In this paper, we consider the design and bit-error performance analysis of linear parallel interference cancellers (LPIC) for multicarrier (MC) direct-sequence code division multiple access (DS-CDMA) systems. We propose an LPIC scheme where we estimate and cancel the multiple access interference (MAI) based on the soft decision outputs on individual subcarriers, and the interference cancelled outputs on different subcarriers are combined to form the final decision statistic. We scale the MAI estimate on individual subcarriers by a weight before cancellation. In order to choose these weights optimally, we derive exact closed-form expressions for the bit-error rate (BER) at the output of different stages of the LPIC, which we minimize to obtain the optimum weights for the different stages. In addition, using an alternate approach involving the characteristic function of the decision variable, we derive BER expressions for the weighted LPIC scheme, matched filter (MF) detector, decorrelating detector, and minimum mean square error (MMSE) detector for the considered multicarrier DS-CDMA system. We show that the proposed BER-optimized weighted LPIC scheme performs better than the MF detector and the conventional LPIC scheme (where the weights are taken to be unity), and close to the decorrelating and MMSE detectors.

In uplink orthogonal frequency division multiple access (OFDMA) systems, multiuser interference (MUI) occurs due to different carrier frequency offsets (CFO) of different users at the receiver. In this paper, we present a multistage linear parallel interference cancellation (LPIC) approach to mitigate the effect of this MUI in uplink OFDMA. The proposed scheme first performs CFO compensation (in time-domain) followed by K DFT operations (where K is the number of users) and multistage LPIC on these DFT outputs. We scale the MUI estimates by weights before cancellation and optimize these weights by maximizing the average signal-to-interference ratio (SIR) at the output of the different stages of the LPIC. We derive closed-form expressions for these optimum weights. The proposed LPIC scheme is shown to effectively cancel the MUI caused by the other user CFOs in uplink OFDMA. While our proposed approach performs CFO compensation in time-domain, an alternate approach proposed recently by Huang and Letaief performs CFO compensation and interference cancellation in frequency-domain. We show that our approach performs better than the Huang & Letaief's approach when the magnitude of the CFO differences (between desired user CFO and other user CFOs) are small, whereas their approach performs better when the magnitude of the individual CFOs (of other users) are small. Since the CFO values can be arbitrary at the receiver, in order to make the receiver robust under various CFO conditions, we propose simple metrics based on CFO knowledge, which the receiver can compute and use to choose between the time-domain (ours) and the frequency-domain (Huang & Letaief's) cancellers so that better performance among the two approaches is achieved under various CFO conditions.

In uplink orthogonal frequency division multiple access (OFDMA) systems, multiuser interference (MUI) occurs due to different carrier frequency offsets (CFO) of different users at the receiver. In this paper, we present a multistage linear parallel interference cancellation (LPIC) approach to mitigate the effect of this MUI in uplink OFDMA. The proposed scheme first performs CFO compensation (in time domain) followed by K DFT operations (where K is the number of users) and multi-stage LPIC on these DFT outputs. We present a detailed performance and complexity comparison of the proposed scheme with another scheme proposed recently by Huang and Letaief which performs CFO compensation and interference cancellation in frequency domain using Circular Convolution (we refer to this scheme as HLCC scheme). We show that the HLCC scheme performs better than our scheme when the individual CFO values are small, whereas our scheme performs better than the HLCC scheme when the CFO differences are small (even if the individual CFO values are large). Also, our scheme has lesser complexity than HLCC scheme when the number of subcarriers is large, which is typical in OFDMA systems.

In this paper, we present the design and bit error performance analysis of weighted linear parallel interference cancellers (LPIC) for multicarrier (MC) DS-CDMA systems. We propose an LPIC scheme where we estimate (and cancel) the multiple access interference (MAI) based on the soft outputs on individual subcarriers, and the interference cancelled outputs on different subcarriers are combined to form the final decision statistic. We scale the MAI estimate on individual subcarriers by a weight before cancellation; these weights are so chosen to maximize the signal-to-interference ratios at the individual subcarrier outputs. For this weighted LPIC scheme, using an approach involving the characteristic function of the decision variable, we derive exact bit error rate (BER) expressions for different cancellation stages. Using the same approach, we also derive exact BER expressions for the matched filter (MF) and decorrelating detectors for the considered MC DS-CDMA system. We show that the proposed weighted LPIC scheme performs better than the MF detector and the conventional LPIC (where the weights are taken to be unity), and close to the decorrelating detector

