This article provides a systematic overview of the basics of talkative power conversion (TPC). TPC is an emerging technique for simultaneous information and power transmission, in which data modulation is integrated into a switched-mode power converter. The data sequence is embedded in the ripple voltage, which is superimposing the output voltage of the converter. In contrast to conventional power line communication (PLC), TPC can be used universally, not only in grid applications. Aspects of power electronics (PE) and digital communication are presented in a structured form, including new perspectives such as multiple-input multiple-output (MIMO) techniques applied to TPC, adaptive modulation and channel coding, and advanced receiver design with adaptive channel and load estimation. The new aspects aim to mitigate the inherent shortcomings of TPC.

Talkative power conversion (TPC), which embeds information into the switching ripple to achieve the simultaneous transmission of power and data without installation of additional hardware, can reduce the additional communication infrastructure in cyber-physical systems of smart grid and renewable power plants. The TPC in DC/DC converters has been the subject of numerous investigations, but comparatively few studies have focused on the DC/AC converters. In this paper, the space-vector pulse width modulation (SVPWM)-based TPC techniques including both variable zero vector width modulation (VZVWM) and variable zero vector position modulation (VZVPM) for three-phase DC/AC converters are proposed. The DC/AC converters can transmit information to the receiver, crossing the LCL filter and power cable, with a good power quality. The sliding FFT method is employed for demodulation, and the bit rate is analyzed. Finally, simulations and experiments prove the viability of the SVPWM-based three-phase DC/AC talkative power converters with a bit rate of 100 bps and 1-blt length time delay.

3GPP has completed the standardization of 5G NR positioning in Release 16 (Rel-16) and Rel-17, and the Rel-18 study on expanded and improved positioning was finished in November, 2022. The standard solutions enable several positioning techniques in efforts to improve accuracy and power efficiency, and to decrease the latency. RAT-independent positioning techniques have also been standardized to support user equipment positioning via satellites and sensors. This article provides the latest progress on 3GPP 5G positioning including requirements, use cases, and the newly supported positioning techniques in Release-17 and Release-18. Finally, future challenges are discussed.

Cooperative radio localization and navigation systems can be used in scenarios where the reception of global navigation satellite system (GNSS) signals is not possible or impaired. While the benefit of cooperation has been highlighted by many papers, calibration is not widely considered, but equally important in practice. Utilizing the signal propagation time requires group delay or ranging bias calibration and estimating the directions-of-arrival (DoAs) requires antenna response calibration. Often, calibration parameters are determined only once before operation. However, the calibration parameters are influenced by e.g. changing temperatures of radio frequency (RF) components or changing surroundings of antennas. To cope with that, we derive a cooperative simultaneous localization and calibration (SLAC) algorithm based on Bayesian filtering, which estimates antenna responses and ranging biases simultaneously with positions and orientations. By simulations, we show that the calibration parameters can be estimated during operation without additional sensors. We further proof practical applicability of SLAC by evaluating measurement data from robotic rovers. With SLAC, both ranging and DoAs estimation performance is improved, resulting in better position and orientation estimation accuracy. SLAC is thus able to provide reliable calibration and to mitigate model mismatch. Finally, we discuss open research questions and possible extensions of SLAC.

A multitude of autonomous robotic platforms collectively organized as a swarm attracts increasing attention for remote sensing and exploration tasks. A navigation system is essential for the swarm to collectively localize itself as well as external sources. In this article, we propose a self-aware swarm navigation system that is conscious of the causality between its position and the localization uncertainty. This knowledge allows the swarm to move in a way to not only account for external mission objectives but also enhance position information. Position information for classical navigation systems has already been studied with the Fisher information (FI) and Bayesian information (BI) theories. We show how to extend these theories to a self-aware swarm navigation system, particularly emphasizing the collective performance. In this respect, fundamental limits and geometric interpretations of localization with generic observation models are discussed. We further propose a general concept of FI and BI based information seeking swarm control. The weighted position Cramér-Rao bound (CRB) and posterior CRB (PCRB) are employed flexibly as either a control cost function or constraints according to different mission criteria. As a result, the swarm actively adapts its position to enrich position information with different emerging collective behaviors. The proposed concept is illustrated by a case study of a swarm mission for gas exploration on Mars.

