The unified 3D communication networks, integrating standard terrestrial mobile communication networks and non-terrestrial networks (NTNs), are seen as the key enabler for global connectivity in the next generation (6G) wireless communications. To achieve this goal, new technologies and components are needed in order to meet the requirements for the 6G networks in terms of higher data rates, and enhanced reliability, security and network reconfigurability. This work introduces the German project 6G-TakeOff, aimed at the design of solutions for unified 3D networks for 6G wireless communication systems. The project consortium brings together academic and industrial partners from Germany and Europe, covering the entire value chain from design of electro-nics to applications. This work presents the key hardware components required for 3D networks and the concept for demonstration of their functionality.

We focus on a (generic) joint source-channel coding problem, appearing in a broad variety of real-world application. Explicitly, a noisy observation from a user/source signal should be compressed, ahead of getting forwarded over an error-prone and rate-limited channel to a remote processing unit. The design problem shall be formulated in a fashion that the impacts of the forward link are taken into account. Aligned with the Information Bottleneck (IB) method, we consider the Mutual Information (MI) as the fidelity criterion, and work out a data-driven approach to tackle the underlying design problem based upon a finite sample set. For that, we derive a tractable variational lower-bound of the objective functional, and present a general learning architecture which can be used to optimize the given lower-bound by standard training of the encoder and decoder Deep Neural Networks. This approach that is, principally, based upon the (generative) latent variable models, extends the concepts of Variational AutoEncoder (VAE) and Deep Variational Information Bottleneck (Deep VIB) for (remote) source coding to the context of joint source-channel coding. We validate the effectiveness of our approach by several numerical simulations over typical transmission scenarios.

Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications. Space-Division Multiple Access (SDMA) precoding is a method that mitigates interference among satellite beams, boosting spectral efficiency. While optimal SDMA precoding solutions have been proposed for ideal channel knowledge in various scenarios, addressing robust precoding with imperfect channel information has primarily been limited to simplified models. However, these models might not capture the complexity of LEO satellite applications. We use the Soft Actor-Critic (SAC) deep Reinforcement Learning (RL) method to learn robust precoding strategies without the need for explicit insights into the system conditions and imperfections. Our results show flexibility to adapt to arbitrary system configurations while performing strongly in terms of achievable rate and robustness to disruptive influences compared to analytical benchmark precoders.

The uplink from a very small aperture terminal (VSAT) towards multiple satellites is considered, in this paper. VSATs can be equipped with multiple antennas, allowing parallel transmission to multiple satellites. A low-complexity precoder based on imperfect positional information of the satellites is presented. The probability distribution of the position uncertainty and the statistics of the channel elements are related by the characteristic function of the position uncertainty. This knowledge is included in the precoder design to maximize the mean signal-to-leakage-and-noise ratio (SLNR) at the satellites. Furthermore, the performance w.r.t. the inter-satellite distance is numerically evaluated. It is shown that the proposed approach achieves the capacity for perfect position knowledge and sufficiently large inter-satellite distances. In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.

Systems of small distributed satellites in low Earth orbit (LEO) transmitting cooperatively to a multiple antenna ground station (GS) are investigated. These satellite swarms have the benefit of much higher spatial separation in the transmit antennas than traditional big satellites with antenna arrays, promising a massive increase in spectral efficiency. However, this would require instantaneous perfect channel state information (CSI) and strong cooperation between satellites. In practice, orbital velocities around 7.5 km/s lead to very short channel coherence times on the order of fractions of the inter-satellite propagation delay, invalidating these assumptions. In this paper, we propose a distributed linear precoding scheme and a GS equalizer relying on local position information. In particular, each satellite only requires information about its own position and that of the GS, while the GS has complete positional information. Due to the deterministic nature of satellite movement this information is easily obtained and no inter-satellite information exchange is required during transmission. Based on the underlying geometrical channel approximation, the optimal inter-satellite distance is obtained analytically. Numerical evaluations show that the proposed scheme is, on average, within 99.8 % of the maximum achievable rate for instantaneous CSI and perfect cooperation.

