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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 13 — Jul. 1, 2013
  • pp: 15926–15937
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Chip-interleaved optical code division multiple access relying on a photon-counting iterative successive interference canceller for free-space optical channels

Xiaolin Zhou, Xiaowei Zheng, Rong Zhang, and Lajos Hanzo  »View Author Affiliations


Optics Express, Vol. 21, Issue 13, pp. 15926-15937 (2013)
http://dx.doi.org/10.1364/OE.21.015926


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Abstract

In this paper, we design a novel Poisson photon-counting based iterative successive interference cancellation (SIC) scheme for transmission over free-space optical (FSO) channels in the presence of both multiple access interference (MAI) as well as Gamma-Gamma atmospheric turbulence fading, shot-noise and background light. Our simulation results demonstrate that the proposed scheme exhibits a strong MAI suppression capability. Importantly, an order of magnitude of BER improvements may be achieved compared to the conventional chip-level optical code-division multiple-access (OCDMA) photon-counting detector.

© 2013 OSA

1. Introduction

Free-space optical (FSO) systems have received considerable research attention due to their advantages of low power consumption, wide bandwidth and high information security [1

1. V. W. S. Chan, “Free-space optical communications,” J. Lightwave Technol. 24(12), 4750–4762 (2006) [CrossRef] .

3

3. Z. Wang, W. Zhong, C. Yu, and S. Fu, “Performance improvement of on-off-keying free-space optical transmission systems by a co-propagating reference continuous wave light,” Opt. Express 20(8), 9284–9295 (2012) [CrossRef] [PubMed] .

]. In this context, the optical code-division multiple-access (OCDMA) scheme constitutes a promising multiple access scheme for high-rate multiuser systems [4

4. X. Wang, Z. Gao, N. Kataoka, and N. Wada, “Time domain spectral phase encoding/DPSK data modulation using single phase modulator for OCDMA application,” Opt. Express 18(10), 9879–9890 (2010) [CrossRef] [PubMed] .

7

7. Y. Du, S. J. B. Yoo, and Z. Ding, “Nonuniform spectral phase encoding in optical CDMA networks,” IEEE Photon. Technol. Lett. 18(23), 2505–2507 (2006) [CrossRef] .

].

However, in conventional non-coherent FSO OCDMA systems, strong multiple access interference (MAI) is imposed owing to the non-zero cross correlations of bandwidth inefficient long unipolar optical orthogonal code (OOC) sequences [8

8. M. Jazayerifar and J. A. Salehi, “Atmospheric optical CDMA communication systems via optical orthogonal codes,” IEEE Trans. Commun. 54(9), 1614–1623 (2006) [CrossRef] .

]. Therefore, numerous OCDMA receivers have been proposed for reducing the MAI, including the conventional correlator (CCR), the chip-level detector as well as the parallel and serial interference cancellation (PIC and SIC) schemes [9

9. C. Goursaud, A. Vergojanne, C. Berthelemot, J. Cances, and J. Dumasl, “DS-OCDMA receiver based on parallel interference cancellation and hard limiter,” IEEE Trans. Commun. 54(9), 1663–1671 (2006) [CrossRef] .

11

11. H. Mrabet, I. Dayoub, R. Attia, and S. Haxha, “Performance improving of OCDMA system using 2-d optical codes with optical SIC receiver,” J. Lightwave Technol. 27(21), 4744–4753 (2009) [CrossRef] .

].

Iterative detection is widely used in radio frequency (RF) communications and thermal-noise-limited optical systems [12

12. X. Yuan, Q. Guo, and L. Ping, “Low-complexity iterative detection in multi-user MIMO ISI channels,” IEEE Signal Processing Lett. 15(1), 25–28 (2008) [CrossRef] .

, 13

13. H. V. Poor, “Iterative multiuser detection,” IEEE Sig. Processing Mag. 21(1), 81–88 (2004) [CrossRef] .

], which typically exploit the signal-independent nature of additive Gaussian noise [14

14. X. Zhou, Y. Yang, Y. Shao, and J. Liu, “Photon-counting chip-interleaved iterative PIC detector over atmospheric turbulence channels,” Chin. Opt. Lett. 10(11), 110603 (2012) [CrossRef] .

]. However, in FSO systems operating in weak received signal scenarios, the employment of a shot-noise-limited Poisson photon-counting based iterative detector encounters signal-dependent Poissonian noise.

