Blind Separation of Two Users Based on User Delays and Optimal Pulse-Shape Design
© Xin Liu et al. 2010
Received: 4 December 2009
Accepted: 9 June 2010
Published: 30 June 2010
A wireless network is considered, in which two spatially distributed users transmit narrow-band signals simultaneously over the same channel using the same power. User separation is achieved by oversampling the received signal and formulating a virtual multiple-input multiple-output (MIMO) system based on the resulting polyphase components. Because of oversampling, high correlations can occur between the columns of the virtual MIMO system matrix which can be detrimental to user separation. A novel pulse-shape waveform design is proposed that results in low correlation between the columns of the system matrix, while it exploits all available bandwidth as dictated by a spectral mask. It is also shown that the use of successive interference cancelation in combination with blind source separation further improves the separation performance.
We consider the problem of multiuser separation in wireless networks via approaches that do not use scheduling. This problem is of interest, for example, when traffic is generated in a bursty fashion, in which case fixed bandwidth allocation would result in poor bandwidth utilization. Lack of scheduling results in collisions, that is, users overlapping in time and/or frequency. To separate the colliding users, one could enable multiuser separation via receive antenna diversity, or code diversity, as in code-division multiple-access (CDMA) systems. However, the former requires expensive hardware since multiple transceiver front ends involve significant cost. Further, the use of multiple antennas might not be possible on small-size terminals or devices. CDMA systems require bandwidth expansion, which requires greater spectral resources, and also introduces frequency-selective fading. In the following, we narrow our field of interest to random-access systems that for the aforementioned reasons cannot exploit antenna diversity, and that are inexpensive in terms of bandwidth. In such systems, the use of different power levels by the users can enable user separation by exploiting the capture effect , or successive interference cancellation (SIC) . Different power levels can result from different distances between the users and the destination, or could be intentionally assigned to users in order to facilitate user separation. While the former case, when it arises, makes the separation problem much easier, the latter approach might not be efficient, as low-power users suffer from noise and channel effects. In the following, we focus on the most difficult scenario of separating a collision of equal-power users. Almost equal powers would also result from power control. Power control is widely used, hence this scenario is of practical interest.
A delay-division multiple access approach was proposed in , which exploits the random delays introduced by transmitters. The approach of  considers transmissions of isolated frames. It requires that users have distinct delays, assumes full channel knowledge at the receiver and exploits the edges of a frame over which users do not overlap. Pulse-shape waveform diversity was considered in  to separate multiple users in a blind fashion. In , the received signal is oversampled and its polyphase components are viewed as independent mixtures of the user signals. User separation is achieved by solving a blind source separation problem. Although no specifics on waveform design are given in , the examples used in the simulations of  consider wideband waveforms for the users. However, if large bandwidth is available, then CDMA would probably be a better alternative to blind source separation. Pulse-shape diversity is also employed in [5, 6], addressing situations in which the pulse-shape waveforms have bandwidth constraints.
In this paper we follow the oversampling approach of , with the following differences. First, we introduce an intentional half-symbol delay between the two users. Second, both users use the same optimally designed pulse-shape waveform. Third, we use successive interference cancelation in combination with blind source separation to further improve the separation performance.
The paper is organized as follows. In Section 2, we describe the problem formulation. The proposed blind method is presented in Section 3. The Pulse-shape design is derived in Section 4. Simulation results validating the proposed method are presented in Section 5, while concluding remarks are given in Section 6.
Bold capitals denote matrices. Bold lower-case symbols denote vectors. The superscript denotes transposition. The superscript denotes the pseudoinverse. denotes the diagonal matrix with diagonal elements the elements of . denotes rounding down to the nearest integer. denotes the trace of its argument. denotes the phase of its argument.
2. Problem Formulation
We consider a distributed antenna system, in which users transmit simultaneously to a base station. Although much of this paper studies the case , for reasons that will be explained later, we will keep the user notation throughout. Narrow-band transmission is assumed here, in which the channel between any user and the base station undergoes flat fading. In addition, quasi-static fading is assumed, that is, the channel gains remain fixed during several symbols.
where denotes the complex channel gain between the th user and the base station; denotes the delay of the th user; is the carrier frequency offset (CFO) of the th user, arising due to relative motion or oscillator mismatch between receive and transmitter oscillators, and represents noise.
Our objective is to obtain an estimate of each user sequence, , up to a complex scalar multiple that is independent of . The estimation will be based on the received signal only, while channel gains, CFOs and user delays are assumed to be unknown. During the recovery process, there is permutation ambiguity, that is, the order of the users may be lost and again the user signals will be recovered up to a scalar multiple. However, these are considered to be trivial ambiguities and are inherent in blind estimation problems.
We should note that typically, in high-speed communication systems, the main lobes of the pulse-shape functions overlap by . This extended time support allows for better frequency concentration, or equivalently, lower spectral occupancy for the transmission of each symbol. However, it introduces intersymbol interference (ISI). Examining for (see (1)), we note the contribution of the th symbol, the contribution of symbol due to the main lobe of , and also contributions of symbols due to the sidelobes of , respectively. If is a Nyquist pulse and samples are taken at times , the overlap does not play any role. However, when we obtain more than one sample during the symbol interval, we expect ISI effects.
where is a matrix whose th row equals ; ; and . This is a instantaneous multiple-input multiple-output (MIMO) problem. Under certain assumptions, to be provided in the following section, the channel matrix is identifiable, and the vector can be recovered up to certain ambiguities. In particular, for each , we get different versions of , that is, within a scalar ambiguity. The effects of the CFO on the separated signals can be mitigated by using any of the existing single-CFO estimation techniques (e.g., [8–13]), or a simple phase-locked loop (PLL) device .
3. Blind User Separation
The following assumptions are sufficient for user separation.
Either the CFOs are distinct, or the user delays are distinct.
and consider the case in which all users have the same delays, that is, . If the CFOs are different, A has full column rank. Even if the CFOs are not distinct, the columns of the channel matrix can be viewed as having been drawn independently from an absolutely continuous distribution, and thus the channel matrix has full rank with probability one .
3.2. Channel Estimation and User Separation
At this point, the users' signals have been decoupled, and all that is left is to mitigate the CFO in each recovered signal. This can be achieved with any of the existing single CFO estimation methods, such as [8–12], or . Alternatively, if the CFO is very small, then we can estimate it and at the same time mitigate its effects using a PLL. We should note here that even a very small CFO needs to be mitigated in order to have good symbol recovery. For example, for 4-ary quadrature amplitude modulation (4QAM) signals and without CFO compensation, even if the normalized CFO is as small as , the constellation will be rotated to a wrong position after samples.
where with . In order to resolve user permutation and shift ambiguities, one can use user IDs embedded in the data .
Although in theory, under the above stated conditions, the matrix has full rank for any number of users, , the matrix condition number may become too high when CFOs or delay differences between users become small. As increases, the latter problem will escalate. Further, for large , the oversampling factor, , must be large. However, as increases, neighboring pulse-shape function samples will be close to each other, and the condition number of will increase. Therefore, the shape of the pulse-shape function sets a limit on the oversampling factor one can use and thus on the number of users one can separate. Recognizing that the above are difficult issues to deal with, we next focus on the two-user case. Further, we propose to introduce an intentional delay of between the two users, in addition to any small random delays there exist in the system.
The performance of user separation depends on the pulse-shape function and also on the location of the samples. Although uniform sampling was described above, non-uniform sampling can also be used, in which case the expressions would require some straightforward modifications. If the samples correspond to a low-value region of the pulse, the corresponding polyphase components will suffer from low signal-to-noise ratio. Also, if the sampling points are close to each other, then the condition number of will increase. Therefore, one should select the sampling points so that the corresponding samples are all above some threshold and the sampling points are as separated as possible. The effect of pulse-shape and optimal shape design will be discussed in the following section.
4. Pulse-Shape Design
In this section, we first investigate the effects of pulse-shape on the condition number of . Since the condition number of a matrix increases as the column correlation increases, we next look at the correlation between the columns of .
4.1. Pulse Effects
In order to maintain a well-conditioned , the correlation coefficient between its columns should be low. Let us further divide the matrix into and . The elements of are samples from the decreasing part of the main lobe of the pulse. On the other hand, the elements of are from the increasing part of the main lobe of the pulse. Thus, the correlation coefficient of and is smaller than the correlation coefficient of and , or that of and . Thus, we focus on the effects of the pulse on the column correlations within and .
See the appendix.
4.2. Optimum Pulse Design
where is the number of samples in . In order for (28) to be a good approximation of (27), should be on the order of .
5. Simulation Results
5.1. Pulse Design Examples
5.2. SER Performance
In this section, we demonstrate the performance of the proposed user separation approach via simulations. We consider a two-user system. The channel coefficients and are taken to be zero-mean complex with unit amplitude and phase that is randomly distributed in . The CFOs are chosen randomly in the range . The input signals are -QAM containing symbols. The estimation results are averaged over independent channels, and Monte-Carlo runs for each channel. One user is intentionally delayed by half a symbol and in addition, small delays, taken randomly from the interval , are introduced to each user.
In our simulations, we combine blind source separation method with SIC . For blind source separation the Joint Approximate Diagonalization of Eigenmatrices (JADE) algorithm was used, which was downloaded from http://perso.telecom-paristech.fr/~cardoso/Algo/Jade/jade.m. We first apply JADE to decouple the users, and then correct the decoupled users' CFOs. Subsequently, the strongest user, that is, the one which shows the best concentration around the nominal constellation is deflated from the received polyphase components to detect the other user. SIC requires that the first user should be detected very well. To achieve this, the sampling points are chosen around the peak of one user signal, so that ISI and interuser interference effects are minimized.
In this experiment, the pulse has time support . We take polyphase components of the received symbols, each consisting of samples taken evenly over the interval , with sampling period . In order to sample around the peak of one user, we used the true shift values. However, in a realistic scenario this information would be obtained via synchronization pilots .
Next, to show the advantage of the intentional half-symbol delay, we consider a case without intentional delay, with random user delays only. The random delays of both users are taken within . In order to prevent worsening of performance we restricted the smallest delay difference between two users to be no less than . We compare the SER performance of the proposed pulse with IOTA and raised cosine pulses at different symbol rates. Firstly, comparing the corresponding curves in Figure 10, one can first see that without the intentional delay the SER performance decreases. In particular, for the proposed pulse in order to achieve SER , we need an SNR of dB and dB for symbol rates M/sec and M/sec, respectively. Secondly, the SER performance of the proposed pulse is still better than that of IOTA and raised cosine pulses at the corresponding symbol rate.
The quality of the CFO estimates depends on the accuracy of the channel matrix estimate. Since low-magnitude elements of the channel matrix correspond to low values of the pulse, and as such are susceptible to errors, we set a threshold, , defined as , and for CFO estimation, we only use elements of whose amplitudes are greater than . In this experiment, we took . The CFO effects were eliminated via a PLL initialized with the CFO estimate of (40). One can see that the larger gives better performance. It is important to note that the large CFOs involve bandwidth expansion. The percentage of bandwidth expansion can be calculated as , where MHz is the bandwidth of the pulse. For and , the percentages of bandwidth expansion for symbol rates M/sec, M/sec and M/sec are, respectively, , , and .
A blind -user separation scheme has been proposed that relies on intentional user delays, optimal pulse-shape waveform design, and also combines blind user separation with SIC. The proposed approach achieves low SER at a reasonable SNR level. Simulation results for the case have confirmed that the proposed pulse design leads to SER performance better than that of conventional pulse-shape waveforms. The intentional delay was equal to half a symbol interval, which means that the users still overlap significantly during their transmissions. The use of intentional delay is necessitated by the fact that, although small user delay and CFO differences help preserve the identifiability of the problem, in practice, they may not suffice to separate the users. Also, although the proposed approach can work for any number of users, as the number of users increases, the CFO and delay differences become smaller, which makes the separation more difficult. Based on our experiments, small CFO differences did not affect performance. Although introducing large intentional CFO differences among users could help, that would increase the effective bandwidth. A new ALOHA-type protocol that separates second-order collision based on the ideas described in this paper, along with a software-defined radio implementation can be found in .
This research was been supported by the National Science Foundation under Grants CNS-09-16947 and CNS-09-05398 and by the Office of Naval Research under Grants N00014-07-1-0500 and N00014-09-1-0342.
