Future industrial shopfloors will feature a large number of autonomous, mobile devices that communicate wirelessly. In such controlled environments, collecting channel state information (CSI) by location and reusing it over time may reduce the overhead of channel sounding and CSI feedback that is required for channel-aware radio resource allocation. However, even if the trajectories of devices repeat over time, temporary variations of the environment put a question mark behind the usability of radio environment maps (REMs) from the past. To address this important question, we designed and carried out a channel measurement campaign in an industrial-like area, we evaluated the data regarding the consistency of the radio channel in a time-varying environment, and we conducted simulations of a multi-user communications scenario based on measured data to present a potential application of channel information reuse. The measurement results show a high similarity of the REM over time and a spatially limited impact of the temporary variations caused by a metallic object on the REM. Our simulation results show up to 75% savings in CSI feedback overhead while the reliability of the communications system remains almost unaffected. In the future, we will elaborate on the estimation of the REM in a time-varying environment.

The sixth generation of wireless networks (6G) is expected to support the deployment of Internet of things (IoT) devices in massive scales. Finding lightweight and decentralized secret-key distribution primitives is therefore a challenge. Secret-key generation (SKG) from wireless channel coefficients is seen as a possible solution. It allows the extraction of secret keys using the channel randomness observed at the physical layer, without a centralized key distribution server. In this work an SKG approach suitable for wideband IoT devices is proposed. We investigate a filterbank-based SKG method, in which secret bits are generated through power measurements over different frequencies. To minimize dependencies and correlation among frequencies the quantile and the Karhunen-Loeve transforms are used. Finally, we perform a numerical evaluation of the achievable SKG rates, in the form of mutual-information (MI) estimates, using 3GPP channel models. Our numerical evaluation shows that the achievable SKG rate depends on the channel statistics, and, hence, to optimally harvest the information, devices need to be channel-aware.

This work proposes an architecture and platform for researching and demonstrating use-cases for connected cars based on small-scale vehicles. The proposal bridges the gap between a lab setup to test individual algorithms and deployments on real cars. It allows researchers to communicate their results with a small-scale indoor demonstrator. The platform employs ROS2 and MicroROS to allow for a modular and scalable hardware and software setup. Moreover, it allows running all control algorithms in a graphical simulation to ease development of complex scenarios. We successfully apply the platform to build a demonstrator for a platooning use-case and point out limitations such as lacking photorealism of the simulation and limited processing power of the platform. Our results indicate that using a well-designed platform and architecture can significantly reduce required effort for implementing connected cars use-cases.

Motivated by 5G application requirements that challenge the use of orthogonal frequency division multiplexing (OFDM), non-orthogonal multi-carriers are being investigated. Unlike OFDM that takes advantage of orthogonal pilot observation, in non-orthogonal waveforms, pilots are contaminated by interference from multiple dimensions, i.e., inter-subsymbol-, inter-carrier-, and inter-antenna-interference, when multiple-input-multiple-output (MIMO) is also part of the transmission. Employing cyclic-prefix (CP) in multi-carrier systems not only protects the signal from inter-symbol-interference but also allows circular interpretations of the channel, which simplifies the estimation and equalization techniques. Nevertheless, the CP information is usually discarded at the receiver side. In this paper, by considering the fact that non-orthogonal waveforms suffer from multiple dimensions of interference, we derive a MIMO linear-minimum-mean-squared-error (LMMSE)-based parallel-interference-cancellation (PIC) method for joint channel estimation and equalization of non-orthogonal waveforms. Unlike the common practice, by properly localizing the pilots in time domain, we also use the pilots' information from CP. We apply our proposed algorithm to a flexible non-orthogonal waveform known as generalized frequency division multiplexing (GFDM). Taking advantage of block-circularity of GFDM, we investigate the complexity aspects for such CP-aided LMMSE-PIC channel estimation. Through simulation results, we show that using CP information of pilots for GFDM gains up to 2.4-dB better frame error rate performance than an OFDM signal.

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 demodulation 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.

Driven by 5G requirements, research on alternatives to the popular cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) waveform recently arose. In particular, non-orthogonal circularly filtered waveforms such as generalized frequency division multiplexing (GFDM) were proposed due to flexibility and robustness. Applying multiple-input multiple-output (MIMO) techniques for future wireless networks are unquestionable and thereby compulsory for any alternative waveform. Despite advancements in accurate MIMO detection algorithms for GFDM, compared with CP-OFDM their complexity still exhibited a higher order of magnitude, impeding an energy-efficient implementation. In this paper, we propose a low-complexity formulation for iterative minimum mean squared error with parallel interference cancellation (MMSE-PIC) detection for non-orthogonal waveforms with localized inter-carrier interference, where we focus on the application to MIMO-GFDM. The proposal achieves complexity similar to CP-OFDM and we evaluate its performance under realistic channel conditions with imperfect channel state information, where we obtain up to 2-dB gain of GFDM compared with OFDM. We confirm our findings by analyzing the measured extrinsic information transfer charts and show that the proposal achieves the performance of optimal maximum likelihood detection. The results point out the MMSE-PIC algorithm as a viable technique for iterative MIMO receiver implementations for non-orthogonal waveforms.