A simultaneous wireless information and power transfer system employing separated frequency bands for energy and data, dubbed dual-band SWIPT, is investigated. Elementary circuit elements are optimized numerically. An experimental testbed based on a gallium nitride (GaN) full-bridge converter demonstrates data rates in excess of 450 kbps employing on-off keying in conjunction with a simple diode detector for data recovery.

This contribution elaborates on transmitter-side precoding for MIMO downlinks with full channel state information at the transmitter and no channel state information at the receiver under equivalent isotropic radiated power (EIRP) constraints. The optimization problem is formulated in standard form. Similarities and differences to current beamforming solutions are drawn. An illustrative example identifies weaknesses of sum-power optimized precoding schemes, when they are scaled to comply with an EIRP limit. In order to improve the achievable rate, semi-random approaches are investigated. Numerical results are presented in the context of massive MIMO and the utilization of multi-mode antennas.

This paper presents spatial diversity techniques applied to multiple-input multiple-output (MIMO) diffusion-based molecular communications (DBMC). Two types of spatial coding techniques, namely Alamouti-type coding and repetition MIMO coding are suggested and analyzed. In addition, we consider receiver-side equal-gain combining, which is equivalent to maximum-ratio combining in symmetrical scenarios. For numerical analysis, the channel impulse responses of a symmetrical 2 × 2 MIMO-DBMC system are acquired by a trained artificial neural network. It is demonstrated that spatial diversity has the potential to improve the system performance and that repetition MIMO coding outperforms Alamouti-type coding.

Future wireless networks put high requirements on many system components. Data rates of the order of 100 Gbps and beyond, currently used in wired optical networks only, are expected for 5G applications. This imposes high effort with respect to code design and decoding algorithms. Due to their low complexity, LDPC codes are an essential part of many modern communication systems. Additionally, high symbol rates will require high-rate analog-to-digital conversion which turns out to be very power consuming. In order to explore trade-offs between hardware complexity and digital baseband processing, this contribution investigates the bit error rate performance and soft Monte Carlo simulation of LDPC codes together with reduced-complexity analog-to-digital conversion.

Connected cars have telecommunication services similar to those found in homes and offices. Passengers have come to expect efficiently using time spent in their cars working and engaging in entertainment. Fulfilling this desire requires advanced infotainment systems with a variety of capabilities and functions similar to mobile phones. As people become more attached to their mobile phones, seamless integration of phones into the car computer becomes more crucial. Bluetooth and IEEE 802.11 systems are often used to connect mobile phones to car computers. Both technologies operate in the 2.4 GHz unlicensed industrial, scientific and medical (ISM) radio bands. However, since early development of standards governing the ISM band, coexistence among devices sharing the band have been under close scrutiny. An increased use of Bluetooth and 802.11 systems in the automotive domain and the logistics of having extremely small distances between devices makes coexistence a challenging task. This paper presents performance evaluation of both WLAN and Bluetooth for typical automotive domain applications (i.e., music streaming and hands-free calling). Focused attention is paid to a scenario in which all three non overlapped WLAN channels are used. The effect of traffic load and WLAN power level are investigated. Results demonstrate that Bluetooth channels 71 to 78 are critical to maintain acceptable Bluetooth connectivity. Hands-free calling is more sensitive to interference than music streaming. Bluetooth effect on WLAN is small.

Wireless systems have migrated into the automotive domain very fast in the recent years. Car passengers expect to have seamless integration of their consumer electronic devices in their cars. IEEE 802.11 systems are popular for entertainment services inside the car, however due to the fact that IEEE 802.11 use the unlicensed industrial, scientific and medical (ISM) radio bands, the wireless coexistence problem will lead to dramatic decrease of throughput. This can affect the performance of different applications in cars. This paper presents both test drives and measurements in the lab. The target is to have an impression on the influence of the surrounding systems on 802.11 systems inside the cars. The results show that the WLAN signals from the surrounding strongly affect the throughput of the systems inside the cars. A throughput drop by 80% compared to maximum was observed in one test drive in the city center.

