arXiv subjects

Gerhard Fettweis

Publications and source records attributed to Gerhard Fettweis.

At least 55 records · Page 3Linked to original sources

arXiv2021

Unifying Message Passing Algorithms Under the Framework of Constrained Bethe Free Energy Minimization

Variational message passing (VMP), belief propagation (BP) and expectation propagation (EP) have found their wide applications in complex statistical signal processing problems. In addition to viewing them as a class of algorithms operating on graphical models, this paper unifies them under an optimization framework, namely, Bethe free energy minimization with differently and appropriately imposed constraints. This new perspective in terms of constraint manipulation can offer additional insights on the connection between different message passing algorithms and is valid for a generic statistical model. It also founds a theoretical framework to systematically derive message passing variants. Taking the sparse signal recovery (SSR) problem as an example, a low-complexity EP variant can be obtained by simple constraint reformulation, delivering better estimation performance with lower complexity than the standard EP algorithm. Furthermore, we can resort to the framework for the systematic derivation of hybrid message passing for complex inference tasks. Notably, a hybrid message passing algorithm is exemplarily derived for joint SSR and statistical model learning with near-optimal inference performance and scalable complexity.

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arXiv2020

In-phase and Quadrature Chirp Spread Spectrum for IoT Communications

This paper describes a coherent chirp spread spectrum (CSS) technique based on the Long-Range (LoRa) physical layer (PHY) framework. LoRa PHY employs CSS on top of a variant of frequency shift keying (FSK), and non-coherent detection is employed at the receiver for obtaining the transmitted data symbols. In this paper, we propose a scheme that encodes information bits on both in-phase and quadrature components of the chirp signal, and rather employs a coherent detector at the receiver. Hence, channel equalization is required for compensating the channel induced phase rotation on the transmit signal. Moreover, a simple channel estimation technique exploits the LoRa reference sequences used for synchronization to obtain the complex channel coefficient used in the equalizer. Performance evaluation using numerical simulation shows that the proposed scheme achieves approximately 1 dB gain in terms of energy efficiency, and it doubles the spectral efficiency when compared to the conventional LoRa PHY scheme. This is due to the fact that the coherent receiver is able to exploit the orthogonality between in-phase and quadrature components of the transmit signal.

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arXiv2020

Network Massive MIMO Transmission Over Millimeter-Wave and Terahertz Bands: Mobility Enhancement and Blockage Mitigation

Mobility and blockage are two critical challenges in wireless transmission over millimeter-wave (mmWave) and Terahertz (THz) bands. In this paper, we investigate network massive multiple-input multiple-output (MIMO) transmission for mmWave/THz downlink in the presence of mobility and blockage. Considering the mmWave/THz propagation characteristics, we first propose to apply per-beam synchronization for network massive MIMO to mitigate the channel Doppler and delay dispersion effects. Accordingly, we establish a transmission model. We then investigate network massive MIMO downlink transmission strategies with only the statistical channel state information (CSI) available at the base stations (BSs), formulating the strategy design problem as an optimization problem to maximize the network sum-rate. We show that the beam domain is favorable to perform transmission, and demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence performance is further proposed for beam domain power allocation. Numerical results reveal that the proposed network massive MIMO transmission approach with the statistical CSI can effectively alleviate the blockage effects and provide mobility enhancement over mmWave and THz bands.

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arXiv2019

Zero Crossing Modulation for Communication with Temporally Oversampled 1-Bit Quantization

Today's communication systems typically use high resolution analog-to-digital converters (ADCs). However, considering future communication systems with data rates in the order of 100Gbit/s the ADC power consumption becomes a major factor due to the high sampling rates. A promising alternative are receivers based on 1-bit quantization and oversampling w.r.t. the signal bandwidth. Such an approach requires a redesign of modulation, receiver synchronization, and demapping. A zero crossing modulation is a natural choice as the information needs to be carried in the zero crossing time instants. The present paper provides an overview on zero crossing modulation, achievable rates, sequence mapping and demapping, 1-bit based channel parameter estimation, and continuous phase modulation as an alternative zero crossing modulation scheme.

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arXiv2019

Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UAV Links

Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The overhead of additional pilot transmission at the price of spectral efficiency is shown necessary to achieve high resilience in operation. An efficient method to deal with high dynamics of UAVs applies machine learning, particularly Q-learning, to analog beam tracking. The proposed Q-learning-based beam tracking scheme uses current/past observations to design rewards from environments to facilitate prediction, which significantly increases the efficiency of data transmission and beam switching. Given the selected analog beams, the goal of digital beamforming is to maximize the SINR. The received pilot signals are utilized to approximate the desired signal and interference power, which yield the SINR measurements as well as the optimal digital weights. Since the selected analog beams based on the received power do not guarantee the hybrid beamforming achieving the maximization SINR, we therefore reserve additional analog beams as candidates during the beam tracking. The combination of analog beams with their digital weights achieving the maximum SINR consequently provides the optimal solution to the hybrid beamforming.