Generalized Frequency Division Multiplexing (GFDM) is a flexible non-orthogonal waveform. Due to its flexibility it can be served as a framework to emulate diverse multi-carrier waveforms including orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain equalization (SC- FDE). Nevertheless, inter-symbol- and inter-carrier- interference may arise in GFDM if the filter roll-off factor is larger than zero. In multiple-input multiple-output (MIMO) scenarios, also inter-antenna- interference further challenges the receiver design. In this paper, we focus on pilot-aided channel estimation for GFDM. In contrast to our prior works, we propose a technique to insert the pilot symbols in a manner such that they are orthogonal to the data symbols in the frequency domain. Based on this design, frequency-domain channel estimation algorithms initially developed for OFDM become straightforwardly applicable. We also examine the impact of such pilot design on the signal properties, including power spectral density (PSD) and peak-to- average-power ratio (PAPR). At the end of the paper, the performance of a MIMO-GFDM system is investigated and compared with the conventional MIMO-OFDM systems.

This paper studies the link level performance of orthogonal frequency division multiplexing (OFDM) and four other advanced waveforms, namely, filtered OFDM (F-OFDM), universal-filtered OFDM (UF-OFDM), filter bank multicarrier (FBMC) and generalized frequency division multiplexing (GFDM). Compared to OFDM, the two filtered variants achieve lower out-of-band (OOB) emissions and can mostly preserve the conventional OFDM-based transceiver design. For the latter two non-orthogonal waveforms, this paper proposes a low complexity implementation of minimum mean square error equalization to jointly tackle the channel and waveform-induced interference. On this basis, the benefits of FBMC and GFDM can be exploited with complexity comparable to the former (quasi-) orthogonal waveforms. The observed benefits include lower peak-to-average power ratio (PAPR) and smaller frame error rate (FER) under challenging doubly dispersive multiple-input multiple-output (MIMO) fading channels. Additionally, linear filtering of FBMC offers an ultra-low OOB emission, while a good compromise in the usage of time and frequency resources can be achieved by circular filtering of GFDM. In the comparison of offset quadrature amplitude modulation (OQAM) versus QAM for non-orthogonal waveforms, OQAM can offer lower PAPR, while smaller FERs can be achieved by QAM in rich multipath fading channels.

New waveform candidates are being investigated for the fifth generation wireless systems. Among the promising candidates, generalized frequency division multiplexing (GFDM) offers the flexibility to address a wide range of requirements (e.g. low latency, coarse synchronization, etc.). Due to the non- orthogonality of GFDM, the transmit signal subjects to inter-symbol and inter-carrier interference. In this paper, the problem of GFDM channel estimation with the aid of reference signals (pilots) is investigated. In GFDM, the receive signal is a combination of pilots, data and the noise part. Hence, when utilizing the conventional estimation techniques, degradation of channel estimation performance due to interference from data symbols further challenges the receiver design for GFDM. We show that if we employ multiple pilots per subcarrier within a single GFDM block, different pilot patterns have significant impact on the resulting interference term and thus, the quality of the channel estimation in GFDM. Such results are then compared with the performance of channel estimation in orthogonal frequency division multiplexing (OFDM) which takes advantage of clear pilot observation.

Upcoming fifth generation (5G) cellular networks will demand more from the physical layer (PHY) than current- generation Orthogonal Frequency Division Multiplexing (OFDM) can deliver. The 5G waveform candidate Universal Filtered OFDM (UF-OFDM) is designed to provide the flexibility required for future applications. However, the introduction of subband filters in UFMC can increase implementation complexity and low-complexity solutions need to be found. State-of-the-art technologies provide an algorithm that performs shorter-length FFTs that can reduce complexity to two to ten times that of OFDM (depending on the allocation sizes), at the cost of only approximating the exact UFMC signal. In this paper we propose a new approximation of the UFMC signal which bases on the similarity of adjacent subcarriers that can be implemented with reduced number of operations. Analysis show that the system can be implemented with only 20% more operations than standard OFDM when accepting some increase in the subband bandwidth. A more accurate solution can be implemented at roughly 3.6 times OFDM complexity. The results can reduce implementation costs for future mobile devices.

