{{}_d^2}.Math.\left\{{\left(L_u.Math.L_u^H\right)}^{-1}-{}_u.Math.I_N\right\}$$\mathrm{and}$${}_u={}_u+\frac{{}_v^2}{{}_d^2}.Math.{}_u.Math.I_N.$

[0138] Using this, .sub.u can be written as, .sub.u=[.sub.u+.sub.u].

[0139] It can be seen that .sub.u is a diagonal matrix and .sub.u is a circulant matrix with zero diagonal values. Using the properties of circulant matrix, .sub.u.sup.2 can be given as,

[00031]${.Math.{}_u.Math.}^2={\left(\frac{{}_v^2}{{}_d^2}\right)}^2.Math.\mathrm{trace}.Math.\left\{R_u^2\right\}.$

E[.sub.u.sup.2] can be approximated as,

[00032]$E\left[{.Math.{}_u.Math.}^2\right]N\left({\left(1+\frac{{}_v^2}{{}_d^2}.Math.{}_u\right)}^2+1\right).$

It is observed that E[.sub.u.sup.2] is approximately independent of assumed channel power delay profile.

[0140] Next, the ratio of power in .sub.u to the power in .sub.u is analyzed. To do so, =

[00033]$\frac{1}{M}.Math.\underset{u=0}{\overset{M-1}{.Math.}}.Math..Math.E_h\left[\frac{.Math.{}_n.Math.}{.Math.{}_u.Math.}\right]$

is defined. Urban micro (UMi) channel is taken and Monte-Carlo simulations are persomed to compute

[00034]$E_h\left[\frac{.Math.{}_u.Math.}{.Math.{}_u.Math.}\right].$

It is further averaged over u to obtain . Plot of versus SNR is given in FIG. 5A for N{32,128,512}, M=3 and ROF{(0.1,0.9}. It is observed that decreases with SNR and increases with ROF. For same SNR and ROF value, it decreases with increasing value of N. FIG. 5B plots against SNR for M{3,11,21}, N=16 and ROF{(0.1,0.9}. It can be seen that decreases with SNR and increases with ROF value. For low ROF values, is decreases with M and for high ROF values, it increases with M. To see the effect of h, three different channel lengths of following uniform power delay profile (PDP) is considered. The value of is plotted against SNR in FIG. 5C for channel lengths L=[16,32,64] representing different rms delay spread .sub.rms[1,1.8,3.7] s respectively. It can be observed that is same for different channel lengths. Hence, it can be concluded that power in .sub.u is much higher than the power in .sub.u irrespective of channel propagation conditions.

[00035]$=E_h\left[\frac{.Math.{}_n.Math.}{.Math.{}_u.Math.}\right]$

is defined. In FIG. 5D, the value of against u[0M1] and ROF=0.3,0.5,0.9 for N=64, M=5,15 and SNR=10 dB is plotted. It is observed that value of is highest for middle two values of u. In case of M=15 and ROF=0.9, the power in .sub.u for u=7,8 is comparable to the power in .sub.u.
Taylor Series Method for Computing .sub.u=.sub.u, 0uM1:

[0141] It can be concluded from the previous section that the power in .sub.u is much higher than the power in .sub.u when both M and ROF have value. Therefore, Taylor series expansion of .sub.u can be used for other situations. In such cases, .sub.u.sub.u.sup.1.sub.u.sub.u.sub.u.sup.1+.sub.u.sup.1.sub.u.sub.u.sup.1.sub.u.sub.u.sup.1+ . . . . Also, using (19) and (21), the following expression can be reached

[0142] .sub.u=W.sub.NR.sub.uW.sub.N.sup.H, where

[00036]$R_u=\frac{{}_v^2}{{}_d^2}.Math.{.Math.R_u.Math.}^{-2}-{}_i.Math.I_N.$

[0143] Therefore,

.sub.u.sub.u.sup.1[I.sub.NW.sub.NR.sub.uW.sub.N.sup.H.sub.u.sup.1+W.sub.NR.sub.uW.sub.N.sup.H.sub.u.sup.1W.sub.NR.sub.uW.sub.N.sup.H.sub.u.sup.1+ . . . ].(25)

Algorithm to Multiply E in (14) with Complex Valued Vector Using Taylor Series Method:

[0144] =U.sub.N.sup.HD.sup.HF.sub.b.sup.HW.sub.MN.sup.HW.sub.MN.sup.Hy, is considered to be an intermediate MN length complex valued vector in (14). Now to compute =E, another intermediate vector in (14), E can computed by putting (23) in (20). Algorithm 1 discuss a low complexity algorithm to multiply =E using Taylor Series expansion. This algorithm is used to develop low complexity receiver structure. Matrices vectors and scalars which are only related GFDM parameters (do not depend on the channel) can be precomputed at the receiver. Hence, it is assumed that, the knowledge of R.sub.u, |R.sub.u|.sup.2, (R.sub.u.sup.H).sup.1, R.sub.u.sup.1, R.sub.u, .sub.u for 0uM1. Also, it is assumed that the knowledge of

[00037]$\frac{{}_v^2}{{}_d^2}$

which can be computed at the receiver and which is known from the channel estimation. K.sub.T+1 is the number of terms in (24) i.e. the number of iterations for step 7 to step 9 is K.sub.T.

TABLE-US-00001 Algorithm 1 Computation of = E Using Taylor Series Method 1: [00038]$\mathrm{Precomputed}:R_u,{.Math.R_u.Math.}^{-2},{\left(R_u^H\right)}^{-1},R_u^{-1},{\tilde{R}}_u,{\mathrm{.Math.}}_u,\frac{{}_v^2}{{}_d^2}.Math..Math.\mathrm{and}$ for 0 u M 1 2: for u = 0 : M 1 do 3: Compute : .sub.u using (16) 4: [00039]$\mathrm{Compute}:{}_u={}_u+\frac{{}_v^2}{{}_d^2}.Math.{\mathrm{.Math.}}_u.Math.I_N$ 5: Compute : = W.sub.N(R.sub.u.sup.H).sup.1W.sub.N.sup.H.sub.u 6: Take : t.sup.(0) = 7: for k = 1 : K.sub.T do 8: t.sup.(k) = W.sub.N{tilde over (R)}.sub.uW.sub.N.sup.H .sub.u.sup.1t.sup.(k1) 9: end for 10: [00040]$\mathrm{Compute}:{}_u^{\left(1\right)}={}_u^{-1}.Math.\underset{k=0}{\overset{K_T}{.Math.}}.Math.t^{\left(k\right)}$ 11: Compute : .sub.u = W.sub.NR.sub.u.sup.1W.sub.N.sup.H .sub.u.sup.(1) 12: end for 13: = [ .sub.0 .sub.1 . . . .sub.M1].sup.T 14: return
Low Iteration Conjugate Gradient (CG) Method for Computing .sub.u=.sub.u.sup.1, 0uM1

[0145] It is established earlier, that .sub.u is a positive definite matrix. So, .sub.u.sub.u=.sub.u can be computed using CG algorithm. CG algorithm gives exact solution in N iterations. To reduce the complexity further Jacobi precoded CG method is used i.e. the system .sub.u.sup.1.sub.u=.sub.u.sup.1.sub.U is solved. Thus, [I.sub.N+.sub.u].sub.u=.sub.u.sup.1.sub.u is solved using CG method. Different iteration count is also used for different values of u since

[00041]$\frac{.Math.{}_u.Math.}{.Math.{}_u.Math.}$

changes with u (as illustrated in FIG. 5D). When

[00042]$\frac{.Math.{}_u.Math.}{.Math.{}_u.Math.}$

is small, number of iterations can be kept small. When

[00043]$\frac{.Math.{}_u.Math.}{.Math.{}_u.Math.}$

is large, number of iteration is made large to obtain low errors.
Algorithm to Multiply E in (14) with Complex Valued Vector Using CG Method:

[0146] Algorithm 2 discuss a low complexity algorithm to multiply =E using CG method. This algorithm is used to develop low complexity receiver structure. k.sub.C is considered to be a M-length vector which holds iteration counts for different values of u. Same as in Algorithm 1, matrices, vectors and scalars which are only related GFDM parameters (do not depend on the channel) are assumed to be precomputed at the receiver.

