Performance Improvement of IDMA System with High Resolution Signal Detection

Performance Improvement of IDMA System with High Resolution Signal Detection

1P. Niroopan, 2K. Bandara, 3Yeon-ho Chung

1Pukyong National University, Busan, South Korea, [email protected] 2Pukyong National University, Busan, South Korea, [email protected]

*3Pukyong National University, Busan, South Korea, [email protected]

Abstract This paper presents performance improvement of Interleave Division Multiple Access system with

high resolution signal based detection (HR-IDMA) that employs received signal refinement via an oversampling method for user chip-level data. The oversampling is performed by using a fractionally spaced equalizer with polyphase subchannels at the receiver. This oversampled data gives additional redundancy for the transmitted signal that is used to minimize the noise power and thus helps to suppress inter-symbol interference (ISI) with reduced multiple access interference (MAI). Moreover, each chip-level data is further refined by iterative processes of sampled data for each user. Numerical simulations demonstrate that the proposed HR-IDMA system with signal refinement offers the improved bit-error rate (BER) performance of the system over the Additive White Gaussian Noise (AWGN) channel as well as a flat fading channel.

Keywords: IDMA, Multiuser Detection, Oversampling, Polyphase, Resolution

1. Introduction

An important question when designing and standardizing wireless cellular systems is the selection

of the multiple access scheme that can support high data rate and high reliability. High data rate and high reliability are considered essential for next generation wireless communication systems.

A new multiple access scheme called Interleave Division Multiple Access (IDMA) [1] [2], which is a special case of CDMA [3], was recently proposed as a spread spectrum multiple access scheme. Unlike a CDMA system, a user-specific spreading sequence is not used in the IDMA system. Instead, user-specific interleavers are adopted as the only mechanism for user separation. As such, the IDMA utilizes a low-complexity iterative multiuser detection process.

The IDMA system uses low-rate codes by combining FEC and repetition code to make spreading more reasonable to support multiuser, which then lead to an improved coding gain and subsequently improved performance. This scheme is not optimized from a performance point of view, since the repetition code is actually a very poor code. However, this structure does have the advantage of flexibility in terms of data rate. If the reduction of repetition length is desired, we have to make redundancy available in another way for an acceptable performance.

A signal refinement via oversampling is useful to improve performance, since it creates redundancy at the receiver and redundant samples are subsequently empoloyed to suppress ISI by minimizing noise power in an iterative detection process. Oversampling techniques are analyzed in a multiuser CDMA system for performance improvement by Vrcelj et al.[7]. Jingxian [8] proposed a new oversampled OFDM scheme by using oversampling in the time domain and frequency domain at the receiver over the fading environments. The influence of oversampling on the channel parameter estimation is also investigated by Schmeink [10]. In implementing the oversampling, the fractionally spaced equalization and polyphase components were discussed to create oversamples in the received signals [14-19]. However, signal refinement issues in the IDMA system have not been reported in the literature.

In this paper, we focus on an improvement of the conventional IDMA system with a high level of signal refinement for user data at the receiver by employing the fractionally spaced equalization with polyphase subchannels. The remainder of this paper is organized as follows. In Section 2, we present a brief introduction of transceiver structure in the IDMA system. Then, the oversampling of the received signal is described in Section 3. In Section 4, numerical simulation results are presented and Section 5 concludes with remarks.

Performance Improvement of IDMA System with High Resolution Signal Detection P. Niroopan, K. Bandara, Yeon-ho Chung

Journal of Next Generation Information Technology(JNIT) Volume4, Number2, April 2013 doi:10.4156/jnit.vol4.issue2.2

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2. IDMA System

The transmitter and receiver structures of an IDMA system with K simultaneous users are shown in Figure 1 and 2, respectively. At the transmitter, the block size of N-length information bits from each user-k is denoted as dk = [dk(0),………, dk(N-1)]T, k=1, 2, …, K. The data sequence is encoded using a convolutional code into bk = [bk(0), ..……, bk(NC-1)]T. That is, the code rate is defined as R1=N/NC. Then each bit of bk is again encoded using a low rate code such as a spread encoder with a rate of R2=1/Sk, where Sk is a spreading factor. Thus, the overall code rate is R1R2, which produces a chip signal. The second encoder output is fed into the user specific interleaver (π1, π2, .….., πK) for user separation, which generates xk(j), j = 1, 2, ….., J, where J is the user frame length. The resultant signal is then transmitted through the multiple access channel. In the receiver, the received signal is given by

)1(.....,,2,1),()()(1

JjjnjxhjrK

kkk

where hk is the channel gain for user-k, xk is the corresponding transmitted signal and n is the additive white Gaussian noise (AWGN) process with zero mean and variance, σ2 = N0/2. It is assumed that the channel coefficients {hk} are known a priori at the receiver. This received signal is passed to a multi-user detection (MUD) receiver that consists of an elementary signal estimator (ESE) and K a posteriori probability (APP) decoders (DECs), one for each user. The ESE performs chip-by-chip detection to roughly remove the interference among users.