The problem of finding good error correcting codes for short block lenghts and its corresponding decoders is an open research topic. A frequently applied soft decoder is the Belief Propagation (BP) decoder, however with degraded performance in case of short loops in the Tanner graph. This is especially problematic for short length codes as loops of small length are more likely to occur. In this paper, we propose the Machine Learning Scaled Belief Propagation (MLS-BP) to mitigate the performance loss of BP decoding for short length codes by introducing a learned scaling factor for the receive signals. The key point of this approach is the fact that the implementation of the BP decoder is not changed and the simple scaling leads to performance results comparable to other proposed BP improvements.

Consider the following setup: Through ajointdesign, multiple observations of a remote data source shall belocallycompressed before getting transmitted via severalerror-prone, rate-limited forward links to a (distant) processing unit. For addressing this specific instance of multiterminalJoint Source-Channel Codingproblem, in this article, the foundational principle of theInformation Bottleneckmethod is fully extended to obtain purely statistical design approaches, enjoying theMutual Informationas their fidelity criterion. Specifically, the forms of stationary points for two types of distributed compression schemes are characterized here. Subsequently, those acquired solutions are utilized as the centerpiece of the proposed generic, iterative algorithm, termed theMultiterminal Forward-Aware Vector Information Bottleneck (M-FAVIB), for addressing the design optimizations. Leveraging an unfolding trick, it will be proven that both distributed compression schemes fall into the category ofSuccessive Upper-Bound Minimization, ensuring their convergence to a stationary point. Eventually, the effectiveness of the proposed compression schemes will be substantiated as well by means of numerical investigations over some typical transmission scenarios.

The focus is on Wyner-Ziv type distributed fronthaul compression for the uplink of Cloud Radio Access Networks with single-hop topology to leverage the correlation among the received signals of neighboring Radio Access Points. For this, we highlight the relation between the problem at hand and the Chief Executive Officer source coding under logarithmic-loss distortion and depict that the achievability arguments from the latter verify addressing the postulated optimization. Subsequently, we derive the pertinent optimal solution and utilize that as the backbone of the Generalized Distributed Information Bottleneck (G-DIB) routine proposed here to tackle the considered remote source coding problem. As its name suggests, this novel approach in its very core spirit extends the State-of-the-Art Distributed Information Bottleneck (DIB) method by enabling individual rate constraints for various fronthaul links.

The main focus will be on the indirect Joint Source-Channel Coding problem in which a noisy observation of the source has to be quantized ahead of transmission over an error-prone forward link to a remote processing unit. To that end, we present here a complete extension to the preliminary Information Bottleneck method by providing the formal optimal solution to this newly established Variational Principle, together with an algorithm, the Forward-Aware Vector Information Bottleneck (FAVIB), to pragmatically tackle its underlying non-convex design optimization. FAVIB extends the current state-of-the-art approaches via capacitating a full sweep over the entire gamut of the trade-off parameter. Consequently, the trajectory of all achievable points in the Information-Compression plane becomes traversable via soft mappings. It will be shown that, by enjoying an inherent error protection, this novel compression scheme can obviate the call for separate channel coding on the forward path.

Consider following setup: A number of observations from a data source shall be compressed jointly prior to a forward transmission via several rate- limited links to a central processing unit. To design the respective quantizers, here, Mutual Information is chosen as the fidelity criterion and the broad-ranging structure of Multivariate Information Bottleneck is then aptly tailored to that purpose. This, indeed, not only yields a novel design approach for the considered distributed scenario but also paves the way towards perceiving the chance of leveraging this flexible conceptual frame in a vast variety of applications regarding digital data transmission. Explicitly, it immediately enables addressing various extensions of the presumed arrangement, incorporating the parallel construction of intertwined compression systems for several correlated sources.