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arXiv2019

Performance Analysis of a 5G Transceiver Implementation for Remote Areas Scenarios

The fifth generation of mobile communication networks will support a large set of new services and applications. One important use case is the remote area coverage for broadband Internet access. This use case ha significant social and economic impact, since a considerable percentage of the global population living in low populated area does not have Internet access and the communication infrastructure in rural areas can be used to improve agribusiness productivity. The aim of this paper is to analyze the performance of a 5G for Remote Areas transceiver, implemented on field programmable gate array based hardware for real-time processing. This transceiver employs the latest digital communication techniques, such as generalized frequency division multiplexing waveform combined with 2 by 2 multiple-input multiple-output diversity scheme and polar channel coding. The performance of the prototype is evaluated regarding its out-of-band emissions and bit error rate under AWGN channel.

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arXiv2019

Secrecy Energy Efficiency of MIMOME Wiretap Channels with Full-Duplex Jamming

Full-duplex (FD) jamming transceivers are recently shown to enhance the information security of wireless communication systems by simultaneously transmitting artificial noise (AN) while receiving information. In this work, we investigate if FD jamming can also improve the systems secrecy energy efficiency (SEE) in terms of securely communicated bits-per- Joule, when considering the additional power used for jamming and self-interference (SI) cancellation. Moreover, the degrading effect of the residual SI is also taken into account. In this regard, we formulate a set of SEE maximization problems for a FD multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, considering both cases where exact or statistical channel state information (CSI) is available. Due to the intractable problem structure, we propose iterative solutions in each case with a proven convergence to a stationary point. Numerical simulations indicate only a marginal SEE gain, through the utilization of FD jamming, for a wide range of system conditions. However, when SI can efficiently be mitigated, the observed gain is considerable for scenarios with a small distance between the FD node and the eavesdropper, a high Signal-to-noise ratio (SNR), or for a bidirectional FD communication setup.

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arXiv2019

Low-Complexity Iterative Receiver for Orthogonal Chirp Division Multiplexing

This paper proposes a low-complexity iterative receiver for the recently proposed Orthogonal Chirp Division Multiplexing (OCDM) modulation scheme, where we consider a system under frequency-selective channels and constrained to channel state information availability only at the receiver. It has been shown that under these assumptions, OCDM becomes an optimal waveform in terms of performance, i.e., frame error rate (FER), when employing a receiver capable of achieving perfect feedback equalizer (PFE) performance. Thus, this work targets proposing such a receiver for OCDM with low-complexity. Our approach is based on the well accepted minimum mean squared error with parallel interference cancellation (MMSE-PIC), where we derive an approximated equalizer whose complexity is reduced to two fast Fourier transforms (FFTs) per iteration. The FER results reveal that i) the proposed low-complexity receiver perform as good as the original MMSE-PIC, ii) OCDM performs very closely to PFE, and iii) OCDM has approximately 2.5 dB improvement over OFDM.

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arXiv2019

On the Binary Lossless Many-Help-One Problem with Independently Degraded Helpers

Although the rate region for the lossless many-help-one problem with independently degraded helpers is already "solved", its solution is given in terms of a convex closure over a set of auxiliary random variables. Thus, for any such a problem in particular, an optimization over the set of auxiliary random variables is required to truly solve the rate region. Providing the solution is surprisingly difficult even for an example as basic as binary sources. In this work, we derive a simple and tight inner bound on the rate region's lower boundary for the lossless many-help-one problem with independently degraded helpers when specialized to sources that are binary, uniformly distributed, and interrelated through symmetric channels. This scenario finds important applications in emerging cooperative communication schemes in which the direct-link transmission is assisted via multiple lossy relaying links. Numerical results indicate that the derived inner bound proves increasingly tight as the helpers become more degraded.

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arXiv2018

Practical GFDM-based Linear Receivers

The conventional receiver designs of generalized frequency division multiplexing (GFDM) consider a large scale multiple-input multiple-output (MIMO) system with a block circular matrix of combined channel and modulation. Exploiting this structure, several approaches have been proposed for low complexity joint linear minimum mean squared error (LMMSE) receiver. However, the joint design is complicated and inappropriate for hardware implementation. In this paper, we define the concept of GFDM-based linear receivers, which first performs channel equalization (CEq) and afterwards the equalized signal is processed with GFDM demodulator. We show that the optimal joint LMMSE receiver is equivalent to a GFDM-based one, that applies LMMSE-CEq and zero-forcing demodulation. For orthogonal modulation, the optimal LMMSE receiver has an implementation-friendly structure. For the non-orthogonal case, we propose two practical designs that approach the performance of the joint LMMSE. Finally, we analytically prove that GFDM-based receivers achieve equal signal-to-interference-plus-noise ratio per subsymbols within the same subcarrier.