The coded modulation (CM) capacity represents the maximum achievable data rate for a CM scheme assuming optimal decoding at the receiver. It is an important analytical tool, providing theoretic limits for near-optimum transceiver design. Next generation wireless communications systems with the use of new technologies, such as massive antennas and nonorthogonal waveforms, tend to be large scale. However, the conventional ways developed for evaluating the CM capacity of small-scale systems expose exponential complexity with respect to the system's input dimension. Therefore, they become infeasible when the input dimension increases by one or two orders of magnitude in large-scale systems of interest. This letter resorts to a lower and upper bound of the CM capacity, allowing for a computationally efficient evaluation with polynomial complexity. In particular, two message passing algorithms, namely expectation propagation (EP) and variational message passing (VMP), are applied to evaluate the bounds. Two applications are examined in the end. The presented results bring valuable information about the system design, allowing one to evaluate the impact of suboptimal implementation in the overall system performance.

Generalized frequency division multiplexing (GFDM) as a nonorthogonal waveform aims at diverse applications in future mobile networks. To evaluate its performance, its capacity limits are of particular importance. Therefore, this paper analyzes its constellation-constrained capacities for cases where the channel state information (CSI) is unknown at the transmitter and perfectly known at the receiver. In frequency selective channels, GFDM may provide advantage over the conventional orthogonal frequency division multiplexing (OFDM) scheme. In order to achieve near-capacity performance, the interaction of data symbols in time and frequency combined with multiple antennas (MIMO) challenges the design of GFDM receivers. This paper, therefore, applies expectation propagation (EP) for systematic receiver design. It is shown that the resulting iterative MIMO-GFDM receiver with affordable complexity can approach optimum decoding performance and outperform MIMO-OFDM in a rich multipath environment. Simulations are also used to illustrate the impact of channel delay spread on the constellation-constrained capacities and on the performance of the novel receiver algorithm.

Within the framework of Monte Carlo simulation, this paper derives a Markov chain Monte Carlo (MCMC) algorithm for efficient detection in multiple-input multiple-output (MIMO) systems using the non-orthogonal multi-carrier waveform termed generalized frequency division multiplexing (GFDM). The proposed MCMC algorithm performs the detection task in frequency domain. Its adopted proposal distribution and Gibbs sampler are tailored under the consideration of complexity and latency for tackling the three-dimensional interference involved in the received signal, i.e., inter-carrier, inter-symbol and inter-antenna interference. By means of simulation, its decoding performance is compared with that achieved by employing the sphere decoding algorithm in a conventional orthogonal frequency division multiplexing (OFDM) based MIMO system. For multi-path fading channels with strong frequency selectivity, the MCMC algorithm proposed for the MIMO-GFDM system can deliver superior performance.

For upcoming 5G networks, new challenges are posed on the physical layer, which go beyond increased data rate. Generalized Frequency Division Multiplexing (GFDM) is proposed as a candidate waveform to combat these challenges. However, inherent self-interference between subcarriers of GFDM hinders the application of standard spatial multiplexing (SM) detection algorithms. We present an algorithm that combines maximum likelihood and successive interference cancellation detection techniques that allows to exploit the inherent frequency diversity of GFDM coming from self-interference. Computer simulations reveal that the proposal outperforms OFDM in terms of symbol error rate in fading multipath channels. These findings prove self- interference to be beneficial and that SM can be successfully applied to GFDM.

A low-complexity algorithm for calculation of LMMSE filter coefficients for Generalized Frequency Division Multiplexing (GFDM) in a fading multipath environment is derived. The simplification is based on the block circularity of the involved matrices. The proposal reduces complexity from cubic to squared order. The proposed approach can be generalized to other waveforms with circular pulse shaping.

This paper presents a solution for achieving transmit diversity with generalized frequency division multiplexing (GFDM). Compared to previous works, the proposed solution significantly improves symbol error rate (SER) performance and latency, where both aspects are crucial for future 5G cellular networks. It is shown that widely linear estimation at the receiver side can jointly equalize and demodulate the space-time encoded GFDM signal. Moreover, maximum ratio combining can further increase the SER performance with multiple receive antennas. SER performance is evaluated in Rayleigh fading multipath channels.

Cellular systems of the fourth generation (4G) have been optimized to provide high data rates and reliable coverage to mobile users. Cellular systems of the next generation will face more diverse application requirements: the demand for higher data rates exceeds 4G capabilities; battery-driven communication sensors need ultra-low power consumption; and control applications require very short response times. We envision a unified physical layer waveform, referred to as generalized frequency division multiplexing (GFDM), to address these requirements. In this paper, we analyze the main characteristics of the proposed waveform and highlight relevant features. After introducing the principles of GFDM, this paper contributes to the following areas: 1) the means for engineering the waveform's spectral properties; 2) analytical analysis of symbol error performance over different channel models; 3) concepts for MIMO-GFDM to achieve diversity; 4) preamble-based synchronization that preserves the excellent spectral properties of the waveform; 5) bit error rate performance for channel coded GFDM transmission using iterative receivers; 6) relevant application scenarios and suitable GFDM parameterizations; and 7) GFDM proof-of-concept and implementation aspects of the prototype using hardware platforms available today. In summary, the flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks.