Channel Knowledge Research Articles

NOMA-Aided Multicell Downlink Massive MIMO

Novel user clustering and pilot assignment schemes are proposed for non-orthogonal multiple access (NOMA) aided multicell massive multiple-input multiple-output systems. Our proposed designs leverage the spatial covariance matrices of users, and they are applicable to both overloaded and underloaded cases. Users having linearly dependent covariance matrices are grouped into the same cluster, and they are assigned with orthogonal pilots to mitigate intra-cluster pilot contamination. These pilots are shared among the users in different clusters to reduce the training overhead. Then, the inter-cluster pilot contamination can be mitigated by exploiting asymptotically linearly independent covariance matrices of pilot-sharing users located in different clusters. The achievable downlink rates are derived by capturing the detrimental effects of practical transmission impairments and partial channel knowledge. Our analysis reveals that the user rates are not fundamentally limited by the intra/inter-cluster pilot contamination arising from pilot reuse for the underloaded case. We show that the achievable rate of each user increases without bound under zero-forcing precoding as the number of base-station antennas grows large. However, for the overloaded case, the users, which share the same spatial direction and located within a NOMA cluster, will have to share pilots. Thus, the achievable rate gains must be traded-off to enable massive connectivity. Three transmit power control policies are designed to maximize the sum rate, while achieving max-min user-fairness by mitigating the adverse near-far effect.

Efficient Estimation and Prediction for Sparse Time-Varying Underwater Acoustic Channels

This paper investigates the estimation and prediction of the sparse time-varying channel in underwater acoustic communication systems. The estimation approach exploits the sparse structure of the delay-Doppler representation of the channel. Various state-of-the-art matching pursuit (MP)-type algorithms for sparse signal reconstruction are compared. Among the considered algorithms, sparsity adaptive MP (SaMP) and its variant adaptive step size SaMP have the advantage of not requiring a priori knowledge of the sparsity level and outperform the other algorithms in terms of mean squared error (MSE). Moreover, due to the fast time-varying nature and the extremely limited bandwidth of the UWA channels, a channel prediction that can provide up-to-date channel state information is necessary for reliable symbol detection. This paper proposes an adaptive channel prediction scheme that extrapolates the channel knowledge estimated from a block of training symbols, and the predicted channel is used to decode consecutive data blocks. The proposed scheme does not require any a priori knowledge of channel dynamic model and noise statistics, and is able to provide future channel estimates based solely on current channel estimates. Furthermore, the proposed scheme operates in the delay-Doppler domain, and thus has a remarkably lower computational complexity when compared with the channel prediction in time domain. To further improve the prediction accuracy, past detected symbols are fed back to assist the proposed predictor with an up-to-date channel estimate. Simulation results of the proposed channel estimation and prediction demonstrate a good tradeoff between the MSE/bit error rate and the computational complexity.

Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies

Spatial Modulation Technologies (SMTs) are schemes that reduce inter-carrier interference (ICI), inter-channel interference, inter-antenna synchronization (IAS), and system complexity for multiple-input multiple-output (MIMO) communication systems. Moreover, high spectral and energy efficiency have rendered SMTs attractive to underwater acoustic (UWA) MIMO communication systems. Consequently, this paper focuses on SMTs such as spatial modulation (SM), generalized spatial modulation (GSM), and fully generalized spatial modulation (FGSM) in which one constant number and one multiple number of antennas are active to transmit data symbols in any time interval for underwater acoustic communication (UWAC). In SMTs, the receiver requires perfect channel state information (P-CSI) for accurate data detection. However, it is impractical that the perfect channel knowledge is available at the receiver. Therefore, channel estimation is of critical importance to obtain the CSI. This paper proposes the pilot-based recursive least-square (RLS) adaptive channel estimation method over the underwater time-varying MIMO channel. Furthermore, maximum likelihood (ML) decoder is used to detect the transmitted data and antennas indices from the received signal and the estimated UWA-MIMO channel. The numerical computation of mean square error (MSE) and bit error rate (BER) performance are computed for different SMTs like SM, GSM and FSGM using Monte Carlo iterations. Simulation results demonstrate that the RLS channel estimation method achieves the nearly same BER performance as P-CSI.

Multi-layer beamforming in uplink/downlink massive MIMO Systems with multi-antenna users

In this paper, considering a hybrid architecture at the base station (BS) in an m-MIMO system, multi-antenna users transmit/receive multiple symbols to/from the BS in the uplink/downlink transmissions. In the uplink transmission, we propose a hybrid analog/digital beamformer at the BS and digital precoders at users. The BS designs the analog beamformer via signal-to-leakage-plus-noise ratio (SLNR) maximization criterion. Then, in the baseband, each user designs its precoder maximizing its effective signal power. Finally, the received signal at the BS is digitally combined with a regularized zero-forcing (RZF) filter. For the downlink transmission, we also design the hybrid beamformer at the BS and digital reception filters at users. In such a configuration, in order to design the hybrid beamformer, the digital precoders and the reception filters, we need much less channel knowledge compared to the large dimensional original channel matrices. Besides, as we show in the simulations, in spatially correlated channels, with smaller number of RF chains compared to the number of antennas, the performance of the proposed approaches would achieve the performance of the fully digital beamformer in terms of sum spectral efficiency (SE) and bit error rate (BER). Moreover, applying beamforming techniques at multi-antenna users further improves the overall network throughput.

Decision-Feedback Frequency-Domain Volterra Nonlinear Equalizer for IM/DD OFDM Long-Reach PON

A low-complexity third-order decision-feedback frequency-domain Volterra nonlinear equalizer (DF-FD-VNLE) with superior nonlinearity-compensation performance is proposed and experimentally demonstrated for OFDM long-reach PONs. High optical launch power up to 18 dBm is implemented to mitigate the chromatic dispersion induced power fading and increase the power budget. By reconstructing and subtracting the nonlinear noise in frequency domain with the knowledge of the nonlinear channel, the proposed DF-FD-VNLE outperforms the conventional time-domain VNLE (TD-VNLE) and feed-forward FD-VNLE (FF-FD-VNLE), resulting in better received signal-to-noise ratio (SNR) performance. The nonlinearity-compensation performance of the DF-FD-VNLE can be further improved with the usage of a FF-FD-VNLE or more than one iteration. Complexity and experimental analyses show that similar complexity and higher SNR can be achieved by using the one-iteration DF-FD-VNLE with FD linear equalization (FD-LE), compared with that of the FF-FD-VNLE. Compared with conventional TD-VNLE, the required number of real-valued multiplication (RNRM) of the one-iteration DF-FD-VNLE with FF-FD-VNLE (FD-LE) is reduced by a factor of as much as 82.19% (89.61%) at a memory length of 14 and a truncation factor of 3. Based on the one-iteration DF-FD-VNLE with FF-FD-VNLE, around 53.79 Gb/s single wavelength OFDM IM-DD transmission over 60.8-km SSMF is successfully demonstrated at a BER of 3.8 × 10−3 and a received optical power (ROP) of −2 dBm, achieving 15% capacity improvement compared to the conventional TD-VNLE.

A Complexity-Efficient High Resolution Propagation Parameter Estimation Algorithm for Ultra-Wideband Large-Scale Uniform Circular Array

Millimeter wave (mm-wave) communication with large-scale antenna array configuration is seen as the key enabler of the next generation communication systems. Accurate knowledge of the mm-wave propagation channels is fundamental and essential. In this contribution, a novel complexity-efficient high resolution parameter estimation (HRPE) algorithm is proposed for the mm-wave channel with large-scale uniform circular array (UCA) applied. The proposed algorithm is able to obtain the high-resolution estimation results of the spherical channel propagation parameters. The prior channel information in the delay domain, i.e., the delay trajectories of individual propagation paths observed across the array elements, is exploited, by combining the high-resolution estimation principle and the phase mode excitation technique. Fast initializations, effective interference cancellations, and reduced searching spaces achieved by the proposed schemes significantly decrease the algorithm complexity. Furthermore, the channel spatial non-stationarity in path gain across the array elements is considered for the first time in the literature for propagation parameter estimation, which is beneficial to obtain more realistic results as well as to decrease the complexity. A mm-wave measurement campaign at the frequency band of 28-30GHz using a large-scale UCA is exploited to demonstrate and validate the proposed HRPE algorithm.

Improving adaptive receivers performance in molecular communication via diffusion.

Forthcoming applications for molecular communications (MC) such as drug-delivery and health monitoring will require robust receiver capabilities to mitigate channel memory and inter symbol interference caused by previous transmitted symbols. Here, the authors introduce an adaptive weighted algorithm to reduce the influence of these factors. This novel signal detection is deployed on to a concentration-based MC system with absorbing receiver which is based on the so-called first passage time concept. The proposed detector has low complexity and does not require explicit channel knowledge. To evaluate authors' proposed algorithm, a theoretical approach is developed to derive the bit error rate (BER). Numerical results also carried out to verify the accuracy of these formulations and establish that the new detector will achieve better performance in comparison with other common low-complex detectors under certain scenarios. Additionally, the authors propose a simple pre-coding technique to combat the sequence of consecutive ones in low ISI scenarios. Also a comparison between detectors is given, which is based on the variation of distance, symbol period, signal-to-noise ratio (SNR), and number of molecules.

Iterative Approaches for Massive MIMO Uplink Processing Under Imperfect Channel Conditions

Obtaining channel state information in massive multiple-input multiple-output (MIMO) systems is challenging because of significant multiple-access interference and very limited resources available for channel estimation. Most existing equalization schemes for massive MIMO systems assume perfect channel knowledge at the receivers. When channel estimation error is present, however, significant performance degradation will be experienced, which has been demonstrated in the literature. In this paper, we model the maximum-likelihood channel estimation as the sum of real channel and random channel estimation error, and develop a minimum mean-square error (MMSE) based turbo equalization method conditioned on the channel estimate. We demonstrate that our newly developed iterative processing approach is a general expression, whereas existing methods can be viewed as special cases. We further derive an MMSE-sorted QR decomposition (MMSE-SQRD) based turbo equalization to further improve the performance for massive MIMO systems under large user numbers and severe channel estimation error conditions. Numerical results and performance comparison show that the proposed MMSE-based turbo equalization scheme greatly outperforms the existing schemes. With a large number of users being simultaneously served, our proposed MMSE-SQRD-based scheme can further enhance system performance over the MMSE-based turbo equalization scheme.

Adaptive feedback bits and power allocation for dynamic TDD systems

In next-generation wireless communication systems, a dynamic time division duplex (TDD) system is promising to support simultaneous uplink (UL) and downlink (DL) transmissions. We herein consider a fixed number of feedback bits per DL user, where each DL user reports its quantized channel direction information (CDI) of the interference channel to a serving DL base station via its dedicated feedback channel. Based on the quantized CDI feedback, we design the UL and DL transmit zero-forcing beamforming (ZFBF) to mitigate the interference signals caused by the coexistence of the UL and DL transmissions. To maximize the DL data rate, the challenge of interference cancellation lies in how many feedback bits are adaptively allocated for the transmission of the quantized CDI of each channel link. This paper proposes an adaptive feedback bits allocation scheme which minimizes the mean rate loss between perfect channel knowledge and limited feedback system. We also propose the UL transmit power allocation scheme under the interference power constraint from the UL user to the DL user when the number of feedback bits for the quantized CDI is given. To jointly solve the feedback bits allocation and the UL transmit power allocation problems, we use an iterative scheme to maximize the DL data rate while satisfying the constraint of interference power. Finally, we investigate how many feedback bits per DL user are required to maintain a constant mean rate loss. The simulation results show that the proposed adaptive feedback bits allocation scheme significantly increases the DL data rate compared with the conventional schemes.

Max–Min Fairness Optimal Rate-Energy Trade-Off of SWIPT for Massive MIMO Downlink

Max–min fairness optimal rate-energy trade-off of simultaneous wireless information and power transfer is derived with imperfectly estimated channels at the massive multiple-input multiple-output base-station and statistical channel knowledge at the users. The optimal transmit power control coefficients, common harvested energy, and common achievable user rates are derived for time-switching and power-splitting protocols.

Ivabradine for the Treatment of Cardiovascular Diseases.

Higher heart rate (HR) is independently related to worse outcomes in various cardiac diseases, including hypertension, coronary artery disease, and heart failure (HF). HR is determined by the pacemaker activity of cells within the sinoatrial node. The hyperpolarization-activated cyclic nucleotide-gated (HCN) 4 channel, one of 4 HCN isoforms, generates the If current and plays an important role in the regulation of pacemaker activity in the sinoatrial node. Ivabradine is a novel and only available HCN inhibitor, which can reduce HR and has been approved for stable angina and chronic HF in many countries other than Japan. In this review, we summarize the current knowledge of the HCN4 channel and ivabradine, including the function of HCN4 in cardiac pacemaking, the mechanism of action of If inhibition by ivabradine, and the pharmacological and clinical effects of ivabradine in cardiac diseases as HF, coronary artery disease, and atrial fibrillation.

Multi-IF-Over-Fiber Based Mobile Fronthaul With Blind Linearization and Flexible Dispersion Induced Bandwidth Penalty Mitigation

The nonlinear degradations and chromatic dispersion induced bandwidth penalty are critical obstacles for implementing the analog radio-over-fiber (A-RoF) technique in future mobile fronthaul (MFH). In this paper, we propose an analog multiple intermediate-frequency-over-fiber (multi-IFoF) MFH system with blind linearity improvement and flexible dispersion-induced bandwidth penalty mitigation. The bias adjustment of a single-electrode driven dual-drive Mach–Zehnder modulator is used to compensate the bandwidth penalty arising from the dispersion-induced power fading of fiber links in different lengths. This operation can increase the available bandwidth for the MFH system free of even-order intermodulation distortions (IMDs). At the receiver, a self-adaptive digital post-processing algorithm is introduced to blindly improve the linearity of the IFoF MFH system by suppressing the residual odd-order IMDs, with no need of training signals or prior knowledge of the transmission channel. In the proof-of-concept experiments, the transmission of 64-QAM-OFDM signals in 1 to 12 IF channels with a 500-MHz bandwidth has been demonstrated over standard single-mode fibers (SSMFs) of 20, 50, and 100 km. For the 50-km (or 100-km) SSMF, the bandwidth penalty for a broadband signal with a bandwidth of 4.5 GHz (or 2.5 GHz) is fully compensated. In addition, the proposed post-processing algorithm can significantly suppress the odd-order IMDs and thus improve the linearity by greater than 5.4 dB in terms of the input RF power range which retains an acceptable error-vector-magnitude (EVM) performance for the 64-QAM-OFDM signal.

A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments.

Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.

Security-Enhanced SC-FDMA Transmissions Using Temporal Artificial-Noise and Secret Key Aided Schemes

We investigate the physical-layer security of uplink single-carrier frequency-division multiple-access (SC-FDMA) systems. Multiple users, Alices, send confidential messages to a common legitimate base-station, Bob, in the presence of an eavesdropper, Eve. To secure the legitimate transmissions, each user superimposes an artificial noise (AN) signal on the time-domain SC-FDMA data symbol. We reduce the computational and storage requirements at Bob's receiver by assuming simple per-sub-channel detectors. We assume that Eve has global channel knowledge of all links in addition to high computational capabilities, where she adopts high-complexity detectors such as single-user maximum likelihood (ML), multi-user minimum-mean-square-error, and multi-user ML. We analyze the correlation properties of the time-domain AN signal and illustrate how Eve can exploit them to reduce the AN effects. We prove that the number of useful AN streams that can degrade Eve's signal-to-noise ratio is dependent on the channel memories of Alices-Bob and Alices-Eve links. Furthermore, we enhance the system security for the case of partial Alices-Bob channel knowledge at Eve, where Eve only knows the precoding matrices of the data and AN signals instead of knowing the entire Alices-Bob channel matrices, and propose a hybrid security scheme that integrates temporal AN with channel-based secret key extraction.

La defensa de la legitimidad durante el reinado de Pedro I de Castilla. Monarquía y comunicación política en contextos de conflicto

The study of the discourses of legitimacy developed during the reigns of Enrique II and Juan I of Castile has deepened knowledge of the political communication channels, instruments and messages used by the Castilian monarchs in the second half of the 14th century. However, the lack of literature on this subject in the previous reign can lead to conclude that Pedro I’s government was characterized by an absence of communication between the king and the kingdom. By reading the sources preserved it can be proved that this is a mistaken notion. In fact, the analysis of the monarch’s answers in the face of the attacks reveals the relevance given by the period’s monarchy to the dialogue.

Development of a Potentially Hazardous Pro-Glacial Lake in Aksay Valley, Kyrgyz Range, Northern Tien Shan

Debris flows caused by glacial lake outburst floods (GLOFs) are common hazards in mountain environments. The risk posed by glacial lake outburst hazards is particularly evaluated where the lower reaches of catchments are populated. A potentially dangerous lake has been identified adjacent to the Uchitel Glacier in Northern Tien Shan. This lake formed between 1988 and 1994 on the site of a retreated glacier in the upper part of the Aksay Valley. In this study we consider the possibility of an outburst of this pro-glacial lake in the future. The study involved bathymetry mapping of the lake, detailed profile sections of the valley, flow rate measurements on the Aksay river, and monitoring of the lake development using satellite images. Modelling of secondary debris flow inundation heights and hazard footprints has been undertaken. The outburst of this lake could cause powerful debris flows posing a threat to permanent residents living downstream, in the Ala-Archa Valley. Monitoring of the lake over the past ten years suggests certain changes in the runoff to the subsurface, and an increase in lake depth is observed. Glacial lakes with subsurface drainage are considered to be the most hazardous type as the knowledge of drainage channels functioning is still very limited and, thus, the timing of an outburst is hard to predict. Development of monitoring approaches to support forecasting of these hazards is of paramount importance to safety in mountain territories globally.

Online Power Control Based on Lyapunov Optimization Framework for Decode-and-Forward Relay Systems With Energy Harvesting

This paper studies the power control in a two-hop decode-and-forward relay system over fading channel. The relay node is an energy harvesting node and is equipped with a data buffer and a rechargeable battery with finite capacity. Under the condition that any prior and statistical knowledge about the harvested energy and channel fading is unknown, based on the Lyapunov optimization framework, an online power control strategy for the source node and the relay node is proposed to maximize the average transmission rate and minimize the average energy consumption of the source node. First, the optimization problem is decomposed into two sub-problems: minimizing the average energy consumption of the source node and maximizing the average transmission rate of the relay node. Then, we use the Lyapunov optimization framework to transform the minimization and maximization problems to the minimization of the upper bounds of two “drift-plus-penalty”s and solve them. The solution of the optimization problem only relies on the current channel coefficient, the state of the data buffer, the energy level of the battery in the current time slot, and the average transmission rate of the past time slots. Only a few calculations are needed to get the optimal power of the source node and the relay node. Finally, simulation is done to verify the performance of the proposed algorithm. The simulation results show that the transmission rate difference between the proposed algorithm and the ideal offline water filling algorithm is small, which has full knowledge of energy and channels in advance and is without the constraints of the causality of energy and data, and the overflow of the battery and data buffer. The proposed algorithm is greatly superior to the online greedy algorithm and the semi-greedy algorithm.

Secure Hybrid Digital and Analog Precoder for mmWave Systems With Low-Resolution DACs and Finite-Quantized Phase Shifters

Millimeter wave (mmWave) communication has been regarded as one of the most promising technologies for the future generation wireless networks because of its advantages of providing a ultra-wide new spectrum and ultra-high data transmission rate. To reduce the power consumption and circuit cost for mmWave systems, hybrid digital and analog (HDA) architecture is preferred in such a scenario. In this paper, an artificial-noise (AN) aided secure HDA beamforming scheme is proposed for mmWave MISO system with low resolution digital-to-analog converters (DACs) and finite-quantized phase shifters on RF. The additive quantization noise model for AN aided HDA system is established to make an analysis of the secrecy performance of such systems. With the partial channel knowledge of eavesdropper available, an approximate expression of secrecy rate (SR) is derived. Then using this approximation formula, we propose a two-layer alternately iterative structure (TLAIS) for optimizing digital precoder (DP) of confidential message (CM), digital AN projection matrix (DANPM) and analog precoder (AP). The inner-layer iteration loop is to design the DP of CMs and DANPM alternatively given a fixed matrix of AP. The outer-layer iteration loop is in between digital baseband part and analog part, where the former refers to DP and DANPM, and the latter is AP. Then for a given digital part, we propose a gradient ascent algorithm to find the vector of AP vector. Given a matrix of AP, we make use of general power iteration (GPI) method to compute DP and DANPM. This process is repeated until the terminal condition is reached. Simulation results show that the proposed TLAIS can achieve a better SR performance compared to existing methods, especially in the high signal-to-noise ratio region.

Empirical Rates Characterization of Wearable Multi-Antenna Terminals for First-Responders

Empirical characterization of the achievable rates for a wearable multi-antenna terminal shows the potential advantages of deploying a large number of antennas at the user end. We focus on the challenges and requirements of the broadband communication in future emergency communication systems, specifically addressing the outdoor–to–indoor propagation scenario, where the first responder is within an underground area such as a garage or basement. The measurement campaign undertaken characterizes the flat fading multiple-input multiple-output (MIMO) channel matrices at 3.5 GHz for a maximum of $M=30$ antennas deployed at the base station (BS), and $N=12$ wearable antennas at the user. The achievable rates are obtained for two transmission strategies that account for the different levels of channel knowledge. In both cases, all the MIMO processing is carried out at the BS.

Distributed Coordinated Transmission with Forward-Backward Training for 5G Radio Access

CoMP transmission and reception have been considered in cellular networks for enabling larger coverage, improved rates, and interference mitigation. To harness the gains of coordinated beamforming, fast information exchange over a backhaul connecting the cooperating BSs is required. In practice, the bandwidth and delay limitations of the backhaul may not be able to meet such stringent demands. These impairments motivate the study of cooperative approaches based only on local CSI that require minimal or no information exchange between the BSs. To this end, several distributed approaches are introduced for CB-CoMP. The proposed methods rely on the channel reciprocity and iterative spatially precoded over-the-air pilot signaling. We elaborate how F-B training facilitates distributed CB by allowing BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured CSI. The trade-off due to the overhead from the F-B iterations is discussed. We also consider the challenge of dynamic TDD where the UE-UE channel knowledge cannot be acquired at the BSs by exploiting channel reciprocity. Finally, standardization activities and practical requirements for enabling the proposed F-B training schemes in 5G radio access are discussed.