Hence, the novelty of this paper is the conception of an efficient chip-level iterative a posteriori probability (APP) MAI cancellation technique for the Poisson photon-counting process, namely that of the photon-counting iterative serial interference cancellation (Iter-SIC) scheme. We will demonstrate that the Iter-SIC scheme is capable of exceeding the optimum performance of the conventional chip-level OCDMA scheme [16

16. H. Shalaby, “Complexities, error probabilities, and capacities of optical OOK-CDMA communication systems,” IEEE Trans. Commun. 50(12), 2009–2017 (2002) [CrossRef] .

]. Equally importantly, the proposed Iter-SIC scheme significantly reduces the spreading-code length required for achieving the BER performance of the classic chip-level OCDMA scheme. Moreover, it is resilient against background light noise and its flexible system structure makes the Iter-SIC scheme suitable for employment in practical scenarios.

The remainder of the paper is organized as follows. Section 2 describes the proposed Iter-SIC aided FSO system and the APP based iterative detection algorithm. Section 3 presents our simulation results. Section 4 makes discussions of the application in practical scenarios, before we conclude in Section 5.

2. System description

2.1. Optical transmitter

The transmitter structure of the Iter-SIC FSO system is shown in Fig. 1. Let k ∈ [1, K] be the user index. The information bit sequence dk = {dk(i), i = 1, ⋯ , Ld} of the kth user is encoded by a forward error correcting (FEC) encoder, generating the coded sequence ck = {ck(j), j = 1, ⋯ ,Lc}, where Ld is the information frame length and Lc is the encoded frame length. Then, the encoded data is interleaved by a random user-specific interleaver Πk, producing the sequence xk = {xk(j), j = 1, ⋯ ,Lc}.

Fig. 1 System model. (a) the proposed iterative SIC FSO system link with multiuser interference over atmospheric Poisson channels. (b) photon-counting based iterative SIC detector.

For practicality, the intensity modulation/direct detection (IM/DD) technique relying on on-off keying (OOK) is employed. The modulated sequence sk = {sk(j), j = 1, ⋯ ,Lc} is then used for driving the optical modulator to generate the appropriate photon counts per chip m0 = 0 and m1 = PTc/ representing “0” and “1”, where P,Tc,υ and h denote the transmitted power, chip duration, optical frequency and Plank’s constant, respectively. The elements of {xk(j)} and {sk(j)} are referred to as “chips”.

2.2. Poisson atmospheric channel model

As shown in Fig. 1, the photons generated are transmitted to the receiver via the FSO channel. The atmospheric turbulence-induced fading coefficient Ik ≥ 0 is modelled by a Gamma-Gamma distribution with the PDF given by [17

17. L. C. Andrews and R. L. Phillips, Laser Beam Propagation Through Random Media, 2nd ed. (SPIE Press, 2005) [CrossRef] .

]
fIk(Ik)=2(αβ)(α+β)/2Γ(α)Γ(β)Ik(α+β)/21Kαβ(2αβIk),
(1)
where Ik denotes the channel’s fading coefficient between the kth user laser and the receiving photon detector (PD). The scintillation parameters α > 0 and β > 0 of Eq. (1) are linked to the Rytov variance σR2, which corresponds to the strength of turbulence [17

17. L. C. Andrews and R. L. Phillips, Laser Beam Propagation Through Random Media, 2nd ed. (SPIE Press, 2005) [CrossRef] .

, 18

18. W. Gappmair and S. S. Muhammad, “Error performance of PPM/Poisson channels in turbulent athmosphere with Gamma-Gamma distribution,” Electron. Lett. 43(16), 880–882 (2007) [CrossRef] .

], while Kl(·) is the modified Bessel function of the second kind of order l. Finally, the scintillation index is defined as S.I. = α−1 + β−1 + (αβ)−1.

At the receiver, the Poisson photon-counting model is adopted. Consequently, the received electron counts per chip r(j) follow a Poisson distribution and is given by,
Pr[r(j)]=[nsR(j)+nb]=[nsR(j)+nb]r(j)r(j)!e[nsR(j)+nb],
(2)
where ℙ[λ] denotes Poisson distribution associated with the parameter λ and the received photoelectron counts are denoted by
nsR(j)=ηk=1KIkmkOpt(j),
(3)
where mjOpt(j)={m1|xk(j)=1;m0|xk(j)=0} denotes the transmitted photon counts of the kth user in the jth chip. η represents the PD efficiency. nb = ηPbTc/() stands for the background radiation photoelectrons per chip interval. Pb is the power incident on the PD owing to the background noise.