TABLE-US-00002 Algorithm 2 Computation of = E using conjugate gradient (CG) method 1: [00044]$\mathrm{Precomputed}:R_u,{.Math.R_u.Math.}^{-2},{\left(R_u^H\right)}^{-1},R_u^{-1},{\tilde{R}}_u,{\mathrm{.Math.}}_u,\frac{{}_v^2}{{}_d^2}.Math..Math.\mathrm{and}$ for 0 u M 1 2: for u = 0 : M 1 do 3: Compute : .sub.u using (17) 4: [00045]$\mathrm{Compute}:{}_u={}_u+\frac{{}_v^2}{{}_d^2}.Math.{\mathrm{.Math.}}_u.Math.I_N$ 5: Compute : = W.sub.N(R.sub.u.sup.H).sup.1W.sub.N.sup.H.sub.u 6: Compute : .sup.(1) = .sub.u.sup.1 7: Take : w(0) = .sup.(1) 8: Compute : e(0) = .sup.(1) [I.sub.N + .sub.u.sup.1W.sub.N{tilde over (R)}.sub.uW.sub.N.sup.H]w(0) 9: Take : p(0) = e(0) 10: for k = 0 : k.sub.C(u) do 11: Compute : (k) = [I.sub.N + .sub.u.sup.1W.sub.N{tilde over (R)}.sub.uW.sub.N.sup.H]p(k) 12: [00046]$\mathrm{Compute}:\left(k\right)=\frac{e^H\left(k\right).Math.e\left(k\right)}{p^H\left(k\right).Math.\left(k\right)}$ 13: Compute : w(k + 1) = w(k) + (k)p(k) 14: Compute : e(k + 1) = e(k) (k)(k) 15: [00047]$\mathrm{Compute}:\left(k\right)=\frac{e^H\left(k.Math.\text{+}.Math.1\right).Math.e\left(k.Math.\text{+}.Math.1\right)}{e^H\left(k\right).Math.e\left(k\right)}$ 16: p(k + 1) = e(k + 1) + (k)p(k) 17: end for 18: Take : .sub.u.sup.(1) = w(k.sub.C(u)) 19: Compute : .sub.u = W.sub.NR.sub.u.sup.1W.sub.N.sup.H .sub.u.sup.(1) 20: end for 21: = [ .sub.0 .sub.1 . . . .sub.M1].sup.T 22: return

Approximation of Bias Correction Matrix

[0147] To compute , diag{{tilde over (E)}.sub.u}, 0uM1, is required (see (17)). Let, Q.sub.u=diag{C.sub.u} and S.sub.u=C.sub.uQ.sub.u, using (17) {tilde over (E)}.sub.u can be given as,

[00048]${\tilde{E}}_u={\left[\underset{J_u}{\underset{}{\frac{{}_v^2}{{}_d^2}.Math.I_N+Q_u}}+S_u\right]}^{-1}\left[Q_u+S_u\right]={\left[J_u+S_u\right]}^{-1}\left[Q_u+S_u\right].$

[0148] It can be shown that Q>>S.sub.u. This implies that J.sub.u>>S.sub.u. Hence, [J.sub.u+S.sub.u].sup.1 can be approximated using Taylor series. {tilde over (E)}.sub.u can be approximated as, {tilde over (E)}.sub.u[J.sub.u.sup.1J.sub.u.sup.1S.sub.uJ.sub.u.sup.1][Q.sub.u+S.sub.u]. Using this diag{{tilde over (E)}.sub.u} can be approximated as, diag{{tilde over (E)}.sub.u}J.sub.u.sup.1Q.sub.udiag{J.sub.u.sup.1S.sub.uJ.sub.u.sup.1S.sub.u}. Since J.sub.u>>S.sub.u as well as Q.sub.u>>S.sub.u, it can be easily shown that J.sub.u.sup.1Q.sub.u>>diag{J.sub.u.sup.1S.sub.uJ.sub.u.sup.1S.sub.u}. So, diag{{tilde over (E)}.sub.u} can be further approximated as,

diag{{tilde over (E)}.sub.u}J.sub.u.sup.1Q.sub.u

[0149] Now, Q.sub.u=diag{C.sub.u} to be computed for computation of diag{{tilde over (E)}.sub.u} in (25). It can be shown, that only three elements in a column of L.sub.u matrix is dominant, which are l.sub.u(0), l.sub.u(1) and l.sub.u(N1). Other elements have comparatively lesser power by at least 40 dB. Hence L.sub.u matrix is approximated as, L.sub.u=circ{l.sub.u(0), l.sub.u(1), 0 . . . 0l.sub.u(N1)}. Using this approximation, (15) and (16), the diagonal elements of Q.sub.u can be approximated as,

