Simplified group interference cancelling for asynchronous DS-CDMA

in this paper, several receivers for data demodulation in an asynchronous direct-sequence code-division multiple access (DS-CDMA) system operating without prior knowledge of the propagation delays are proposed and compared. Special attention is paid to the near-far problem, and the proposed schemes are numerically shown to be near-far robust. The near-far robustness is obtained by estimating the a priori unknown propagation delay using a subspace-based technique. Quantities obtained in the estimation procedure are subsequently used to design an interference suppression filter according to the minimum mean-squared error criterion. A simple two-state Viterbi algorithm is used for the data demodulation in the uncoded case. By extending the trellis used in the Viterbi algorithm, error correcting coding is easily implemented.

In this paper, we examine the performance of the iterative minimum mean-squared error (MMSE) adaptive receiver structure over asynchronous direct-sequence code-division multiple-access (DS-CDMA) channels. The receiver base-station uses estimates of the detected users' symbols to improve the convergence rate of the adaptive algorithm. The iterative algorithm used is based on the parallel interference cancellation (PIC) algorithm, and we examine the transient behaviour of both the least-mean-square (LMS) and the recursive-least squares (RLS) algorithms in asynchronous (i.e., reverse-link) DS-CDMA systems. Our results demonstrate a significant convergence improvement for the asynchronous case. It is shown that for the more severe asynchronous case, the convergence speed of the iterative algorithm is even better than the one achieved for the synchronous channel model. Also, for systems with no power control, the iterative algorithm is shown to be near-far resistance.

In this chapter, an effective blind source separation (BSS) algorithm is applied to solve the co-channel interference problem in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the complex domain and improving output signal to interference plus noise ratio (SINR), but also have relatively low computational complexity. We propose a fast Fourier transform (FFT)-based algorithm called feedback independent component analysis (FebICA) that is able to blindly separate complex modulated digital signals. By applying this algorithm to communication signals, it is observed that it has the advantages of SINR gain improvement as well as low computational complexity. The performance of the FebICA algorithm is shown to be better than the joint approximate diagonalization of eigen-matrices (JADE) algorithm in terms of the output SINR and requires lower computational complexity than the analytical constant modulus algorithm (ACMA). The algorithm is also shown to be more robust with increasing number of sources compared to other algorithms. The separation performance by using the collected field data has also been demonstrated.

Performance Analysis of Wireless Adhoc Networks in Flat Fading and Frequency Selective Fading channels

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks. Instead of using conventional primary user interference constraint, we give a new criterion called allowable signal to interference plus noise ratio (SINR) loss constraint in cognitive transmission to protect primary users. Considering power allocation problem for cognitive users over flat fading channels, in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user, we propose a new power allocation algorithm. The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.

Due to cell planning in a cellular communication network, it is difficult for a mobile user in a cell to get ranging measurements from other neighboring cells. Consequently, it is necessary to enhance signal sensitivity using long integration techniques so that the user position is obtained by trilateration. In order to maximize integration gain in coherent/noncoherent hybrid long integration, it is desirable to make SNR (signal to noise ratio) after coherent integration become as large as possible. Because SIR (signal to interference ratio) is not improved by the long integration, however, it is needed to mitigate CCI (Co-Channel Interference) so that the resultant hearability can be enhanced for weak signals. This paper presents a hybrid long integration scheme in presence of CCI. SNIR (signal to noise plus interference ratio) is employed to explain the loss induced by interference as well as noise. Together with interference cancellation and residual frequency estimation technique, design procedure of hybrid long integration is proposed to maximize SNIR. Simulation results show that hearability is greatly improved at most of cell coverage when the proposed method is used.