Coherent detection is commonly facilitated by means of pilot-aided channel estimation. While this approach offers a high estimation accuracy, the spectral and power efficiencies are reduced due to the pilot overhead. In the case that redundancy due to channel coding is not used for the purpose of channel estimation, the maximum pilot spacing for which a time-varying channel can be reconstructed without distortion is given by the Nyquist-Shannon sampling theorem. In this paper, it is shown that if channel coding is taken into account for channel estimation in an iterative fashion, the maximum spacing can be significantly extended, which results in an increased spectral efficiency. Towards this goal, a so-called coded sampling bound is derived. This semi-analytical bound is compared with the Nyquist-Shannon sampling theorem. The presented results indicate that the maximum pilot spacing can be arbitrarily large given a suitable channel code and code rate.

In most modern wireless systems, adaptive modulation and channel coding is applied to achieve a high spectral efficiency. Hence, suitable receivers need to support a large variety of modulation and channel coding schemes (MCS) while maintaining a low complexity. The performance and complexity of a multi-dimensional factor-graph based (MD-GSIR) framework for iterative joint channel estimation and data detection is investigated for the application of adaptive modulation and coding in an LTE environment. Three detection algorithms within the MD-GSIR framework are investigated and a novel tree-based detection is proposed. The versatile structure of the MD-GSIR concept offers a flexible trade-off between computational complexity and performance. Furthermore, the message exchange is adapted to support the irregular training structure which may occur in adaptive environments.

Bijective mapping techniques with uniformly distributed constellation diagrams, like PSK and QAM, are standard in today's communications systems. Yet, it is well known that without additional effort in form of active signal shaping, bijective mapping techniques are not capacity achieving. In recent years, an alternative technique called superposition modulation (SM) has been investigated, which theoretically is capacity achieving on the Gaussian channel. However, non-bijective mapping (which can be interpreted as lossy source coding) is challenging with respect to the design of proper channel codes. In this paper, the suitability of irregular convolutional coding in conjunction with superposition modulation is investigated. With proper code design, this combination is capable of achieving 6 bits/symbol at just 0.7 dB away from the Shannon capacity limit given a memory-4 mother code.

The principles and benefits of soft decisions are well known and widely applied. The advantage of knowing that a decision is reliable or not is obvious. Belief propagation within a factor graph enables a unified use of soft information for both channel estimation and data detection. However, if soft information does not reflect the true reliability of a decision, the achievable performance may degrade. In this paper, the calculation of reliability information is refined to consider the event of unreliable soft decisions. The proposed solution is based on the mean bit error probability calculated after each iteration and integrates nicely within the existing factor graph. Simulation results are provided to illustrate the performance gains.

A graph-based receiver is presented that iteratively performs soft channel estimation and soft data detection. Reliability information of data symbols is utilized to improve channel estimation, and in turn, soft channel estimates refine data symbol estimates. The proposed multi-dimensional factor graph introduces transfer nodes that exploit correlation of adjacent channel coefficients in an arbitrary number of dimensions (e.g. time, frequency, and spatial domain). This establishes a simple and flexible receiver structure that facilitate soft channel estimation and data detection in multi-dimensional dispersive channels, and supports arbitrary modulation and channel coding schemes. Simulation results demonstrate that the proposed multi-dimensional graph-based receiver outperforms iterative and non-iterative state-of-the-art receivers.

In this paper, a graph-based soft iterative receiver (GSIR) for fast fading channels is investigated. Soft channel estimation as well as soft-output data detection are jointly accomplished via Bayesian inference over a general factor graph. The key feature is a transfer node which enables information flow from one channel node to adjacent channel nodes. The performance characteristics of this receiver is investigated via an EXIT chart analysis and simulation results. Particular emphasis is on a proper channel code design. The algorithm is universally applicable to arbitrary (bijective and non-bijective) modulation formats and can easily be extended to multi-dimensional channel estimation.

Currently, combining turbo or low-density parity-check (LDPC) codes with bit-interleaved coded modulation (BICM) is the most common coding scheme for bandwidth-limited channels. However, in order to be capacity achieving, shaping is necessary, which is difficult in conjunction with iterative processing. An alternative is interleave-division multiplexing (IDM), which can be either treated as a coded modulation scheme or a multiplexing scheme. In IDM, coded data sequences are linearly superimposed, thus avoiding the necessity of active signal shaping. The characteristics of IDM can be controlled by power and phase allocation. With typical parameter settings, IDM is non- bijective. In that case, the main task of channel coding is aiding to resolve the data sequences ("layers") at the receiver side, whereas noise mitigation is a secondary task. In this paper, we demonstrate that classical channel codes (like LDPC and turbo codes) fail when applied with IDM. In fact, repetition coding is quite useful. It is shown by means of an EXIT chart analysis that a concatenation of a high-rate convolutional code with an irregular repetition code matches well with the characteristics of an IDM a posteriori probability (APP) demapper.