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the “fourth industrial revolution”, referred to as “Industrie 4.0”. Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements. This paper proposes representative use cases, which have been examined in the German Tactile Internet 4.0 (TACNET 4.0) research project. In order to analyze these use cases, this paper identifies the main challenges and requirements of communication networks in Industrie 4.0 and discusses the applicability of 5th generation wireless communication systems (5G).

The main focus of this paper is on the problem of noisy source coding wherein observed signals from an inaccessible source shall be compressed. To that end, rather than resorting to the conventional methods from Rate-Distortion theory, the so-called Information Bottleneck paradigm is deployed in order to obtain a highly informative representing signal w.r.t. the given source. An efficient, generic and highly flexible graph-based message passing routine for clustering, known as the Affinity Propagation is successfully applied here as a novel treatment for that purpose. The fundamental differences and the performance-wise comparison w.r.t. the state-of-the-art KL-Means-IB algorithm is provided as well.

In the context of noisy source coding, contrary to the conventional Rate-Distortion theory, the so-called Information Bottleneck method formulates the existent fundamental complexity-precision trade-off in a symmetric and purely information-theoretic fashion. Since the pertinent optimization task to design the quantizer is quite demanding, a number of heuristics have been developed to provide practically feasible procedures at the expense of yielding suboptimal solutions. In this paper, we consider two pertinent routines originally appeared in totally different applications and set out to precisely prove their algorithmic equivalence by conducting a thorough analysis over the corresponding algorithmic steps. We further corroborate our theoretical investigation employing computer-based simulations.

Multi-carrier transmission schemes with general non-orthogonal waveforms provide a flexible time-frequency resource allocation and are bandwidth efficient. However, the interference inherently introduced by the non-orthogonal waveforms always requires a higher order equalizer at the receiver. Depending on the localization properties of the applied waveform, the structure and the complexity of this equalizer is adapted to consider channel influences, like carrier frequency and timing offsets. Especially for two-phase two-way relaying channels (TWRCs), where two users simultaneously transmit data on the same resources, a robust transmission scheme in presence of practical constraints such as asynchronicity is of utmost importance. This paper focuses on the utilization of general multi-carrier transmission schemes applied to TWRCs and the utilization of factor graph-based equalizers (FGEs) at the relay in order to mitigate the impacts of the physical channels, offsets, and the non-orthogonal waveforms. In combination with the subsequent physical-layer network coding detection/decoding scheme, this combination allows for a flexible design of the waveforms and the FGE to meet the complexity-performance trade-off at the relay. As demonstrated by numerical evaluation results, the proposed multi-carrier scheme with well-localized waveforms utilizing FGEs outperforms orthogonal frequency division multiplexing in TWRC for a wide range of practical impacts.

The general problem of quantizing observation signals appears in different aspects of data processing, from special code designs to realization of low-complexity receivers. To this end, a new framework, known as the Information Bottleneck method, has recently attracted a great deal of attention. In this paper, after introducing this framework and providing the Iterative Information Bottleneck algorithm as the primary pertinent solution, we also discuss three other heuristics aiming to solve the similar problem efficiently. Since the resultant solution of considered approaches is locally optimum, it strongly depends on the choice of initialization. The main contribution of this work is to prove the equivalence of these algorithms asymptotically, i.e., assuming an infinite run of algorithms for the extreme case of infinitely large trade-off parameter. We also substantiate this claim by means of computer-based simulations.