In uplink orthogonal frequency division multiple access (OFDMA) systems, multiuser interference (MUI) occurs due to different carrier frequency offsets (CFO) of different users at the receiver. In this paper, we present a multistage linear parallel interference cancellation (LPIC) approach to mitigate the effect of this MUI in uplink OFDMA. The proposed scheme first performs CFO compensation (in time domain), followed by K DFT operations (where K is the number of users) and multistage LPIC on these DFT outputs. We scale the MUI estimates by weights before cancellation and optimize these weights by maximizing the signal-to-interference ratio (SIR) at the output of the different stages of the LPIC. We derive closed-form expressions for these optimum weights. The proposed LPIC scheme is shown to effectively cancel the MUI caused by the other user CFOs in uplink OFDMA

In this letter, we present a weighted linear parallel interference canceller (LPIC) where the multiple access interference (MAI) estimate in a stage is weighted by a factor before cancellation on Rayleigh fading and diversity channels. We obtain exact expressions for the average signal-to-interference ratio (SIR) at the output of the cancellation stages which we maximize to obtain the optimum weights for different stages. We also obtain closed-form expressions for the optimum weights for the different stages. We show that this SIR-optimized weighted LPIC scheme clearly outperforms both the matched filter (MF) detector as well as the conventional LPIC (where the weight is taken to be unity for all stages), in both near-far as well as non-near-far conditions on Rayleigh fading and diversity channels

In this paper, we consider the design and bit error performance analysis of linear parallel interference cancellers (LPIC) for multicarrier (MC) DS-CDMA systems. We propose an LPIC scheme where we estimate (and cancel) the multiple access interference (MAI) based on the soft decision outputs on individual subcarriers, and the interference cancelled outputs on different subcarriers are combined to form the final decision statistic. We scale the MAI estimate on individual subcarriers by a weight before cancellation. In order to choose these weights optimally, we derive exact closed-form expressions for the bit error rate (BER) at the output of different stages of the LPIC, which we minimize to obtain the optimum weights for the different stages. We show that the proposed BER-optimized weighted LPIC scheme performs better than the MF detector and the conventional LPIC scheme (where the weights are taken to be unity), and close to the decorrelating detector

In this paper, we present weighted linear parallel interference cancellers (LPIC) where the estimated multiple access interference (MAI) from each interfering user is weighted by a factor before cancellation on Rayleigh fading and diversity channels. We obtain exact expressions for the signal-to-interference ratio (SIR) at the output of the cancellation stages which we maximize to obtain the optimum weights for each interfering user in each cancellation stage. For a system that uses same weight for all interfering users in a given stage, we obtain exact closed-form expressions for the optimum weights for the different stages of the LPIC. We show that this SIR-optimized weighted LPIC scheme clearly outperforms both the matched filter (MF) detector as well as the conventional LPIC (where the weight is taken to be unity for all interfering users and for all stages), in both near-far as well as non-near-far conditions.

We obtain closed-form expressions for the bit error rate (BER) of a conventional linear parallel interference canceller (LPIC), where the interference from all other users is estimated and cancelled on Rayleigh fading and diversity channels. We also propose an improved LPIC scheme which cancels the interference from only those users whose instantaneous SNRs exceed a certain threshold on Rayleigh fading channels. We obtain the optimum threshold by optimizing an approximate expression for the SIR at the canceller output. We show that the proposed threshold-based LPIC scheme performs better than the matched filter (MF) detector as well as the conventional LPIC scheme.

In this paper, we present a performance analysis of the bit error performance of detectors in multiuser systems, considering diversity reception with imperfect channel estimation. The detectors we consider are the decorrelating (DC) detector and the conventional matched filter (MF) detector. We consider a pilot-based channel estimation scheme in flat as well as diversity fading channels, and analytically quantify the degradation in the bit error performance of the DC and the MF detectors due to imperfect channel estimates. We show that, while imperfect channel estimates degrade performance compared to perfect channel estimates, the degradation in the bit error performance can be compensated by using a larger number of receive antennas.