Iterative receiver structures that jointly perform channel estimation and decoding promise substantial performance gains. However, these gains only materialize with sufficiently accurate initial channel estimates. In this paper, initialization by multi-objective particle swarm optimization (MOPSO) is investigated. MOPSO supports low-complexity initial channel estimation with superimposed training symbols. Furthermore, it is shown that MOPSO works well in rank-deficient scenarios with arbitrary training sequences. Numerical results validate the performance enhancement of MOPSO initialization integrated within a graph-based iterative receiver.

The substantial gains promised by multi-antenna systems require accurate channel knowledge, at least at the receiver side. Furthermore, the complexity of most receivers becomes unfeasible when the number of transmit antennas gets large (known as "large-MIMO" systems). In this paper, channel estimation by means of cooperative particle swarm optimization (CPSO) and factor-graph based data detection with belief propagation is presented. The overall complexity of the receiver grows linearly with respect to the number of transmit and receive antennas. Numerical results validate the performance of the CPSO channel estimation integrated within a graph-based iterative receiver, including a 24×16 MIMO system.

Interleave-division multiplexing (IDM) is both a multiplexing and a modulation technique. When seen as a modulation technique the constellation is determined by the number of superimposed layers together with the amplitude and phase coefficients assigned to each layer. The most natural and also predominant selection is equal power allocation and uniform phase distribution. A well known fact is that superposition techniques lead to a high peak-to-average power ratio (PAPR). To investigate the effect of phase allocation on the PAPR, particle swarm optimization (PSO) is used to find good setups. The results show that for equal power allocation uniform phase distribution is optimal, but if unequal power allocation is used, then the phase distribution should be modified to significantly reduce the PAPR and at the same time to increase the theoretical performance limit.

We address joint channel estimation and data detection based on factor graphs. The considered graph-based approach utilizes reliability information of channel estimates to facilitate soft-output data detection, and in-turn reliability information about the data symbols is taken into account for channel estimation. In this paper graph-based soft channel estimation and detection is extended to an OFDM based air interface, where the channel response varies in two dimensions; time and frequency. Initial channel estimates obtained by training symbols are conveyed by a two dimensional (2D) factor-graph in time and frequency with only a linear increase in complexity. The required training overhead for the proposed 2D graph-based soft channel estimation scheme may be substantially reduced by taking the redundancy introduced by the channel coding into account.

Future digital subscriber line (DSL) systems are supposed to achieve higher data rates by using interference cancellation techniques. Since far-end crosstalk (FEXT) is the major impairment in DSL systems based on frequency division duplexing, mitigation of this interference can increase the signal- to-interference-and-noise ratio, and therefore significantly boost the data rate. For simulation and evaluation of FEXT cancellation algorithms, more accurate modelling of the multiple-input- multiple-output (MIMO) crosstalk channels is needed. Until now, DSL standards usually rely on the 99% worst case modelling of crosstalk power-sum for the lines in a binder. This paper proposes a parametric stochastic FEXT model based on the sum-of-sinusoids approach for the pair-to-pair crosstalk among multiple lines in a binder, which shows a more realistic behaviour than the worst case models.

Bit error probability (BEP) and word error probability (WEP) are used as reliability-based retransmission criteria in conjunction with hybrid ARQ (HARQ) protocols. Instead of exploiting an error-detecting code, the decision for a retransmission is based on the error probability of the decoded word, which can be calculated in or after the decoding process.

In this paper, we propose an iterative soft channel estimation and data detection algorithm based on a factor graph. Channel coefficients as well as data symbols are treated as variable nodes and are all estimated in a low-complexity element-wise manner. Applying asymmetric LDPC codes, this algorithm is able to deliver ambiguity-free outputs for MIMO systems with or without training symbols. Training symbols are inherently utilized as a type of a priori information. This algorithm thoroughly relaxes the troublesome constraints on training design in the sense that an arbitrary (even zero) number of training symbols can be placed at arbitrary positions within a data burst.