The air interface for millimeter wave (mmWave) communications must be designed by properly taking into account the specific characteristics of the wireless channel at higher frequencies. In this work, we start by considering a channel model recently proposed in the literature for mmWave communications in outdoor urban scenarios. First, on top of this channel model we implement a sectorized beamforming model necessary to compensate the large path-loss at mmWave range and study how channel statistics, namely, delay spread and angle spread, are influenced by employing different beamwidths. Subsequently, adopting this beamforming model in the mmWave channel, orthogonal frequency division multiplexing (OFDM) and single carrier frequency domain equalization (SC-FDE) systems are compared. Extensive link level simulations are performed by considering different beamwidths, line-of-sight (LOS) coverage and channel coding. Numerical results show that SC-FDE using minimum mean square error (MMSE) equalization performs close to OFDM in coded systems. However, SC-FDE might be beneficial in practice due to much lower peak to average power ratio (PAPR) than OFDM.

The performance of physical-layer network coding (PLNC) in two way relay channels (TWRCs) is significantly decreased by impairments like carrier frequency offsets or timing offsets. This mismatch cannot be completely compensated at the receiver side, even if the offsets are known. Multi-carrier systems with Gaussian waveforms for TWRC systems are more robust against the impact of these offsets. In comparison to rectangular multi-carrier systems, Gaussian waveforms have a better time-frequency shape and they provide improved spectral efficiency due to lower out of band radiations. In this paper, we introduce a multi-carrier TWRC system with Gaussian waveforms and develop an adapted soft interference cancellation (SIC) equalizer to consider the intrinsic interference of the Gaussian transmit/receive filters. The presented results show, that systems with Gaussian waveforms achieve bit error rates close to the rectangular waveforms in PLNC systems while being more robust against Doppler and delay spreads.

In this paper, a novel setpoint-based design approach for Irregular Repeat Accumulate (IRA) codes in iterative detection and decoding structures is presented. In contrast to conventional IRA code design in which the convolutional decoder is combined with the detector, the goal behind this approach is to keep the IRA decoding structure consisting of convolutional decoder and repetition decoder intact, i.e. to consider it as an inner loop of the overall detection structure. The outer loop is then composed of the IRA decoder and the system specific detector. This approach requires to adapt the irregular repetition code jointly to the convolutional decoder as well as to the detector which is achieved by formulating setpoints for the inner and outer code characteristic. As will be shown, the presented code design approach, although starting from a completely different viewpoint as the conventional approach, leads to an irregular repetition code with a very similar transfer characteristic and code rate than the conventional approach.

This paper discusses two approaches of reliability-based HARQ, adapting the packet size of a retransmission in a 3GPP Long Term Evolution (LTE) system. We focus on the adaptation of the retransmission size in terms of physical resources by using information 1) of the channel, namely the signal-to-noise ratio (SNR) or 2) reliability information from the decoder output, taking the overall transmission into account. Both approaches will be compared to the HARQ system used in LTE in terms of throughput performance. Link level simulations will be performed with single bit feedback and 2 bit multilevel ACK/NAK. This work takes realistic impairments such as channel estimation, signal-to-noise ratio (SNR) estimation and implementation of a Turbo en- and decoder into regard.

To meet the increasing capacity demands of future mobile networks, dense deployment of radio access nodes in combination with partly centralized processing by means of a cloud-based architecture is a promising option. In such an architecture, the design and optimization of the backhaul plays a crucial role. In this paper, we review different backhaul technologies available and discuss their characteristics for use in cloud-based networks. We point out how a heterogeneous backhaul network and a flexible centralization enables the proposed architecture and give an outlook on how a joint design of access and backhaul can help in meeting the increased demands.

In this paper, distributed Interleave-Division- Multiplexing Space Time Codes (dIDM-STC) in Multi- User Decode-and-Forward Relay Systems are considered. Due to decoding errors at the relays, which are unavoidable in practical systems, error propagation to the destination occurs. In order to cope with this error propagation, recently a Reliability-Aware Iterative Detection Scheme (RAID) at the destination was proposed by the authors, which takes the decoding success at the relays, as well as the decoding reliability of the relays into account. This scheme requires a CRC check and also the estimation of the error probability at each relay. In this paper, a modification of RAID is presented, which only requires a CRC check at the relays, completely avoiding the estimation of the error probabilities at the relays and the signaling to the destination. Instead, the determination of the error probabilities is shifted to the destination reducing the complexity at the relays and the overall signaling overhead. As will be shown, the proposed complexity reduced RAID scheme (CR-RAID) allows for the same end-to-end performance in terms of frame-error-rates as the original RAID.

The application of Compresses Sensing is a promising physical layer technology for the joint activity and data detection of signals. Detecting the activity pattern correctly has severe impact on the system performance and is therefore of major concern. In contrast to previous work, in this paper we optimize joint activity and data detection in under-determined systems by minimizing the Bayes-Risk for erroneous activity detection. We formulate a new Compressed Sensing Bayes-Risk detector which directly allows to influence error rates at the activity detection dynamically by a parameter that can be controlled at higher layers. We derive the detector for a general linear system and show that our detector outperforms classical Compressed Sensing approaches by investigating an overloaded CDMA system.

In dense mobile network deployments, the cooperation of base stations in the uplink promises performance gains w.r.t. area throughput and power efficiency. In this paper, we propose the use of a distributed consensus-based estimation algorithm for the linear equalization of multiple user signals occupying the same resources. We will show that using an iterative process, the same estimation quality can be achieved as if a centralized joint detection of the signals was performed, and that with a limited number of iterations, a satisfactory bit error performance can be achieved.

This paper discusses retransmission approaches to improve the throughput performance of Hybrid-ARQ (HARQ) schemes in a point-to-point single user 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) uplink system. One goal of communication systems is to achieve a reliable transmission with a throughput performance as close as possible to channel capacity. For that, reducing the channel utilization will improve the throughput performance. Instead of sending two retransmission packets for two HARQ processes of one users, a previously published HARQ scheme uses the XOR combining of these packets to get only one retransmission packet with the same size. Similar to this idea, a new varied scheme performs XOR combining of parts of one conventional full retransmission to generate a smaller retransmission packet. Both approaches will reduce the channel utilization. They will be compared with a HARQ system in LTE uplink using a full size retransmission and a half size retransmission. The main focus of this work is the throughput performance evaluation of these schemes in an LTE link-level simulator.

In this paper physical layer network coding (PLNC) in two-phase two-way relaying networks using coded orthogonal frequency division multiplexing (OFDM) transmission is investigated. After receiving the superimposed signal from both sources, the relay estimates the XOR-based network coded signal, which is broadcast back to the sources. Assuming that the relay is equipped with multiple antennas, the uplink transmission forms a multiple-input multiple-output (MIMO) system, which allows the application of MIMO detection technologies. To this end, the impact of employing multiple antennas at the relay on different detection and decoding schemes under investigations is studied and compared with respect to mutual information (MI). Numerical simulations verify our theoretical analysis.

The combination of OFDM with joint pre-processing in adaptive multi-antenna systems offers both an ease of equalization in frequency-selective channels and keeping the signal processing at the mobile stations simple. In addition, the spatial dimension can be efficiently exploited to ensure high system throughput. With the utilization of higher-order modulation the performance of the system is highly sensitive to multiple access interference and nonorthogonal subchannels due to hardware impairments or insufficient adaptation to the current channel conditions. A further source of error in TDD systems are the non-reciprocal transceivers inhibiting the baseband-to-baseband channel reciprocity required for accurate channel state acquisition based on the uplink channel estimate. In this paper, measurement results of a low-cost hardware-based calibration are presented and the drawbacks are discussed leading to the utilization of a recently introduced relative calibration. The latter is applied to an OFDM system and achieves or at least approximates the baseband-to-baseband reciprocity. Thus, it enables the link adaptation using the uplink channel state information. Furthermore, preliminary hardware implementations of the relative calibration running on a real-time system show accurate results.