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arXiv2018

Time-Domain Multi-Beam Selection and Its Performance Improvement for mmWave Systems

Multi-beam selection is one of the crucial technologies in hybrid beamforming systems for frequency-selective fading channels. Addressing the problem in the frequency domain facilitates the procedure of acquiring observations for analog beam selection. However, it is difficult to improve the quality of the contaminated observations at low SNR. To this end, this paper uses an idea that the significant observations are sparse in the time domain to further enhance the quality of signals as well as the beam selection performance. By exploiting properties of channel impulse responses and circular convolutions in the time domain, we can reduce the size of a Toeplitz matrix in deconvolution to generate periodic true values of coupling coefficients plus random noise signals. An arithmetic mean of these signals yields refined observations with minor noise effects and provides more accurate sparse multipath delay information. As a result, only the refined observations associated with the estimated multipath delay indices have to be taken into account for the analog beam selection problem.

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arXiv2018

Unified Low Complexity Radix-2 Architectures for Time and Frequency-domain GFDM Modem

Most of the conventional multicarrier waveforms explicitly or implicitly involve a generalized frequency division multiplexing (GFDM)-based modem as a core part of the baseband processing. Some are based on GFDM with a single prototype filter, e.g. orthogonal frequency division multiplexing (OFDM) and others employ multiple filters such as filter bank multicarrier (FBMC). Moreover, the GFDM degrees of freedom combined with multiple prototype filters design allow the development and optimization of new waveforms. Nevertheless, GFDM has been widely considered as a complex modulation because of the requirements of odd number of subcarriers or subsymbols. Accordingly, the current state of the art implementations consume high resources. One solution to reduce the complexity is utilizing radix-2 parameters. Due to the advancement in GFDM filter design, the constraint of using odd parameters has been overcome and radix-2 realization is now possible. In this paper, we propose a unified low complexity architecture that can be reconfigured to provide both time-domain and frequency-domain modulation/demodulation. The design consists of several radix-2 fast Fourier transform (FFT) and memory blocks, in addition to one complex multiplier. Moreover, we provide a unified architecture for the state of the art implementations, which is designed based on direct computation of circular convolution using parallel multiplier chains. As we demonstrate in this work, the FFTbased architecture is computationally more efficient, provides more flexibility, significantly reduces the resource consumption, and achieves similar latency for larger block size.

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arXiv2018

Power Allocation for Massive MIMO-based, Fronthaul-constrained Cloud RAN Systems

Cloud radio access network (C-RAN) and massive multiple-input-multiple-output (MIMO) are two key enabling technologies to meet the diverse and stringent requirements of the 5G use cases. In a C-RAN system with massive MIMO, fronthaul is often the bottleneck due to its finite capacity and transmit precoding is moved to the remote radio head to reduce the capacity requirements on fronthaul. For such a system, we optimize the power allocated to the users to maximize first the weighted sum rate and then the energy efficiency (EE) while explicitly incorporating the capacity constraints on fronthaul. We consider two different fronthaul constraints, which model capacity constraints on different parts of the fronthaul network. We develop successive convex approximation algorithms that achieve a stationary point of these non-convex problems. To this end, we first present novel, locally tight bounds for the user rate expression. They are used to obtain convex approximations of the original non-convex problems, which are then solved by solving their dual problems. In EE maximization, we also employ the Dinkelbach algorithm to handle the fractional form of the objective function. Numerical results show that the proposed algorithms significantly improve the network performance compared to a case with no power control and achieves a better performance than an existing algorithm.

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arXiv2018

How Reliable and Capable is Multi-Connectivity?

Multi-connectivity (MCo) is considered to be a key strategy for enabling reliable transmissions and enhanced data rates in fifth-generation mobile networks, as it provides multiple links from source to destination. In this work, we quantify the communication performance of MCo in terms of outage probability and throughput. For doing so, we establish a simple, yet accurate analytical framework at high signal-to-noise ratio (SNR), in which the number of links, the spectral efficiency, the path loss, and the SNR are incorporated, giving new insights into the potentials of MCo as compared with single-connectivity (SCo). These are our main contributions: (1) finding the exact coding gain of the outage probability for parallel block-fading channels; (2) quantifying the performance improvement of MCo over SCo in terms of SNR gain; and (3) comparing optimal and suboptimal combining algorithms for MCo at the receiver side, namely joint decoding, selection combining, and maximal-ratio combining, also in terms of SNR gain. Additionally, we apply our analytical framework to real field channel measurements and thereby illustrate the potential of MCo to achieve high reliability and high data rates in real cellular networks.