We discuss recent results on discrete-time models for triply-selective multiple-input multiple-output (MIMO) Rayleigh fading channels. Our key finding is that a previously proposed model (Xiao et al., 2004), which allows for efficient computer simulations, is sufficiently accurate for a wide range of practical scenarios.

A multi-layer Interleave-division multiple access (IDMA) based system proposal is presented in order to enhance the universal terrestrial radio access (UTRA) uplink. Compatibility and co-existence with the current UTRA FDD mode are of primary importance. Although focus is on UTRA long term evolution and related research activities initiated by the 3rd generation partnership project (3GPP), multi-layer IDMA may be applied to enhance other direct-sequence code- division multiple access (DS-CDMA) systems, or may serve as an innovative air interface for future systems. As a novel feature, non-uniform power allocation on chip level is introduced.

In this paper, a low-complexity high-performance detection algorithm for multiple input multiple output (MIMO) channels with severe delay spread is proposed. This algorithm performs iterative data detection over factor graphs which apply an independence approximation as well as a Gaussian approximation. It is shown that this algorithm achieves a near-optimum BER performance for coded MIMO systems with severe delay spread. The computational complexity of this algorithm is strictly linear in the number of transmit antennas, the number of receive antennas, and the number of non-zero channel coefficients.

In this paper, we introduce a new method for calculating word error probabilities by means of a modified Viterbi decoder. The proposed method can be seen as an approximation of the reliability output Viterbi algorithm (ROVA). The results provided by the proposed algorithm are only slightly worse than that of the ROVA, given a significantly lower computational complexity especially for codes with large memory lengths. As a potential application, both algorithms are compared in reliability- based HARQ.

Physical layer constrained routing takes the conditions of the physical layers of a network into account when finding an appropriate route. The constraints are usually given by a routing metric, which describes the cost arising when transmitting via a link between two nodes. In nearly all wireless networks, on the physical layer channel coding is used to correct errors and to establish a reliable transmission in terms of error probabilities. In this paper, we introduce new routing metrics that are based on reliability information provided by a soft-output channel decoder. This is different from other approaches calculating error probabilities or link states by using channel statistics. In this paper, the actual error probability is calculated for a decoded packet rather than using a look-up table for a certain SNR or averages for certain channel statistics. The calculation and utilization of bit error probability, packet error probability, and the expected number of hops for a certain path or link are explained. Monte Carlo simulations indicate that using these routing metrics in wireless communications result in a significantly lower average packet delivery time than shortest path routing.

Error performance of distributed space–time codes used for mobile broadcasting systems consisting of multiple base stations is analysed. The base stations are assumed to operate in a simulcast mode, that is they simultaneously transmit the same message using the same carrier frequency. Mobile users within the intersection of the coverage areas, thus, enjoy a small probability of shadowing and a high probability of at least one line-of-sight (LOS) link. In effect, the base stations establish a virtual multiple-antenna system. The use of a distributed space-time code offers an additional spatial diversity gain. Considering a single user with a fixed (random) position, the impact of shadowing and LOS components on the error performance of the system is analysed and compared with a conventional multiple-antenna system with co-located transmit antennas. Specifically, the analysis shows that already a single LOS link significantly improves the overall system performance. In the case of shadowing, huge diversity gains are obtained in the distributed system. In a system with co-located transmitters, however, the performance improvements compared with a single-antenna system are rather small, when shadowing is taken into account. Altogether, it can be concluded that systems with distributed transmitters are typically superior to conventional multiple-antenna systems (due to macroscopic diversity), and that distributed space–time codes are superior to conventional simulcasting (due to microscopic diversity).

We analyze the correlation properties of the discrete-time triply-selective multiple-input multiple-output (MIMO) Rayleigh fading channel model, while triply-selective refers to selectivity in time, frequency, and space. A rigorous analysis of these correlation properties is indispensable to assess the performance of multiple-antenna techniques in realistic environments. To the best of the authors' knowledge, a similar analysis has not yet been presented in the literature.