- Onozato Y, Liu J, Noguchi S: Stability of a slotted ALOHA system with capture effect. IEEE Transactions on Vehicular Technology 1989, 38(1):31-36. 10.1109/25.31132View ArticleGoogle Scholar
- Tse D, Viswanath P: Fundamentals of Wireless Communication. Cambridge University Press, Cambridge, UK; 2005.View ArticleMATHGoogle Scholar
- Brandt-Pearce M: Signal separation using fractional sampling in multiuser communications. IEEE Transactions on Communications 2000, 48(2):242-251. 10.1109/26.823557View ArticleGoogle Scholar
- Zhang Y, Kassam SA: Blind separation and equalization using fractional sampling of digital communications signals. Signal Processing 2001, 81(12):2591-2608. 10.1016/S0165-1684(01)00155-4View ArticleMATHGoogle Scholar
- Petropulu AP, Olivieri M, Yu Y, Dong L, Lackpour A: Pulse-shaping for blind multi-user separation in distributed MISO configurations. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '08), March-April 2008, Las Vegas, Nev, USA 2741-2744.Google Scholar
- Liu X, Oymak S, Petropulu AP, Dandekar KR: Collision resolution based on pulse shape diversity. Proceedings of the IEEE International Workshop on Signal Processing Advances for Wireless Communications (SPAWC '09), June 2009, Perugia, Italy 409-413.Google Scholar
- Sklar B: Digital Communications: Fundamentals and Applicatons. Prentice Hall, Upper Saddle River, NJ, USA; 2001.Google Scholar
- Ciblat P, Loubaton P, Serpedin E, Giannakis GB: Performance analysis of blind carrier frequency offset estimators for noncircular transmissions through frequency-selective channels. IEEE Transactions on Signal Processing 2002, 50(1):130-140. 10.1109/78.972489View ArticleGoogle Scholar
- Ghogho M, Swami A, Durrani T: On blind carrier recovery in time-selective fading channels. Proceedings of the 33rd Asilomar Conference on Signals, Systems, and Computers, 1999, Pacific Grove, Calif, USA 1: 243-247.Google Scholar
- Gini F, Giannakis GB: Frequency offset and symbol timing recovery in flat-fading channels: a cyclostationary approach. IEEE Transactions on Communications 1998, 46(3):400-411. 10.1109/26.662646MathSciNetView ArticleGoogle Scholar
- Scott KE, Olasz EB: Simultaneous clock phase and frequency offset estimation. IEEE Transactions on Communications 1995, 43(7):2263-2270. 10.1109/26.392969View ArticleGoogle Scholar
- Wang Y, Ciblat P, Serpedin E, Loubaton P: Performance analysis of a class of nondata-aided frequency offset and symbol timing estimators for flat-fading channels. IEEE Transactions on Signal Processing 2002, 50(9):2295-2305. 10.1109/TSP.2002.801919View ArticleGoogle Scholar
- Roman T, Visuri S, Koivunen V: Blind frequency synchronization in OFDM via diagonality criterion. IEEE Transactions on Signal Processing 2006, 54(8):3125-3135.View ArticleGoogle Scholar
- Wolaver DH: Phase-Locked Loop Circuit Design. Prentice Hall, Englewood Cliffs, NJ, USA; 1991.Google Scholar
- Sidiropoulos ND, Giannakis GB, Bro R: Blind PARAFAC receivers for DS-CDMA systems. IEEE Transactions on Signal Processing 2000, 48(3):810-823. 10.1109/78.824675View ArticleGoogle Scholar
- Cardoso JF, Souloumiac A: Blind beamforming for non-Gaussian signals. IEE Proceedings F 1993, 140(6):362-370.Google Scholar
- Liu X, Kountouriotis J, Petropulu AP, Dandekar KR: ALOHA with collision resolution (ALOHA-CR): theory and software defined radio implementation. IEEE Transactions on Signal Processing 2010, 58(8):4396-4410.MathSciNetView ArticleGoogle Scholar
- IEEE standard 802.11 http://www.ieee802.org/11/
- Wu S, Boyd S, Vandenberghe L: FIR filter design via spectral factorization and convex optimization. In Applied and Computational Control, Signals and Circuits. Edited by: Datta BN. Birkhauser, Basel, Switzerland; 1998:215-245.Google Scholar
- Sidiropoulos ND, Davidson TN, Luo Z-Q: Transmit beamforming for physical-layer multicasting. IEEE Transactions on Signal Processing 2006, 54(6):2239-2251.View ArticleGoogle Scholar
- Le Floch B, Alard M, Berrou C: Coded orthogonal frequency division multiplex [TV broadcasting]. Proceedings of the IEEE 1995, 83(6):982-996. 10.1109/5.387096View ArticleGoogle Scholar
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