Figure 1. The transmitter structure of IDMA scheme

Figure 2. The receiver structure of IDMA scheme


10

ESEe'

The outputs of the ESE and DECs are extrinsic log-likelihood ratios (LLRs) about {xk} defined as

)2(1)(|(

1)(|(log))((

jxyp

jxypjxe

k

kk

Those LLRs are further distinguished by eESE(xk(j)) and eDEC(xk(j)), depending on whether they are generated by the ESE or DECs. In the ESE section, y in (2) denotes the received channel output while for the DECs, y in (2) is formed by the deinterleaved version of the outputs of the ESE block.

)3(,1)(|)((

,1)(|)((log))((

kk

kkkESE hjxjrp

hjxjrpjxe

where eESE(xk(j)) of each user is estimated depending on the channel coefficient hk of each user.

)4(,1)(|'(

,1)(|'(log))((

cjxep

cjxepjxe

kESE

kESEkDEC

where is derepeater data of the deinterleaved version of the outputs from the ESE block and c is the code constraint by convolutional code. These results are then combined using a turbo-type iterative process for a pre-defined number of iterations. Finally the DECs produce hard decisions on information bits for each user.

3. Oversampling Technique

We consider a fractionally spaced equalizer with polyphase subchannels by oversampling the

received continuous-time signal in the conventional IDMA system. Figure 3 shows the transceiver structure of a continuous-time IDMA system with integral oversampling at receiver. The user specific interleaved input data sequence of user-k is converted from discrete signal to analog signal with a rate of 1/T and is passed through a pulse shaping filter. This continuous time data passes through the continuous multiple access channel h(t). After passing through the multiple access channel, the signal is corrupted by other users’ data and additive white Gaussian noise. At the receiver, the received continuous time data are sampled at q-times the data rate at the output of the transmitter. This oversampled data with rate q/T enters the fractionally spaced equalizer that operates at the correspondingly higher rate. Consider a continuous time model in Figure 3 Before oversampling, the received data from K-users are derived as

K

k ikkc tniTthixtr

1

)5()()()()(

where xk(i) denotes the symbol emitted from the interleaver of user-k with symbol duration T and

rc(t) is the continuous-time received signal. hk(t) denotes the continuous-time channel of user-k and n(t) is the AWGN signal. After oversampling the received signal by a factor of q, the received data will be

)6()()()()()'(1

'''

K

k iqTj

qTj

kkqTj

cm niThixrjr

where the size of j’ is q-times the frame length J. xk(i) denotes the interleaved data of user-k at the

transmitter and rm(j') is the sampled signal at the receiver.


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qkh ,

Figure 3. Transceiver system for continuous-time IDMA with integral oversampling

Figure 4. IDMA system with discrete-time equivalent

In symbol duration T, q-oversamples of the discrete data are the same at the transmitter. When these data are passed through the channel with noise, data will be changed at the receiver. From this, we derive the discrete-time equivalent channel from the continuous-time channel h(t) sampled at q-times. To model the oversampled channel, we consider the fractionally spaced channel with polyphase description. The discrete-time equivalent of the oversampled system is shown in Figure 4. We can rewrite the received signal with the oversampled channels as,

)7()()'()()'( '

1, q

TjK

k iqkkm niqjhixjr

where rm(j’) is the sampled signal at the receiver. denotes discrete channel coefficients of user-k and n is the noise component. We redraw the structure with polyphase components of the oversampled filter as shown in Figure 5. The equivalent q-number of each user data is passed through the different polyphase components of the oversample filter. Then, signals are passed through the additive noise, delay and upsamplers by splitting them into appropriate polyphase components. In the frequency domain, polyphase component of the channel at the sampling rate q/T is given as

q

i

qik

ik zHzzH

1,

)1( )8()()(

where Hk denotes the z-transform of the channel response hk and Hk,i is the subchannel response hk,i.


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Figure 5. Polyphase representation of oversampling by a factor of 2

Figure 6. Proposed equalizer with oversampling by a factor of 2

In the receiver, the received signal is divided into polyphase components. Then, polyphase data is

passed through the filter bank. These decomposed polyphase components can be derived as

q

i

qik

ik zFzzF

1,

)1( )9()()(

Where Fk denotes the decompose filter of the channel and Fk,i is the polyphase component of the

subchannel. In the frequency domain, the condition of the equalizer output can be written as

)10()()( dkk zzHzF

where d is delay element. In the time domain, the impulse response of the channel convolution can

be written as

)11()(*)(........)(*)()( 0,1,1,0, nhnfnhnfnh kqkqkkk

where * denotes convolution and hk(n) denotes the channel impulse response. hk,q(n) and fk,q(n) are

impulse responses of polyphase subchannel and decomposed components of polyphase filter, respectively.