This paper considers interference cancellation in radar systems when the signal environment is non-stationary and focuses on the extended sample matrix inversion algorithm (ESMI) first proposed by Hay ward (1996). An explicit expression of the signal to noise plus interference ratio (SINR) obtained by this ESMI algorithm is given and analyzed. Compared to the SMI algorithm, it is shown that the performance improves for mainlobe jammers but degrades for sidelobe jammers. To overcome this drawback, an alternative constraint strategy is proposed which attains the good performances of the standard ESMI algorithm whatever the position of the jammers. Finally, the explicit expressions of these SINR are compared to Monte Carlo simulations w.r.t. implementation conditions

The line-of-sight (LoS) dominant air-ground channels have posed critical interference issues in cellular-connected unmanned aerial vehicle (UAV) communications. In this paper, we propose a new base station (BS) cooperative beamforming (CB) technique for the cellular downlink to mitigate the strong interference caused by the co-channel terrestrial transmissions to the UAV. Besides the conventional CB by cooperatively transmitting the UAV's message, the serving BSs of the UAV exploit a novel CB-based {\it \textbf{interference transmission}} scheme to effectively suppress the terrestrial interference to the UAV. Specifically, the co-channel terrestrial users' messages are shared with the UAV's serving BSs and transmitted via CB so as to cancel their resultant interference at the UAV receiver. To optimally balance between the CB gains for UAV signal enhancement and terrestrial interference cancellation, we formulate a new problem to maximize the UAV's receive signal-to-interference- plus-noise ratio (SINR) by jointly optimizing the power allocations at all of its serving BSs for transmitting the UAV's and co-channel terrestrial users' messages. First, we derive the closed-form optimal solution to this problem in the special case of one serving BS for the UAV and draw useful insights. Then, we propose an algorithm to solve the problem optimally in the general case. Simulation results show that the proposed CB scheme with interference transmission and cancellation (ITC) significantly improves the UAV's downlink SINR as compared to the conventional CB without applying ITC.

Based on a stationary Poisson point process, a wireless network model with\nrandom propagation effects (shadowing and/or fading) is considered in order to\nexamine the process formed by the signal-to-interference-plus-noise ratio\n(SINR) values experienced by a typical user with respect to all base stations\nin the down-link channel. This SINR process is completely characterized by\nderiving its factorial moment measures, which involve numerically tractable,\nexplicit integral expressions. This novel framework naturally leads to\nexpressions for the k-coverage probability, including the case of random SINR\nthreshold values considered in multi-tier network models. While the k-coverage\nprobabilities correspond to the marginal distributions of the order statistics\nof the SINR process, a more general relation is presented connecting the\nfactorial moment measures of the SINR process to the joint densities of these\norder statistics. This gives a way for calculating exact values of the coverage\nprobabilities arising in a general scenario of signal combination and\ninterference cancellation between base stations. The presented framework\nconsisting of mathematical representations of SINR characteristics with respect\nto the factorial moment measures holds for the whole domain of SINR and is\namenable to considerable model extension.\n

Recent advances in power line communications (PLC) have made it popular for in-home networking. This makes PLC an increasingly relevant source of interference for digital subscriber line (DSL) networks within the home environment. This paper presents measured PLC-to-DSL coupling channels for a worst case DSL deployment scenario. We then propose an interference cancelling scheme based on a frequency domain interference canceller (FDIC) that utilizes the common mode (CM) PLC interference to estimate and remove the differential mode (DM) PLC interference. The mean square error (MSE) of the proposed FDIC is derived and compared to the minimum MSE achieved by an optimum Wiener filter. Improvement in the signal to interference plus noise ratio (SINR) achieved by using the FDIC is also presented. Both mathematical analysis and simulation results demonstrate the effectiveness of the proposed FCID in reducing the DM PLC interference on the DM DSL signal.

Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semantic communication systems. In this paper, we model the interference cancellation problem as a maximum a posteriori (MAP) problem over the joint posterior probability of the signal and interference, and theoretically prove that the solution provides excellent estimates for the signal and interference. To solve this problem, we develop an interference cancellation diffusion model (ICDM), which decomposes the joint posterior into independent prior probabilities of the signal and interference, along with the channel transition probability. The log-gradients of these distributions at each time step are learned separately by DMs and accurately estimated through deriving. ICDM further integrates these gradients with advanced numerical iteration method, achieving accurate and rapid interference cancellation. Extensive experiments demonstrate that ICDM significantly reduces the mean square error (MSE) and enhances perceptual quality compared to schemes without ICDM. For example, on the CelebA dataset under the Rayleigh fading channel with a signal-to-noise ratio (SNR) of 20 dB and signal to interference plus noise ratio (SINR) of 0 dB, ICDM reduces the MSE by 4.54 dB and improves the learned perceptual image patch similarity (LPIPS) by 2.47 dB. The code is available at https://github.com/Wireless3C-SJTU/ICDM.