2.3. Iterative SIC algorithm

As shown in Fig. 1, for each user, the multiuser detector (MUD) consists of an iterative noise estimation (NE) block, an external log-likelihood-ratio calculation (ELC) block and an APP decoder (DEC).

In the MUD block, given the turbulence channel observation I = {Ik, ∀k}, the a posteriori log-likelihood ratios (LLRs) of the encoded sequence {xk(j)} are defined as
LMUD[xk(j)]=logPr[xk(j)=1|r(j),I]Pr[xk(j)=0|r(j),I]=logPr[r(j)|xk(j)=1,I]Pr[r(j)|xk(j)=0,I]LMUD_e[xk(j)]+logPr[xk(j)=1]Pr[xk(j)=0]LMUD_a[xk(j)],
(4)
where LMUD_e [xk(j)] denotes the extrinsic LLR about xk(j), and LMUD_a [xk(j)] denotes the a priori LLR about xk(j).

The ELC block is used for generating the extrinsic LLRs by taking into account the noise estimates provided by the NE block of Fig. 1. By exploiting Eq. (2) and Eq. (4), we have
LMUD_e[xk(j)]=r(j)log[ηIkm1ηk˜=1,k˜kKIk˜mk˜Opt(j)+nb+1]ηIkm1=r(j)log[ηIkm1ξk(j)+1]ηIkm1,
(5)
where we define ξk(j)=ηk˜=1,k˜kKIkmk˜Opt(j)+nb, as the equivalent noise imposed by the multiuser Poisson channels.

On the other hand, the NE processes the a priori LLRs from the DEC and generates the noise estimates of ξk (j) for supporting the operation of the corresponding MUD block. More explicitly, the estimated noise is
ξkEst(j)=ηk˜=1,k˜kKIk˜E[mk˜Opt(j)]+nb,
(6)
Let E[mkOpt(j)] denote the estimated mean of the photon counts of the jth chip for the kth user, which is
E[mkOpt(j)]=m1Pr[mkOpt(j)=m1]+m0Pr[mkOpt(j)=m0]=m1exp{LMUD_a[xk(j)]}1+exp{LMUD_a[xk(j)]}.
(7)
Thus, based on Eq. (6) and Eq. (7), we can achieve
ξkEst(j)=ηk˜=1,k˜kK{Ik˜m1exp{LMUD_a[xk˜(j)]}1+exp{LMUD_a[xk˜(j)]}}+nb,
(8)
where LMUD_a [x (j)] ∈ ΘMUD_a [xk (j)], ΘMUD_a [xk (j)] = {LMUD_a [x1 (j)],..., LMUD_a [xk−1 (j)], LMUD_a [xk+1 (j)],..., LMUD_a [xk (j)]} stands for the set of LLRs from interfering users. As illustrated in Fig. 1 and 2, { ΘMUD_a(n)(xk), 1 ≤ kK} are serially updated, where n is the iteration index.

Fig. 2 Illustration of the LLRs from interfering users at nth iteration.

In the DEC block, the APP decoding is a standard function [19

19. C. Berrou and A. Glavieux, “Near optimum error correcting coding and decoding: turbo-codes,” IEEE Trans. Commun. 44(10), 1261–1271 (1996) [CrossRef] .

]. If a low-complexity repetition coding scheme is used as our FEC code, the a posteriori LLRs of the information bits { LDECBit[dk(i)], i = 1, 2,..., Ld} can be obtained by combining the a priori LLRs of the encoded chips {LDEC_a [ck (j)], j = 1, 2,..., Lc} as follows:
LDECBit[dk(i)]=z=1NcLDEC_a[ck((i1)Nc+z)]sz,
(9)
where szs, s=[+1,1,,+1,1]Nc is the spreading vector of the repetition code employed and Nc denotes the repetition code length. The corresponding repetition code is ck(j)={[1,0,1,0,,1,0]Ncdk(i)=1,[0,1,0,1,,0,1]dk(i)=0.

2.4. Summary of the iterative SIC scheme

For the sake of explicit clarity, the pseudo-code of our Iter-SIC scheme is provided in Algorithm I.