This paper is concerned with the application of distributed Interleave-Division-Multiplexing Space- Time Codes (dIDM-STCs) in relaying systems with error-prone relays applying Decode-and-Forward (DF). In case of erroneous decoding at the relays, error propagation occurs which is not considered by the original detection scheme for IDM-STCs. Hence, a new Reliability Aware Iterative Detection Scheme (RAID) is proposed which takes the decoding success of the relays as well as their decoding reliability into account. By optimally incorporating this knowledge in the detection process at the destination, substantial performance gains compared to the original detection scheme are achieved. The proposed RAID scheme even outperforms adaptive relaying as it explicitly exploits also erroneous relays, which is not the case for the adaptive scheme.

In this paper, a novel HARQ transmission scheme based on network coding is proposed for wireless unicast scenarios. Instead of retransmitting erroneous packets individually, a network coded packet constructed by the XOR of two erroneous packets is transmitted similar to network coding. In order to fully exploit the network coded packet in combination with the previously received erroneous packets, soft combining methods with respect to Chase Combing (CC) and Incremental Redundancy (IR) are developed. The expected throughput gain of 33% for one retransmission for the proposed solution compared to common HARQ transmission is confirmed by LTE link-level simulations.

This paper investigates the optimal power routing problem in relay-based cooperative networks, where the relays are arbitrarily positioned. We generalize the standard shortest path routing algorithm (GSPRA) to find an minimum-power distributed MIMO multi-hop route from a source to a destination while satisfying a given e2e outage probability demand. The task of the proposed approach includes how to group relays to virtual antenna array (VAA) and discover the optimal multi-hop path. Instead of using per hop (or link) constraint, which is assumed by most of the existing routing algorithm, an e2e outage probability constraint is assumed for more relevance and freedom in practical systems. Under the concept of virtual node and virtual link, an efficient power allocation solution for general distributed MIMO multi-hop networks is used to calculate link costs for the shortest path algorithm. The proposed routing approach can fully exploit the merits of both cooperative communications and multi-hop transmissions. The significant power savings due to the proposed approach in comparison to the existing algorithms is demonstrated by numerical results.

In this paper we develop a power allocation scheme for single-relay systems applying Decode-and-Forward (DF) based on the resulting bit error rate (BER) at the destination. First, an analytical expression for the BER of M-QAM modulation considering estimation errors at the relay is derived. Based on this expression, the total transmit power is optimally assigned to the source and the relay in order to minimize the probability of errors at the destination. The preciseness of the derived closed form expression as well as the superior performance of the proposed DF-based relaying system are demonstrated by simulation results.

In this paper, we present a new relaying protocol for coded OFDM-based relaying systems. The classical Decode-Forward (DF) protocol exploits the coding gain within the relay, however the overall performance suffers from error propagation in case of decodings errors at the relay as no reliability information about the source-relay (SR) link can be exploited. This drawback is avoided by the proposed Decode-Quantize-Forward (DQF) scheme, where the code bits estimated by the decoder in the relay are directly forwarded to the destination. Based on the observation, that code bit errors at the relay occur likely on subcarriers with low signal to noise ratio (SNR) on the SR link, we propose a modified maximum ratio combining (cMRC) scheme for the destination. As this approach exploits the varying channel reliability per subcarrier it outperforms the well-known DF protocol significantly.

In this paper a physical-layer network coded two-way relay system applying Low-Density Parity-Check (LDPC) codes for error correction is considered, where two sources A and B desire to exchange information with each other by the help of a relay R. The critical process in such a system is the calculation of the network-coded transmit word at the relay on basis of the superimposed channel-coded words of the two sources. For this joint channel-decoding and network-encoding task a generalized Sum-Product Algorithm (SPA) is developed. This novel iterative decoding approach outperforms other recently proposed schemes as demonstrated by simulation results.

In this paper we present a generalized joint channel coding and physical layer network coding scheme for two-way relay systems, where the two sources A and B desire to exchange information from each other through the relay R simultaneously. Physical network coding scheme allows the relay to decode the network-coded information of both sources from the superimposed received signal. A novel iterative decoding approach is developed for arbitrary linear channel code, e.g. Low-Density Parity-Check (LDPC) code. Simulation results show that the proposed scheme outperforms other recently proposed network coding schemes with slightly increased complexity.