Full-rate, full-diversity space-time codes can be applied in the framework of multiple-input multiple-output asynchronous direct-sequence code-division multiple access (MIMO-ACDMA) to increase the system capacity. First, the performance of these codes in this framework is investigated analytically assuming known channel fading coefficients. An upper bound on the pair-wise error probability is derived. It is shown that the determinant of an effective SINR matrix can serve as a metric to select chip waveforms. Next, different expurgated union bounds are derived to approximate the bit error rate (BER) at different SNR levels. Results show that a chip-level oversampling technique can effectively suppress the multiple-access interference (MAI) when used jointly with high-rate space-time codes.

In this paper, a novel subspace-based spatial and temporal mitigation method is proposed for GPS signal tracking by a multi-antenna receiver in multipath environment. Since the tracking result from the delay-lock-loop has been polluted by the multipath incidence, we only use it to follow the code phase shift but design a subspace-based estimator to distinguish different incident rays by their spatial and temporal signatures. The carrier phase is firstly extended into the signatures because of the signal BPSK modulation. A subspace rotation invariance in space and DOAM-like correlation matrices are both established by the property of the receiving array, and a forward and backward space smooth is also taken here to solve the coherent problem by multipath. The angle-of-arrivals and signatures are estimated from the eigenvalues and eigenvectors from the cross-correlation matrix, the signatures also provide the relative delay and carrier phase estimates with respect to the tracking loop. The line-of-sight ray's estimates are picked up by its first arrival character, and a modified delay tracking output without multipath bias is finally achieved by combining tracking loop and the subspace estimator results together. Simulation results prove that compared with the existing methods, such as the narrow early-minus-late and the multipath estimating delay lock loop, the proposed method has the best multipath mitigation in the delay estimation.

The authors derive the true Cramér–Rao lower bound (CRLB) for a non-data-aided (NDA) carrier phase estimation from general M-ary phase-shift keying (M-PSK) modulation signal over flat Rayleigh fading channels (FRFC). The numerical Monte Carlo method is used to compute the true CRLB from general M-PSK modulation signals over FRFC with complex additive white Gaussian noise. The efficiency of the proposed method is made obvious through the assessment of the true CRLBs from both symmetric and asymmetric 8-PSK modulation signals for various observation window sizes. The simulation results show that optimal NDA carrier phase recovery is harder to acquire from symmetric modulation signals for signal-to-noise ratio (SNR) range up to 5 dB, but depends on the constellation density, only for SNR range superior to 5 dB. For both constellations the CRLB decreases as long as the observation window size increases. The derived bound provides an efficient standard for evaluating the performance of any unbiased NDA carrier phase estimator from general M-PSK modulation signals over FRFC. The method presented here stands useful for general M-ary quadrature amplitude modulation signals as well.

We present a reduced-complexity parallel interference cancellation (PIC) technique, in which PIC is performed only on "unreliable" bits (blocks). We term our technique statistical PIC (STPIC), since the decision to employ PIC for any user signal is based upon received signal statistics. Here we propose two different methods: one is bitwise (instantaneous) STPIC (ISTPIC), in which IC is performed only on the bits whose absolute log likelihood ratio (LLR) is below a pre-selected threshold; the other method is block STPIC (BSTPIC), in which IC is performed only on the blocks whose average signal-to-noise-plus-interference ratio (SNIR) is below a pre-selected threshold. Both LLR and SNIR statistics reduce to quantities simple to obtain. We show that STPIC can achieve performance equivalent to conventional full PIC (FPIC) and other partial PIC techniques in flat Rayleigh fading channels, but with reduced computational complexity. We also show our proposed ISTPIC can achieve performance better than FPIC in good channel conditions (e.g., AWGN), still with reduced computational complexity. The ISTPIC performance gain over conventional FPIC is larger in conditions of larger loading factor where efficient IC techniques are more likely to be needed