Account Channel Estimation Research Articles

Uplink Power-Domain Non-Orthogonal Multiple Access (NOMA)

Non-orthogonal multiple access (NOMA) has been proposed as a promising multiple access (MA) technique in order to meet the requirements for fifth generation (5G) communications and to enhance the performance in internet of things (IoT) networks by enabling massive connectivity, high throughput, and low latency. This paper investigates the bit error rate (BER) performance of two-user uplink power-domain NOMA with a successive interference cancellation (SIC) receiver and taking into account channel estimation errors. The analysis considers two scenarios: perfect (ideal) channel estimation and a channel with estimation errors for various modulations schemes, BPSK, QPSK, and 16-QAM. The simulation results show that, as expected, increasing of the modulation level increases the SIC receiver BER. For example, at a signal-to-noise ratio (SNR) of 5 dB for perfect channel estimation and QPSK modulation, the user that is detected first has a BER of 0.005 compared to 0.14 for the user that is detected with the aid of the SIC receiver. Similarly, the BER of QPSK, assuming 0.25 channel estimation error of user 1, is equal to 0.06 at SNR = 15 dB compared to 0.017 for perfect estimation.

On the Achievable Rate of Multiple-Input–Multiple-Output Underwater Acoustic Communications

This paper investigates the data gain brought by the multiple-input-multiple-output (MIMO) principle for underwater acoustic (UWA) communication. By considering a class of acoustic MIMO channels where each fading coefficient is modeled by a Rice distribution law with correlation factor depending on channel Doppler spread, we derive the achievable rate of such system that takes into account both channel estimation error and training sequence overhead. Model parameters are then estimated from an experiment campaign in a MIMO shallow water channel conducted in the roadstead of Brest, France. The system achievable rate is evaluated for several MIMO architectures and channel configurations and then compared against the conventional single-input-multiple-output transmission. The achievable rate gain is finally put in perspective with end-to-end data rate performance of a single-carrier MIMO transmission system experimented at sea.

Multicell Massive MIMO: Downlink Rate Analysis With Linear Processing Under Ricean Fading

This paper investigates the downlink (DL) rate of multicell massive multiuser multiple-input and multiple-output systems over Ricean fading channels that takes into account channel estimation errors. To acquire channel state information at all users, beamforming training (BT) is examined. Considering both maximum-ratio transmission (MRT) and zero forcing, this paper derives closed-form expressions on the lower bound of the achievable rates for two cases, with or without BT. With the obtained expressions, Bernoulli's inequality is invoked to find the ranges for the length of DL pilots such that the sum spectral efficiency of the scheme with BT is superior to that of the scheme without BT, and vice versa. Various power scaling laws concerning DL data and pilot transmit powers and uplink pilot transmit power are analyzed. Numerical results corroborate the accuracy of the closed-form expressions. In particular, the results show that employing BT with MRT processing is only preferred in environments having a high signal-to-noise ratio, low mobility, and small Ricean $K$ -factors.

On the Interaction Between Joint Tx/Rx IQI and Channel Estimation Errors in DVB-T Systems

Digital video broadcasting terrestrial (DVB-T) systems use hierarchical modulation, which involve a “high-priority” and a “low-priority” (LP) bit stream. An inherent weakness of this scheme is the severe performance degradation of the less protected LP stream. In addition, the presence of inphase quadrature imbalance (IQI) at both transmitter/receiver front end may significantly reduce the detection accuracy. Here, we propose an improved receiver design for OFDM-based DVB-T systems confronting the effects of both imperfect channel estimation and IQI. First , we propose a specific pilot-symbol arrangement inside the OFDM symbol that facilitates derivation of the posterior distribution of the perfect channel conditioned on its maximum-likelihood (ML) estimate. The latter posterior distribution plays an important role toward an improved receiver design for taking into account channel estimation errors in the presence of IQI at the receiver side. Second , we derive the formulation of an improved and efficient detector under channel estimation errors when IQI is also present only at the receiver side. Third , we generalize our proposed improved detector for the case where IQI is present at both transmitter and receiver sides. The proposed approach is able to reduce the impact of channel uncertainty on the receiver's performance in the presence of IQI. Our results are compared to those achieved by the conventional mismatched ML detector, which uses the imperfect channel as perfect channel and to those obtained by the detector based on the expectation-maximization algorithm. Numerical results over a realistic COST 207 channel model show that significant performance improvement is achieved for bit error rate of the LP stream in the considered DVB-T system.

On quasi‐optimum iterative DF detection for large‐scale MU‐MIMO systems

AbstractThis paper deals with single‐carrier/frequency‐domain equalization for uplink transmission within a broadband multiuser multi‐input–multi‐output system, where a large number of base station antennas (NR) can be adopted, possibly much larger than the number of transmitter antennas (NT) jointly using the same time‐frequency resource at mobile terminals. In this context, we propose low‐complexity iterative decision‐feedback (DF) detection techniques, which can be an interesting alternative to the usually recommended linear detection techniques. Our performance results for a range of quadrature amplitude modulation schemes are discussed with the help of selected performance bounds, with a special attention being devoted to the convergence of the iterative process and to the impact of an imperfect channel estimation. They confirm that simple linear detection techniques, designed to avoid the need of complex matrix inversions, can lead to unacceptably high error floor levels unless NR≫NT. However, it is shown that the combined use of such simple linear detectors and suitable interference cancellation procedures within the proposed class of iterative DF detection techniques can offer a nonnegligible performance advantage over the somewhat more complex (due to the required matrix inversions) linear minimum mean‐squared error detection technique. Moreover, we can achieve close approximations to the appropriate performance bounds, which are able to take into account channel estimation imperfections, even for , provided that is also high enough to ensure iterative DF convergence.

Soft-Output MIMO Detectors with Channel Estimation Error

New expressions for the soft decision bit log-likelihood ratio (LLR) of a MIMO system using quadrature amplitude modulation (QAM) taking into account channel estimation error (CEE). The bit LLR for the maximum likelihood (ML) and the linear minimum mean-squared error (MMSE) receivers are derived, showing in both receivers explicit scaling of the LLR that is a function of the QAM symbol and the CEE variance. These new expressions for the LLRs are used to show that only modest improvements in the link performance are achieved relative to the LLRs that do not take into account the CEE. This indicates that separating the detector design from channel estimation does not significantly impact the system performance, which leads to simplifications in the overall receiver implementation.

Exact Outage Analysis of Underlay Cooperative Cognitive Networks with Reactive Relay Selection Under Imperfect Channel Information

This paper investigates the effect of imperfect channel information on the outage performance of underlay cooperative cognitive networks with reactive relay selection under both primary outage constraint and maximum transmit power constraint. Towards this end, we firstly propose a power allocation scheme for secondary users to meet both constraints and take into account channel estimation error. Then, based on this scheme we propose an exact closed-form outage probability expression for secondary networks. This expression facilitates in quickly assessing the performance behaviour of underlay cooperative cognitive networks with reactive relay selection in different aspects and provides useful insights into the system performance. Various results show considerable system performance degradation due to imperfect channel information, a performance trade-off between the secondary network and the primary network, the performance saturation phenomenon in the secondary network, and the significant performance improvement with respect to the increase in the number of relays.

Massive MIMO Channel-Aware Decision Fusion

In this paper, we provide a study of channel-aware decision fusion (DF) over a “virtual” multiple-input multiple-output (MIMO) channel in the large-array regime at the DF center (DFC). The considered scenario takes into account channel estimation and inhomogeneous large-scale fading between the sensors and the DFC. The aim is the development of (widely) linear fusion rules, as opposed to the unsuitable optimum log-likelihood ratio (LLR). The proposed rules can effectively benefit from performance improvement via a large array, differently from existing suboptimal alternatives. Performance evaluation, along with theoretical achievable performance and complexity analysis, is presented. Simulation results are provided to confirm the findings. Analogies and differences with uplink communication in a multiuser (massive) MIMO scenario are underlined.

Linear Interference Suppression With Covariance Mismatches in MIMO-OFDM Systems

Interference cancellation is a key design concern for next-generation communication systems. One practical approach is the interference rejection combining (IRC) scheme, which is already being considered as the base receiver for next-generation 3GPP specifications. IRC treats interference as a stationary Gaussian process to facilitate simple interference suppression. However, this stationary assumption does not hold in practice; for example, the variances of the pilot signal and of the data signal from the interfering base station (eNB) are quite different. This considerably impacts the possible improvements of an interference-aware receiver. In this paper, novel interference suppression schemes are proposed, which handle separately the interfering pilot and the interfering data signals. The proposed schemes take into account channel estimation errors and the errors in estimating the covariance of interference plus noise. The performance of the proposed schemes is validated by using realistic channel models and asynchronous scenarios for MIMO-OFDM systems.

Comments on “Soft Decision Metric Generation for QAM With Channel Estimation Error”

A log-max generalized bit log-likelihood ratio (GLLR) was derived in an earlier work of Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> for soft decision decoding of quadrature amplitude modulation that takes into account channel estimation error. The results in the above paper show that the GLLR metric outperforms the conventional LLR metric that does not account for channel estimation error. In trying to reproduce the results of the above paper, our simulations indicate that their results were obtained without proper scaling of the transmit constellation. Conversely, through simulations, we demonstrate that by properly scaling the constellation to have unity average energy, the GLLR metric does not result in a noticeable advantage in the bit error rate over the conventional LLR metric for practically meaningful values of the channel estimation error.

Performance analysis of FFH/BPSK system with partial band noise jamming and channel estimation error in high-mobility wireless communication scenarios

In this paper, we analyze the bit error ratio (BER) of fast frequency hopping (FFH) system with binary phase shift keying (BPSK) in high-mobility wireless channels. By taking into account partial band noise jamming and channel estimation errors as Gaussian noise, both of maximal-ratio combining (MRC) and equal-gain combining (EGC) schemes are considered, and exact analytical BER expressions for MRC and EGC schemes are obtained. Especially, a closed-form BER expression for the MRC scheme is also presented in high signal-to-noise ratio region. Analytical and simulation results demonstrate that, the FFH/BPSK schemes suffer from performance degradation in the presence of channel estimation errors. The worst case jamming factor is shown to be inversely proportional to the signal to jamming ratio. With properly designed frequency hopping schemes and channel estimation schemes, the performance improvement is also observed as compared with the traditional noncoherent FFH/BFSK system.

Performance analysis of cooperative diversity networks with imperfect channel estimation over Rician fading channels

In this study, the authors examine the effect of a channel-estimation error on the error and outage probabilities of a multi-relay system with amplify-and-forward relaying over a frequency-flat Rician fading channel. The authors consider orthogonal relaying and study both conventional cooperative systems (i.e. all relays participate in the relaying phase) and opportunistic cooperative systems (i.e. only the best relay participates in the relaying phase). Based on the derivation of an effective signal-to-noise ratio (SNR) at the destination node taking into account channel-estimation error, the authors obtain closed-form expressions for error and outage probabilities in a high SNR regime. Such closed-form solutions are highly desirable because they allow for rapid and efficient evaluation of system performance. Computer simulations are used to validate the authors’ analytical results.

Robust MSE equalizer design for MIMO communication systems in the presence of model uncertainties

This paper considers a robust mean-square-error (MSE) equalizer design problem for multiple-input multiple-output (MIMO) communication systems with imperfect channel and noise information at the receiver. When the channel state information (CSI) and the noise covariance are known exactly at the receiver, a minimum-mean-square-error (MMSE) equalizer can be employed to estimate the transmitted signal. However, in actual systems, it is necessary to take into account channel and noise estimation errors. We consider here a worst-case equalizer design problem where the goal is to find the equalizer minimizing the equalization MSE for the least favorable channel model within a neighborhood of the estimated model. The neighborhood is formed by placing a bound on the Kullback-Leibler (KL) divergence between the actual and estimated channel models. Lagrangian optimization is used to convert this min-max problem into a convex min-min problem over a convex domain, which is solved by interchanging the minimization order. The robust MSE equalizer and associated least favorable channel model can then be obtained by solving numerically a scalar convex minimization problem. Simulation results are presented to demonstrate the MSE and bit error rate (BER) performance of robust equalizers when applied to the least favorable channel model.

Adaptive MIMO-OFDM with Low Signalling Overhead for Unbalanced Antenna Systems

The knowledge of channel state information (CSI) at the transmitter (TX), which in case of time division duplex (TDD) systems can be easily obtained due to radio channel reciprocity, can dramatically increase the spectral efficiency of a multiple-input multiple-output (MIMO) system. This paper presents a robust link adaptation method for TDD systems employing MIMO-OFDM channel eigenmode based signalling. We propose a rather simple logarithm-free, bit and power loading algorithm which requires low signalling overhead. Proposed method maintains constant frame error rate (FER) by controlling the instantaneous transmitted power in such a way that the average SNR at the equalizer output is kept constant. The scheme takes into account channel estimation errors at TX side by partial compensation of the SNR estimation errors. By using a simple scalar channel encoder, for an unbalanced MIMO system with larger number of transmit than receive antennas, the achieved spectral efficiency at low and medium SNR is significantly higher than the outage MIMO capacity with unknown CSI at the TX. The simulation results show that for a constant FER, the throughput degradation compared to the universally accepted Hughes-Hartogs algorithm is negligible.

Analysis of cellular interference for MC‐CDMA and its impact on channel estimation

AbstractWe address the downlink of a cellular multi‐carrier CDMA (MC‐CDMA) system taking into account channel estimation. The system performance in presence of a synchronization mismatch between two interfering base stations (BS) is analyzed in the way that a mobile terminal receives the perfectly synchronized signal from the desired BS as well as the signal from one interfering BS with a synchronization offset. It is demonstrated through simulations that spreading and cell specific random subcarrier interleaving effectively decorrelates the interfering signal, independent of the synchronization offset. Furthermore, the robustness of the channel estimator to cellular interference is examined. Copyright © 2004 AEI

Soft decision metric generation for QAM with channel estimation error

The channel code bit log likelihood ratio (LLR) for soft decision decoding is derived for quadrature amplitude modulated signals (QAM). The effect of imperfect channel knowledge on soft decision decoding performance is studied. Our results indicate this effect increases with channel estimation error and/or QAM modulation level. A metric based on generalized log likelihood ratio (GLLR) is derived for soft decision channel decoding with imperfect channel knowledge. Numerical results show that the GLLR-based metric outperforms the conventional minimum distance decoding metric that does not take into account channel estimation error.