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From the discussion in Section 2, we know how to separate each of the user components. For the oversampling by a factor of 2, there are two sets of polyphase channel data of all users with part of the noise components. Therefore, these polyphase channels will be kept separately with the output symbol rate equal to 1/T, and perform the multiuser detection at each user. In the ESE, estimated value can be derived as follows.

)12()&(,1)(|)((

)&(,1)(|)((log))((

2,1,

2,1,1

kkk

kkkkESE fhjxjrp

fhjxjrpjxe

)13()&(,1)(|)((

)&(,1)(|)((log))((

1,2,

1,2,2

kkk

kkkkESE fhjxjrp

fhjxjrpjxe

Finally, we combine the results to obtain the MAI-free signal received from user-k. Figure 6 shows

the multiuser detection structure of the oversampling method.

4. Simulation and Results

The simulation results are presented to analyze the BER performance of the HR-IDMA and conventional IDMA system over the AWGN and a flat fading channel. We implemented a system using Matlab Simulink.

Initially we performed an evaluation over AWGN channel. The simulations are performed with 5 and 10 simultaneous users. The q-times oversampled channel impulse responses hk,q(n) were chosen randomly under the constraint that they coincide with IDMA channel at hk(n). It is assumed that all users in the system use the same energy level. The parameters used in the simulation are given in the table 1.

Table 1. Parameters used in the simulation over the AWGN channel

Parameters Specifications

No. of users 5, 10

Data length 1024 bits

Encoder Convolutional code (23, 35)8

Repetition 4, 8, 10

Modulation BPSK

Interleaver Random Interleaver

Oversampled factor 2

Channel AWGN

Iteration 10

Figure 7 shows performance comparison between the conventional IDMA and HR-IDMA with 5

simultaneous users for iteration 10 and two different repetition codes. The factor of integral oversampling is 2. The performance of the HR-IDMA system gives better performance over the conventional IDMA system. Also, when the repetition length increases, the BER performance improves significantly.


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Figure 7. Performance comparison between conventional IDMA and HR-IDMA with K = 5

In Figure 8, we performed further evaluation with 10 users, while the oversampling factor remains

to be 2. The number of iterations is 10, while the repetition length of 8 and 10 are used, respectively. Compared with the conventional IDMA, the BER performance of the HR-IDMA system shows significant improvement. When the number of users increases, the performance of the HR-IDMA gives higher performance gain at low Eb/N0 values, whereas the conventional IDMA suffers from poor performance.

Figure 8. Performance comparison between conventional IDMA and HR-IDMA with K = 10


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We made further evaluation over flat fading channel whether oversampling technique still gives improvements in the IDMA system. The parameters used in the simulation are given in the table 2.

Table 2. Parameters used in the simulation over flat fading channel

Parameters Specifications

No. of users 5, 10

Data length 1024 bits

Encoder Convolutional code (23, 35)8

Repetition 8

Modulation QPSK

Interleaver Random Interleaver

Oversampled factor 2

Channel Flat fading

Iteration 10

Doffler Shift (Hz) 5, 50, 100

(a)


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(b)

(c)

Figure 9. Performance comparison between conventional IDMA and HR-IDMA with

K = 5 and 10

(a) fd = 5 Hz (b) fd = 50 Hz (c) fd = 100 Hz


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Figure 9 shows the performance comparison between conventional IDMA and HR-IDMA with 5 and 10 simultaneous users for different Doppler shifts (fd) of 5, 50 and 100 Hz over flat fading channel. The BER performance of the HR-IDMA system shows significant improvement over the conventional IDMA system and it can also be seen that the performance is gradually poor as Doppler shifts increase. An oversampling factor of two is found to be sufficient to collect most of the benefits of the oversampled IDMA system while keeping the iterative multiuser detection process similar to the conventional IDMA system.

5. Conclusion

We present an improvement of the IDMA system that is based on higher resolution of the received

signal via an integral oversampling and its performance is investigated over an AWGN channel and a flat fading channel. The redundancy in the oversamples of the received signal has been exploited to improve the performance of the IDMA system. Using the polyphase channel, we provide a fractionally spaced equalizer that gives reduced noise power and better estimated values for the specific user data with relatively low correlation. The simulation results demonstrate a significant BER performance improvement of the HR-IDMA over the conventional IDMA at the expense of moderate increase in receiver complexity.

6. References

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Performance Improvement of IDMA System with High Resolution Signal Detection

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