Algorithm I:. Poisson photon-counting Iter-SIC

table-icon
View This Table

Below we analyze the computational complexity of Algorithm I. Firstly, as for the MUD block, according to Eq. (8) and Fig. 2, the noise estimates {ξkEst(n)(j)} of the kth user can be invoked for the (k + 1)th user during the nth iteration as
ξk+1Est(n)(j)=ξkEst(n)(j)+η{IkE[mkOpt(n)(j)]Ik+1E[mk+1Opt(n1)(j)]}=ξkEst(n)(j)+η{Ikm1exp{LMUD_a(n)[xk(j)]}1+exp{LMUD_a(n)[xk(j)]}Ik+1m1exp{LMUD_a(n1)[xk+1(j)]}1+exp{LMUD_a(n1)[xk+1(j)]}}.
In this way, the computational complexity maybe substantially reduced. The normalized computational complexity involved in step (2.1) and (2.2) of Algorithm I is only 1 exponentiation, 1 logarithm, 4 additions and 5 multiplications per user per chip per iteration. Secondly, as for the DEC block, the complexity is given by (2Nc − 1) additions, 2Nc multiplications per bit, namely (2 − 1/Nc) addition, 2 multiplications per user per chip per iteration, if repetition coding is adopted.

Thus, the overall computational cost of Algorithm I is 1 exponentiation, 1 logarithm, (6−1/Nc) additions and 7 multiplications per user per chip per iteration. The Iter-SIC scheme’s computational complexity of O(K) per chip per iteration is modest. By contrast, some typical CDMA MUD algorithms have a substantially higher complexity of O(K2) per chip per iteration, such as that of the well-known SIC-MMSE detector [15

15. X. Wang and H. V. Poor, “Iterative (turbo) soft interference cancellation and decoding for coded CDMA,” IEEE Trans. Commun. 467, 1046–1061 (1999) [CrossRef] .

].

3. Simulation results

In this section, the BER performance of the proposed Iter-SIC FSO scheme is evaluated for transmission over Poisson atmospheric channels.

3.1. Comparison with conventional OCDMA schemes

Firstly, we compare the performance of the Iter-SIC scheme of 5 iterations to that of the conventional OCDMA schemes [16

16. H. Shalaby, “Complexities, error probabilities, and capacities of optical OOK-CDMA communication systems,” IEEE Trans. Commun. 50(12), 2009–2017 (2002) [CrossRef] .

] in Fig. 3 and 4, where we support K = 9 users.

Fig. 3 Performance of the proposed Iter-SIC scheme in non-turbulent atmosphere. (a) Comparison between the conventional OCDMA CCR and chip-level receiver. (b) Comparison between the optimum OCDMA receiver. For CCR, chip-level detector of OCDMA, Rc is OOC spreading factor. For Iter-SIC receiver, Rc denotes the FEC coding rate. And, the background radiation photoelectrons per bit interval nbbit=nb/Rc.
Fig. 4 Performance of the proposed Iter-SIC scheme in non-turbulent atmosphere. (a) Comparison of two conventional OCDMA schemes. (b) Performance of the NE block. For single-user performance, Rc = 1/2(Turbo)*3/5(Rep.) for the same effective throughput.

In Fig. 3(a), the performance of OCDMA employing a CCR and a chip-level receiver is illustrated, showing that the proposed Iter-SIC scheme is capable of efficiently mitigating the MAI for Poisson channels and hence achieves significant BER performance improvements compared to the conventional OCDMA schemes. Furthermore, the performance of the optimum OCDMA receiver is illustrated in Fig. 3(b), demonstrating that upon increasing the average photon counts per bit, the proposed Iter-SIC scheme is capable of exceeding the best possible performance of the chip-level conventional OCDMA scheme without MAI cancellation [16

16. H. Shalaby, “Complexities, error probabilities, and capacities of optical OOK-CDMA communication systems,” IEEE Trans. Commun. 50(12), 2009–2017 (2002) [CrossRef] .

].

For ease of comparison, we compare the results of two OCDMA detectors and of the Iter-SIC detector in Fig. 4(a). The attainable performance of turbo code aided Iter-SIC is also portrayed, exhibiting a powerful MAI mitigation capability. On the other hand, importantly, the repetition coding rate of the Iter-SIC scheme is Rc = 1/30, which is higher than that of the spreading code rate of the conventional OCDMA schemes, such as Rc = 1/150, hence potentially improving the bandwidth-efficiency by a factor of 5.

More insightfully, the power of the NE block is illustrated in Fig. 4(b), where the estimation of the equivalent noise becomes increasingly more accurate as the number of iterations increases.

3.2. Performance over atmospheric Poisson channels

Figure 5(a) investigates the performance of the Iter-SIC scheme for transmission over atmospheric Poisson channels associated with σR = 0.25. The number of users supported is set to K = 9 and the repetition coding rate is set to Rc = 1/30. Our simulation results show that the proposed Iter-SIC scheme exhibits a rapid convergence after 5 iterations.

Fig. 5 (a) BER of the Iter-SIC scheme over Gamma-Gamma fading channels (σR = 0.25). (b) BER of the Iter-SIC scheme against the number of users for the same effective throughput RcK = 3/10.

Figure 5(b) demonstrates the effect of the number of users associated with different photon counts per bit by maintaining the same effective throughput of RcK = 3/10 over turbulent fading channels. As expected, the BER performance degrades upon increasing the number of users and it improves upon increasing the photon counts per bit.

3.3. Convergence analysis

The achievable convergence speed is an important characteristic of iterative signal processing algorithms. Our convergence-speed evaluation method is based on quantifying both the minimum and the mean of LLRs [20

20. A. C. Reid, T. A. Gulliver, and D. P. Taylor, “Convergence and errors in turbo-decoding,” IEEE Trans. Commun. 49(12), 2045–2051 (2001) [CrossRef] .

], because if the minimum and the mean of the LLRs become large and reach their steady-state value after a number of iterations, then the algorithm is deemed to have converged.

In Fig. 6, both the minimum and the mean of the chip-LLRs is seen to converge after 7 iterations, while the variance of the chip-LLRs tends to zero. Hence the algorithm becomes convergent and therefore the BER also reaches its best possible value.

Fig. 6 Analysis of convergence properties. (a) non-turbulence with user number K = 9. (b) turbulence-fading with user number K = 9. (c) turbulence-fading with user number K = 12. Repetition coding Rc = 1/30. Gamma-Gamma turbulence fading associated with σR = 0.25. Average transmit photon-counts are 200.

Thus, Fig. 6 reveals that the proposed Iter-SIC scheme is capable of rapid convergence both in the non-turbulence and turbulence-fading channel scenarios, provided that a sufficiently low FEC coding rate is adopted.

3.4. Impact of background light noise

Figure 7 shows that the BER performance associated with nbbit=30 is close to that of nbbit=60, 90, 120, respectively, for both non-turbulent and turbulent fading (σR = 0.25) channels, which makes our proposed scheme suitable for employment in practical scenarios as it is resilient against the background-induced light-noise.

Fig. 7 Impact of background light noise ( nbbit) on the photon-counting Iter-SIC system. (a) non-turbulent channels. (b) turbulent fading channels.

3.5. Impact of the number of users with different repetition coding rates

Figure 8 depicts the BER curves associated with a different user numbers, and with different repetition coding rates. For a single-user operating in the absence of multiuser interference, Fig. 8(a) shows that the BER curve is reminiscent of a straight line, especially in the high average photon-count region. For K = 3, 6, 9 users, Fig. 8(a) also shows an approximately linear BER vs photon counts relationship. The reason is likely to because the multiuser interference is essentially eliminated and the multiuser scenario becomes similar to the single-user case. Thus, the BER curve exhibits an approximately linear trend. Moreover, the shot-noise is signal-dependent, which constitutes the reason for the performance gaps among the K = 3, 6, 9 cases, although the multiuser interference was substantially mitigated. By contrast, in the presence of multiuser interference, the BER curve of conventional chip-level OOC exhibits an error-floor, as shown in the dashed line marked by cross of Fig. 8(a). In turbulence fading channels associated with α = 4.1, β = 2.0, the error-floor occurs at a high BER level for both single-user and multiuser scenarios, as seen in Fig. 8(b).

Fig. 8 BER performance associated with different number of user numbers (K), and with different repetition coding rates (Rc), namely with K = 3, 6, 9, and with the repetition coding rate of Rc=110, 120, 130, respectively. For K = 1, we use Rc=14. For the chip-level OOC, we have K = 9 with Rc=1120. (a) non-turbulent channels. (b) turbulent fading channels.