Distributed MIMO multi-hop relaying is one of the most promising technologies that permits cost-effective improvement of coverage, data rate and end-to-end (e2e) user experience by utilizing distributed low-complexity space-time codes to overcome path losses and deep fades of wireless channels. However, an efficient transmission scheme and resource management are required to exploit these advantages. Specifically, low-complexity adaptive schemes and power control strategies should be designed, thereby achieving robust and cost-efficient e2e communications. In this paper an adaptive transmission scheme is presented, where one relay stops forwarding the message if it is in outage and other nodes adapt to a new space-time code. For this adaptive scheme, optimal as well as sub-optimal closed-form power allocation solutions are derived which minimize the total transmission power while satisfying a given e2e outage probability. The significant power savings due to the proposed approaches in comparison to a non-adaptive scheme is demonstrated by numerical results.

Adaptive coding and modulation is an important topic considering future communication systems. Orthogonal frequency division multiplexing (ODFM) has been identified as a promising technique, which offers the possibility for further enhancements by bit and power loading schemes. Commonly, channel coding has not been considered in the optimization of such algorithms. It is, however, an important component used in nearly every communication system. In this paper we propose a new scheme to adapt code rate, modulation and transmit power by solving a convex optimization problem based on a bisection approach in order to enhance the frame error rate at a fixed target rate.

Distributed MIMO multi-hop relaying can provide cooperative diversity and overcome path losses, hence, boost the end-to-end (e2e) performance. By using a low-complexity adaptive scheme, where one relay stops sending the message if it is in outage and other nodes adapt to a new space-time code, robust communication links can be further achieved. The contribution of this paper is the derivation of near-optimal closed-form solution for joint power and time allocation for such adaptive scheme that minimizes he transmission power while satisfying a given e2e non-ergodic outage probability.

In this paper a soft relaying scheme for coded relay networks combining the benefits of classical Decode-Forward (DF) and Amplify-Forward (AF) is extended to higher order modulation schemes. In order to minimize the mean-squared- error at the destination, the conditional expectation value of the symbol after soft-output channel decoding at the relay is transmitted. The main idea of this scheme called Decode- Estimate-Forward (DEF) is to exploit coding gain like DF while still preserving reliability information as AF. This approach was presented by the authors for BPSK in coded systems, but the extension to arbitrary modulation alphabets presented here allows for more flexible system designs. The performance of the proposed relay function is compared to classical AF and DF in a wide variety of scenarios like AWGN and Rayleigh fading channels as well as different numbers and constellations of relays.

The throughput of multi-hop communication systems can significantly be increased by the application of MIMO concepts. To utilize the physical resources in an efficient way while meeting the quality-of-service (QoS) constraints, appropriate power allocation strategies are desired. In this paper the total transmit power of a MIMO multi-hop system is minimized under the constraint of a given end-to-end outage probability. The optimum power allocation corresponds to a convex optimization problem. In order to achieve an analytical solution, the original task is relaxed by stringent approximations and a simple closed- form near-optimum solution is proposed. As this improved approximative power allocation (IAPA) achieves excellent performance results, this new approach is also very useful for investigating outage restricted multi-hop systems analytically.

Distributed MIMO multi-hop schemes can provide high data rates through spatially distributed relaying nodes. The relaying nodes allow the deployment of MIMO techniques to enhance the throughput by utilizing uncorrelated sub-channels. However, the spatial farness of geometrically separated relaying nodes results in different path losses from the nodes of one virtual antenna array (VAA) to the nodes of another VAA. In this paper we derive an approximative expression for the end-to-end (e2e) outage probability for such asymmetric networks, where orthogonal space-time block codes (OSTBC) are utilized for transmission. Based on this analytical expression a convex optimization problem that aims to reduce the total transmission power while meeting a given e2e outage level is formulated and an efficient near-optimal power allocation approach with low complexity is proposed. This near-optimum solution leads to the interesting result, that the same power is assigned to each node of one VAA. Thus, the power allocation turns out to be symmetric with respect to the nodes of one VAA also for networks with asymmetrically distributed nodes.