In this paper, a receiver structure suitable for single-antenna cochannel interference cancellation in cellular TDMA networks is studied. The receiver under investigation consists of a novel linear prefilter followed by a nonlinear detector. The task of the prefilter is to suppress non-Gaussian interference and - simultaneously - to shorten the overall impulse response, whereas the task of the nonlinear detector is to cancel intersymbol interference. The prefilter is designed according to the principle of minimum mean square error estimation, without solving an eigenvalue problem. Optionally the prefilter can be designed utilizing available knowledge of all cochannels, knowledge of the channel of a desired user only, or with no channel knowledge at all. The nonlinear detector may be an arbitrary nonlinear equalizer, in the simplest case a memoryless detector. The receiver structure under investigation is compatible with conventional TDMA receivers ignoring cochannel interference. Performance results are presented for synchronous and asynchronous GSM/GPRS networks

In 3GPP GERAN, the performance specification of dual-antenna interference suppression techniques will be a subject of the downlink advanced receiver performance (DARP) phase II work item. In this study, we apply a filter-based single antenna interference suppression technique for downlink GSM 8PSK modulation assuming the terminal is equipped with two antennas. We evaluate the performance using a correlation-based spatial channel model and show the potential of suppressing the co-channel interference even for high correlation factors. Besides, a performance improvement can be observed if the phase angles of the correlation factors for the desired user and interferer are different in highly spatially correlated channels

Iterative channel estimation and data detection is a useful method to improve the channel estimation quality without sacrificing the bandwidth efficiency. Since both the known training symbols (non-blind) and the unknown data symbols (blind) are used for channel estimation, corresponding techniques are referred to as semiblind. If the channel estimator and data detector are both optimal in the sense of maximum-likelihood criterion, we may call the algorithm as maximum-likelihood (ML) semiblind channel estimation (SBCE). This paper deals with ML-SBCE for frequency-flat multi-input multi-output systems with focus on the channel estimation mean squared error (MSE) analysis. Through semi-analytical efforts, we will show that ML-SBCE is biased at low SNR and tends to be unbiased at high SNR. The reasons of biasing are the erroneous data detection and the correlation between the noise and the detection errors. Besides, we will show that the MSE performance of MLSBCE is also influenced by the noise-error correlation. Based on these analyses, possibilities to compensate the biasing as well as improve the MSE performance will be pointed out.

In this letter, single antenna co-channel interference cancellation for cellular time-division multiple access (TDMA) networks by means of joint delayed-decision feedback sequence estimation is studied. The performance is increased by a novel adaptive state allocation technique.

A channel estimation scheme for interleave-division multiple access (IDMA) systems is presented. Training and data are superimposed, i.e., no bandwidth expansion compared to a system without any training is necessary. A semi-blind linear minimum mean squared error (MMSE) channel estimator is derived. Monte Carlo simulation results are presented showing that the derived channel estimator in combination with the proposed training scheme is able to track fast-fading frequency-selective channels. As opposed to conventional channel estimation techniques, the BER performance even improves with increasing Doppler spread. An error performance close to the case of perfect channel knowledge can be achieved with high power efficiency

In this paper, a novel design of a receiver-side FIR prefilter is proposed which is able to jointly shorten the impulse responses of multiple co-channels given just one receive antenna. The prefilter coefficients are computed without explicitly solving an eigenvalue problem. In conjunction with a reduced-complexity multiuser detector, a good error performance can be achieved. As a possible application, co-channel interference cancellation for the EDGE system is studied. The prefilter, however, is also suitable for interference suppression in MIMO systems, for crosstalk suppression, or for reducing the length of the cyclic prefix in multitone systems, among other applications.

Sparse intersymbol-interference (ISI) channels are encountered in a variety of high-data-rate communication systems. Such channels have a large channel memory length, but only a small number of significant channel coefficients. In this paper, trellis-based equalization of sparse ISI channels is revisited. Due to the large channel memory length, the complexity of maximum-likelihood detection, e.g., by means of the Viterbi algorithm (VA), is normally prohibitive. In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality. In this new context, two known reduced-complexity algorithms for sparse ISI channels are recapitulated: The multi-trellis VA (M-VA) and the parallel-trellis VA (P-VA). It is shown that the M-VA, although claimed, does not lead to a reduced computational complexity. The P-VA, on the other hand, leads to a significant complexity reduction, but can only be applied for a certain class of sparse channels. In the second part of the paper, a unified approach is investigated to tackle general sparse channels: it is shown that the use of a linear filter at the receiver renders the application of standard reduced-state trellis-based equalizer algorithms feasible, without significant loss of optimality. Numerical results verify the efficiency of the proposed receiver structure