The simulation results of Fig. 8 show that, for repetition coding, the relationship between the BER and the photon counts is nearly linear in non-turbulent channels. At the time of writing, the theoretical analysis of this phenomenon is an open problem, since the Poisson distributed iterative structure imposes a challenge.

4. Discussions on the employment in practical scenarios

The photon-counting Iter-SIC scheme has numerous advantages in practical situations. Firstly, our results demonstrated that the Iter-SIC scheme is resilient against the background-induced light-noise. Naturally, the additional employment of optical filters may also be necessary in practical scenarios for employment during the day time. Secondly, the Iter-SIC scheme may also be further developed to a multiple-lasers based multiple-PDs aided structure for the sake of mitigating the effects of turbulence-fading. Thus, a beneficial spatial diversity gain can be achieved for mitigating the effects of the turbulence fading. Apart from the above-mentioned factors, the absorption by water molecules also constitutes a critical impairment in FSO communications. The hybrid FSO/RF Iter-SIC scheme is an attractive candidate solution for employment in both foggy and rainy weather conditions.

5. Conclusions

A photon-counting Iter-SIC scheme is proposed for transmission over Poisson atmospheric channels. An efficient iterative MUD algorithm was conceived for mitigating the MAI in presence of shot noise, background radiation and turbulent fading. Our simulations demonstrated that the proposed scheme is capable of mitigating the effects of MAI at a significantly increased bandwidth-efficiency. Moreover, it is resilient against background-induced light-noise and its flexible system structure helps the Iter-SIC scheme to be employed in practical scenarios.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant No. 60802011 and the National High Technology Research and Development Program of China under Grant No. 2011AA100701. The support of the European Research Council’s Advanced Fellow Scheme is also gratefully acknowledged. The authors would like to thank the anonymous reviewers for their constructive comments.

References and links

1.

V. W. S. Chan, “Free-space optical communications,” J. Lightwave Technol. 24(12), 4750–4762 (2006) [CrossRef] .

2.

L. Hanzo, H. Haas, S. Imre, D. O’Brien, M. Rupp, and L. Gyongyosi, “Wireless myths, realities, and futures: from 3G/4G to optical and quantum wireless,” Proc. IEEE 100(special centennial issue), 1853–1888 (2012) [CrossRef] .

3.

Z. Wang, W. Zhong, C. Yu, and S. Fu, “Performance improvement of on-off-keying free-space optical transmission systems by a co-propagating reference continuous wave light,” Opt. Express 20(8), 9284–9295 (2012) [CrossRef] [PubMed] .

4.

X. Wang, Z. Gao, N. Kataoka, and N. Wada, “Time domain spectral phase encoding/DPSK data modulation using single phase modulator for OCDMA application,” Opt. Express 18(10), 9879–9890 (2010) [CrossRef] [PubMed] .

5.

J. Jiang, D. Wu, and P. Fan, “General constructions of optimal variable-weight optical orthogonal codes,” IEEE Trans. Inf. Theory 57(7), 4488–4496 (2011) [CrossRef] .

6.

G. C. Yang, C. H. Chen, and W. C. Kwong, “Accurate analysis of double-weight optical CDMA with power control,” IEEE Trans. Commun. 60(2), 322–327 (2012) [CrossRef] .

7.

Y. Du, S. J. B. Yoo, and Z. Ding, “Nonuniform spectral phase encoding in optical CDMA networks,” IEEE Photon. Technol. Lett. 18(23), 2505–2507 (2006) [CrossRef] .

8.

M. Jazayerifar and J. A. Salehi, “Atmospheric optical CDMA communication systems via optical orthogonal codes,” IEEE Trans. Commun. 54(9), 1614–1623 (2006) [CrossRef] .

9.

C. Goursaud, A. Vergojanne, C. Berthelemot, J. Cances, and J. Dumasl, “DS-OCDMA receiver based on parallel interference cancellation and hard limiter,” IEEE Trans. Commun. 54(9), 1663–1671 (2006) [CrossRef] .

10.

A. O. M’foubat, I. Dayoub, J. M. Rouvaen, W. Hamouda, and A. Mazen, “Approach to interference cancellation in DS-CDMA optical networks,” J. Opt. Commun. Netw. 1(3), 204–212 (2009) [CrossRef] .