[00049]$\begin{array}{cc}{\left[Q_u\right]}_{s,s}\underset{r=0,1,N-1}{\overset{}{.Math.}}.Math..Math.{.Math.l_u\left(r\right).Math.}^2.Math..Math..Math.\tilde{h}\left(u+\left(\left(r+s\right).Math.\mathrm{mod}.Math..Math.N\right).Math.M\right).Math.{.Math.}^2,& \left(26\right)\end{array}$

[0150] where 0sN1 and 0uM1. Now, using (17), (25) and (26), can be approximated as,

[00050]$\begin{array}{cc}\frac{1}{M}.Math.I_M.Math.\underset{u=0}{\overset{M-1}{.Math.}}.Math..Math.J_u^{-1}.Math.Q_u.& \left(27\right)\end{array}$

Algorithm to Compute .SUB.JP

[0151] Algorithm 3 explains low complexity computation of .sub.JP. |l.sub.u(r)|.sup.2 is assumed for r=0,1, N1 and 0uM1 is precomputed at the receiver which requires storage of 3M real values for unbiased receiver.

TABLE-US-00003 Algorithm 3 Computation of .sub.JP 1: if biased receiver then 2: .sub.JP = I.sub.MN 3: else 4: [00051]$\begin{array}{c}\mathrm{Precomputed}.Math..Math.{.Math.l_u\left(r\right).Math.}^2.Math..Math.\mathrm{for}.Math..Math.r=0,1,N-1.Math..Math.\mathrm{and}\\ 0uM-1..Math..Math.\frac{{}_v^2}{{}_d^2}.Math..Math.\mathrm{and}.Math..Math.\end{array}$ 5: for u = 0 : M 1 do 6: Compute : Q.sub.u using (26) 7: [00052]$\mathrm{Compute}:J_u=\left[\frac{{}_v^2}{{}_d^2}.Math.I_N+Q_u\right]$ 8: Compute : J.sub.u.sup.1Q.sub.u 9: end for 10: Compute : .sub.JP = using (27) 11: end if 12: return .sub.JP

Joint-MMSE Low Complexity Structure

[0152] To implement,

y=W.sub.MN.sup.HW.sub.MN.sup.Hy

[0153] IFFT.sub.MN of the product of diagonal matrix .sup.H with (FFT.sub.MN of y) is computed. To implement, =P.sup.TU.sub.M.sup.HPy, the vector, y is first shuffled according to (17) and then passed through N FFT.sub.M whose output is again shuffled according to (18). To implement, =U.sub.N.sup.HD.sup.H, the vector, , is multiplied with D.sup.H using MN-point multiplier whose output is then passed through M FFT.sub.N. The vector, is then passed through M N-order square matrix inversion block to obtain =E using Algorithm 1 or 2. In the last, the vector, is first shuffled according to (17) and then passed through N, IFFT.sub.M whose output is again shuffled according to (18) and multiplied to .sub.JP to obtain estimated data, d. .sub.JP can be computed using Algorithm 3.

Testing

Two Stage Receiver

[0154] Monte-Carlo simulation is performed for GFDM system which comprises of the proposed transmitter and two-stage receiver. Each point in the BER curve is calculated for 107 transmission bits.

[0155] BER of the proposed low complexity transceiver in multipath fading channel is plotted in FIG. 7. The proposed receivers do not incur any performance loss over direct implementations. The proposed MMSE receiver shows significant BER performance gain over other GFDM receivers. For ROF value of 0.1, the proposed MMSE receiver gives the best performance. MMSE receiver achieves SNR gain of 4 dB over OFDM at BER of 10-5. This SNR gain over OFDM is due to higher frequency resolution of GFDM [Ref: N. Michailow, M. Matthe, I. Gaspar, A. Caldevilla, L. Mendes, A. Festag, and G. Fettweis, \Generalized Frequency Division Multiplexing for 5th Generation Cellular Networks, IEEE Transactions on Communications, vol. 62, no. 9, pp. 3045{3061, September 2014]. MMSE receiver also achieves SNR gain of 2.5 dB over our proposed ZF receiver and SIC receiver in [Ref: A. Farhang, N. Marchetti, and L. E. Doyle, \Low complexity GFDM receiver design: A new approach, in 2015 IEEE International Conference on Communications (ICC), June 2015, pp. 4775-4780.] at BER of 410-6. For ROF value of 0.9, the proposed MMSE receiver shows a SNR gain of 5 dB over our proposed ZF receiver at BER of 10-4. BER of SIC receiver in [Ref: Ref: A. Farhang, N. Marchetti, and L. E. Doyle, \Low complexity GFDM receiver design: A new approach, in 2015 IEEE International Conference on Communications (ICC), June 2015, pp. 4775-4780.] floors at 10-2 and has SNR loss of 15 dB over the proposed MMSE receiver.