The concept of virtual antenna array (VAA) is a promising approach to apply MIMO concepts in relaying systems. In order to fulfill a given quality-of-service (QoS) requirement while reducing the power consumption of the entire system, efficient resource allocation strategies have to be developed. The optimum power allocation solution corresponds to a convex optimization problem. To achieve an analytical solution, the approximative approach IAPA (improved approximative power allocation) has been proposed by the author. Within this paper this closed-form solution is further analyzed and analytical expressions for the power allocation in case of symmetric relaying networks are derived. Thereby, an approach for analyzing and optimizing different multi-hop scenarios is developed.

In recent years, communication systems with multiple antennas at the transmitter and at the receiver have gained considerable interest. Layered architectures like the V-BLAST scheme are a strong candidate to exploit the capacity advantages of multiple antenna systems leading to practical wireless communication schemes with very high data rates. The combination with orthogonal frequency division multiplexing (OFDM), called MIMO-OFDM, with per-antenna-coding is one of the most likely implementations of multilayer architectures in frequency selective environments. In this paper, we present a novel, computational efficient implementation of successive interference cancellation (SIC) for coded MIMO-OFDM. It utilizes a parallelized version of the Sorted QR Decomposition (SQRD) to achieve the same optimized detection order for all subcarriers in order to exploit the error correction capability of the code within the SIC. In comparison to other schemes known from literature our approach requires only a fraction of computational complexity with almost the same performance.

In recent publications the use of lattice-reduction for signal detection in multiple antenna systems has been proposed. In this paper, we adopt these lattice-reduction-aided schemes to the MMSE criterion. We show that an obvious way to do this is infeasible and propose an alternative method based on an extended system model, which in conjunction with simple successive interference cancellation nearly reaches the performance of maximum-likelihood detection. Furthermore, we demonstrate that, a sorted QR decomposition can significantly reduce the computational effort associated with lattice-reduction. Thus, the new algorithm clearly outperforms existing methods with comparable complexity.

Theoretical and experimental studies have shown that layered space-time architectures like the BLAST system can exploit the capacity advantage of multiple antenna systems in rich-scattering environments. We present a new efficient algorithm for detecting such architectures with respect to the MMSE criterion. This algorithm utilizes a sorted QR decomposition of the channel matrix and leads to a simple successive detection structure. The algorithm needs only a fraction of the computational effort compared to the standard V-BLAST algorithm and achieves the same bit error performance.

The use of multiple antenna systems provides a great performance advantage in Rayleigh fading environments. To exploit this advantage several schemes like the layered space-time architecture have been proposed, but mostly restricted to narrowband transmission. Recently, Lozano and Papadias have presented a generalization of the well-known V-BLAST detection algorithm for frequency selective fading channels. In order to improve the performance of this receiver, we propose an iterative detection algorithm in this paper.

We propose a "turbo" coding scheme for the multiple-input multiple-output (MIMO) Rayleigh fading channel consisting of the serial concatenation of a block code as the outer code and different orthogonal space-time block codes (STBC) for more than two transmit antennas as the inner code. Here, we consider the orthogonal STBC as a mapping scheme in space and time. At the receiver, we apply iterative space-time detection and decoding. We analyze the impact of different mapping strategies on the information transfer of the soft-input-soft-output (SISO) space-time detector. Moreover, we analytically show that additional performance gains over Gray mapping can be obtained by different mapping strategies. Furthermore, we use extrinsic information transfer characteristics (EXIT-charts) in order to predict the performance and the behavior of the system.