11.

H. Mrabet, I. Dayoub, R. Attia, and S. Haxha, “Performance improving of OCDMA system using 2-d optical codes with optical SIC receiver,” J. Lightwave Technol. 27(21), 4744–4753 (2009) [CrossRef] .

12.

X. Yuan, Q. Guo, and L. Ping, “Low-complexity iterative detection in multi-user MIMO ISI channels,” IEEE Signal Processing Lett. 15(1), 25–28 (2008) [CrossRef] .

13.

H. V. Poor, “Iterative multiuser detection,” IEEE Sig. Processing Mag. 21(1), 81–88 (2004) [CrossRef] .

14.

X. Zhou, Y. Yang, Y. Shao, and J. Liu, “Photon-counting chip-interleaved iterative PIC detector over atmospheric turbulence channels,” Chin. Opt. Lett. 10(11), 110603 (2012) [CrossRef] .

15.

X. Wang and H. V. Poor, “Iterative (turbo) soft interference cancellation and decoding for coded CDMA,” IEEE Trans. Commun. 467, 1046–1061 (1999) [CrossRef] .

16.

H. Shalaby, “Complexities, error probabilities, and capacities of optical OOK-CDMA communication systems,” IEEE Trans. Commun. 50(12), 2009–2017 (2002) [CrossRef] .

17.

L. C. Andrews and R. L. Phillips, Laser Beam Propagation Through Random Media, 2nd ed. (SPIE Press, 2005) [CrossRef] .

18.

W. Gappmair and S. S. Muhammad, “Error performance of PPM/Poisson channels in turbulent athmosphere with Gamma-Gamma distribution,” Electron. Lett. 43(16), 880–882 (2007) [CrossRef] .

19.

C. Berrou and A. Glavieux, “Near optimum error correcting coding and decoding: turbo-codes,” IEEE Trans. Commun. 44(10), 1261–1271 (1996) [CrossRef] .

20.

A. C. Reid, T. A. Gulliver, and D. P. Taylor, “Convergence and errors in turbo-decoding,” IEEE Trans. Commun. 49(12), 2045–2051 (2001) [CrossRef] .

OCIS Codes
(010.1330) Atmospheric and oceanic optics : Atmospheric turbulence
(060.4510) Fiber optics and optical communications : Optical communications
(060.2605) Fiber optics and optical communications : Free-space optical communication

ToC Category:
Fiber Optics and Optical Communications

History
Original Manuscript: April 23, 2013
Revised Manuscript: June 11, 2013
Manuscript Accepted: June 11, 2013
Published: June 26, 2013

Citation
Xiaolin Zhou, Xiaowei Zheng, Rong Zhang, and Lajos Hanzo, "Chip-interleaved optical code division multiple access relying on a photon-counting iterative successive interference canceller for free-space optical channels," Opt. Express 21, 15926-15937 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-13-15926


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References

  1. V. W. S. Chan, “Free-space optical communications,” J. Lightwave Technol.24(12), 4750–4762 (2006). [CrossRef]
  2. L. Hanzo, H. Haas, S. Imre, D. O’Brien, M. Rupp, and L. Gyongyosi, “Wireless myths, realities, and futures: from 3G/4G to optical and quantum wireless,” Proc. IEEE100(special centennial issue), 1853–1888 (2012). [CrossRef]
  3. Z. Wang, W. Zhong, C. Yu, and S. Fu, “Performance improvement of on-off-keying free-space optical transmission systems by a co-propagating reference continuous wave light,” Opt. Express20(8), 9284–9295 (2012). [CrossRef] [PubMed]
  4. X. Wang, Z. Gao, N. Kataoka, and N. Wada, “Time domain spectral phase encoding/DPSK data modulation using single phase modulator for OCDMA application,” Opt. Express18(10), 9879–9890 (2010). [CrossRef] [PubMed]
  5. J. Jiang, D. Wu, and P. Fan, “General constructions of optimal variable-weight optical orthogonal codes,” IEEE Trans. Inf. Theory57(7), 4488–4496 (2011). [CrossRef]
  6. G. C. Yang, C. H. Chen, and W. C. Kwong, “Accurate analysis of double-weight optical CDMA with power control,” IEEE Trans. Commun.60(2), 322–327 (2012). [CrossRef]
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