TABLE-US-00004 TABLE 1 Simulation Parameter for two stage receiver Parameters Value Number of Subcarriers N 128 Number of Timeslots M 8 Mapping 16 QAM Pulse shape Raise Cosine (RC) Sub-carrier Bandwidth 15 KHz Channel Extended Typical Urban (ETU) [Ref: M. Series, Guidelines for evaluation of radio interface technologies for IMT- advanced, Report ITU, no. 2135-1, 2009.] Carrier Frequency 2.4 GHz Maximum Doppler shift 100 Hz RMS delay Spread 1 micro second Channel Equalization MMSE FDE

Joint MMSE Receiver

[0156] In this section, BER performance of proposed receiver is presented in the multipath channel. Table 8 presents the simulation parameters. The multipath channel Urban Micro (UMi) [26] with 20 taps is considered, whose channel delay and channel power are [0 10 15 20 35 40 45 50 55 200 205 250 330 440 515 530 580 590 625 730] ns and [6.7 4.9 7.1 1.9 6.3 3 5.2 7 7.5 10.8 5.2 4.9 9.2 15.5 12.4 16.9 12.7 23.5 22.1 23.6] dB, respectively. The CP is chosen long enough to accommodate the wireless channel delay spread. A coded system with code rate of 0.5 is assumed. Convolution code is used with constraint length of 7 and code generator polynomial of 171 and 133. A random interleaver having length equal to K.sub.qamMN is considered, where K.sub.qam is number of bits in a QAM symbol. Soft maximum likelihood (ML) decoding is implemented at the receiver. Each point in our BER curve is calculated for 10.sup.8 transmission bits.

TABLE-US-00005 TABLE 2 Simulation Parameters for Joint-MMSE Receiver Parameters Value Number of Subcarriers N 128 Number of Timeslots M 5 Mapping 16 QAM Pulse shape Raise Cosine (RC) Sub-carrier Bandwidth 15 KHz Channel Umi [Ref: M. Series, Guidelines for evaluation of radio interface technologies for IMT-advanced, Report ITU, no. 2135-1, 2009.] Carrier Frequency 2.4 GHz Maximum Doppler shift 6.67 Hz RMS delay Spread 0.38 micro second Decoding Soft Maximum Likelihood (ML)

[0157] For soft ML decoding, post processing SNR of MMSE receiver output is required. It is straight forward to compute SNR ((1)) for l.sup.th symbol which can be given as,

[00053]$\left(l\right)=\frac{1}{{\left[\right]}_{l,l}-1}.$

As discussed, is computed for correcting the bias of MMSE equalization outputs using Algorithm 3. is periodic with N. Thus (l+mN)=(l), where, m[0, M1]. This means that computation of F requires additional N complex multiplications.

[0158] The BER performance of the proposed receiver is computed with the direct ones in Michailow et al., Generalized Frequency Division Multiplexing for 5th Generation Cellular Networks. for N=128, M=5 i.e. in TI scenario for ROF value of 0.3 and 0.9 in FIGS. 8A and 8B respectively. For CG based method, k.sub.C=[1410104] is considered i.e. K.sub.C=5. When K.sub.T=0, the proposed TS based receiver shows 0.5 dB SNR loss at the BER of 210.sup.6 for ROF value of 0.3. Whereas for ROF value of 0.9, the proposed receiver with, K.sub.T=0, has 1 dB SNR degradation at the BER of 10.sup.5. When K.sub.T=2 and K.sub.C=5, It is observed that there is no notable degradation in BER as compared to direct implementation for ROF values of 0.3 as well as 0.9. The bias correction provides SNR gain of 1 dB and 0.8 dB at BER of 10.sup.5 for ROF value of 0.3 and 0.9 respectively without any observable increase in complexity. It can be concluded that approximations made in Algorithm 1 and Algorithm 2 have no noticeable effect on BER performance. The proposed bias correction has BER benefits without any observable increase in complexity.