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arXiv2018

Extended GFDM Framework: OTFS and GFDM Comparison

Orthogonal time frequency space modulation (OTFS) has been recently proposed to achieve time and frequency diversity, especially in linear time-variant (LTV) channels with large Doppler frequencies. The idea is based on the precoding of the data symbols using symplectic finite Fourier transform (SFFT) then transmitting them by mean of orthogonal frequency division multiplexing (OFDM) waveform. Consequently, the demodulator and channel equalization can be coupled in one processing step. As a distinguished feature, the demodulated data symbols have roughly equal gain independent of the channel selectivity. On the other hand, generalized frequency division multiplexing (GFDM) modulation also employs the spreading over the time and frequency domains using circular filtering. Accordingly, the data symbols are implicitly precoded in a similar way as applying SFFT in OTFS. In this paper, we present an extended representation of GFDM which shows that OTFS can be processed as a GFDM signal with simple permutation. Nevertheless, this permutation is the key factor behind the outstanding performance of OTFS in LTV channels, as demonstrated in this work. Furthermore, the representation of OTFS in the GFDM framework provides an efficient implementation, that has been intensively investigated for GFDM, and facilitates the understanding of the OTFS distinct features.

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arXiv2018

Optimal Radix-2 FFT Compatible Filters for GFDM

For a linear waveform, a finite condition number of the corresponding modulation matrix is necessary for the waveform to convey the message without ambiguity. Based on the Zak transform, this letter presents an analytical approach to compute the condition number of the modulation matrix for the multi-carrier waveform generalized frequency division multiplexing (GFDM). On top, we further propose a filter design that yields non-singular modulation matrices for an even number of subcarriers and subsymbols, which is not achievable for any previous work. Such new design has significant impact on implementation complexity, as the radix-2 FFT operations for conventional multicarrier waveforms can readily be employed for GFDM. Additionally, we analytically derive the optimal filter that minimizes the condition number.We further numerically evaluate the signal-to-interference ratio (SIR) and noise-enhancement factor (NEF) for matched filter (MF) and zero-forcing (ZF) GFDM receivers for such design, respectively.

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arXiv2018

Hybrid Beamforming Based on Implicit Channel State Information for Millimeter Wave Links

Hybrid beamforming provides a promising solution to achieve high data rate transmission at millimeter waves. Implementing hybrid beamforming at a transceiver based on available channel state information is a common solution. However, many reference methods ignore the complexity of channel estimation for large antenna arrays or subsequent steps, such as the singular value decomposition of a channel matrix. To this end, we present a low-complexity scheme that exploits implicit channel knowledge to facilitate the design of hybrid beamforming for frequency-selective fading channels. The implicit channel knowledge can be interpreted as couplings between all possible pairs of analog beamforming vectors at the transmitter and receiver over the surrounding channel. Instead of calculating mutual information between large antenna arrays, we focus on small-size coupling matrices between beam patterns selected by using appropriate key parameters as performance indicators. This converts the complicated hybrid beamforming problem to a much simpler one: it amounts to collecting different sets of the large-power coupling coefficients to construct multiple alternatives for an effective channel matrix. Then, the set yielding the largest Frobenius norm (or the largest absolute value of the determinant) of the effective channel provides the solution to the hybrid beamforming problem. It turns out that the proposed method does not require information on MIMO channel and can be simply implemented by the received correlated pilot signals that are supposed to be used for channel estimation.

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arXiv2018

Frequency-Selective Hybrid Beamforming Based on Implicit CSI for Millimeter Wave Systems

Hybrid beamforming is a promising concept to achieve high data rate transmission at millimeter waves. To implement it in a transceiver, many references optimally adapt to a high-dimensional multi-antenna channel but more or less ignore the complexity of the channel estimation. Realizing that received coupling coefficients of the channel and pairs of possible analog beamforming vectors can be used for analog beam selection, we further propose a low-complexity scheme that exploits the coupling coefficients to implement hybrid beamforming. Essentially, the coupling coefficients can be regarded as implicit channel state information (CSI), and the estimates of these coupling coefficients yield alternatives of effective channel matrices of much lower dimension. After calculating the Frobenius norm of these effective channel matrices, it turns out that the effective channel having the largest value of the Frobenius norm provides the solution to hybrid beamforming problem.

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