When the same data sequence is transmitted over two independent channels, or when a data sequence is transmitted twice but independently over the same channel, the independent observations can be combined at the receiver side. From an information-theory point of view, the overall mutual information between the data sequence and the received sequences represents a combination of the mutual information of the two channels. This concept is termed information combining. A lower bound and an upper bound on the combined information is presented, and it is proved that these bounds are tight. Furthermore, this principle is extended to the computation of extrinsic information on single code bits for a repetition code and for a single parity-check code of length three, respectively. For illustration of the concept and the bounds on information combining, two applications are considered. First, bounds on the information processing characteristic (IPC) of a parallel concatenated code are derived from its extrinsic information transfer (EXIT) chart. Second, bounds on the EXIT chart for an outer repetition code and for an outer single parity-check code of a serially concatenated coding scheme are computed.

In this paper, typical uplink scenarios in a cellular system are considered, where two cooperating mobile stations (serving, for example, as mobile relays) are transmitting the some information to a base station by using a distributed space-time coding scheme. Due to the distributed nature of the system, the transmitted signals are typically subject to different average path losses. For fixed distances between the mobile stations and the base station, the error performance of the distributed space-time coding scheme is determined analytically. Then, based on considerations concerning the spatial distribution of the mobile stations, analytical expressions for the distribution of the average path losses are derived and verified by means of simulations. These results are then used in order to compute the expected error performance of the system. It is shown that in most scenarios the average performance loss compared to a conventional multiple-antenna system with colocated antennas is less than 2 dB at a bit error rate of 10/sup -3/. The most significant performance losses occur for a large path-loss exponent.

Single antenna co-channel interference cancellation for cellular TDMA networks by means of joint delayed-decision feedback sequence estimation is proposed. The performance can be increased by a novel adaptive state allocation technique.

Information combining is applied to an accumulator to obtain bounds on the extrinsic information transfer (EXIT) functions without relying on Gaussian or other approximations for the distribution of the a-priori messages.

In this paper, fast techniques for computing the coefficients of a Kalman-based prefilter originally developed by Mulgrew are proposed. This prefilter approximates a minimum-phase overall impulse response. In conjunction with a reduced-complexity equalizer, near optimum performance compared to maximum-likelihood sequence estimation is demonstrated for wireless channels. We suggest an exact recursive solution as well as a new iterative solution. In both solutions, the computational complexity of calculating the prefilter coefficients is O(L/sub WMF/) with respect to L/sub WMF/, where L/sub WMF/ is the number of prefilter coefficients. For comparison, the complexity of the original algorithm is O(L/sub WMF//sup 3/).

In the first part, an iterative receiver for joint channel estimation and cochannel interference cancellation suitable for time-division multiple-access (TDMA) cellular radio systems is proposed. The receiver is based on joint maximum-likelihood sequence estimation (JMLSE). It is blind with respect to the data of the interfering users but training-based with respect to the data of the desired user and is therefore referred to as semiblind. As opposed to concurrent receiver structures where the number of receiving antennas must exceed the number of cochannel interferers, the proposed receiver is suitable for just one receive antenna. No knowledge on the burst structure of the interferer is used. Hence, the proposed receiver is suitable for (but not restricted to) the downlink in asynchronous networks. The proposed receiver is able to operate at a signal/interference ratio of 0 dB in asynchronous GSM/GPRS networks given a single dominating interferer. Furthermore, the results indicate that with proper interference cancellation it is not necessary to synchronize GSM/GPRS networks. In the second part, a lower bound on the bit error probability for joint maximum-likelihood sequence estimation in the presence of cochannel interference (CCI) and intersymbol interference (ISI) is presented. This lower bound is valid for an arbitrary number of interferers.

On basis of the GSM/GPRS system, we investigate the application of the delay diversity scheme. The delay diversity scheme is a simple special case of a space-time trellis code (STTC). Delay diversity may be offered by network operators even in existing systems, since the standard is not affected at all. The performance improvements obtainable by means of this technique are demonstrated both on basis of analytical and simulation results. A lower bound on the bit error probability is derived and an optimization of the intrinsic delay parameter is considered.

On the basis of the GSM/EDGE system, the application of space-time coding techniques is investigated, especially with regard to aspects of compatibility with current specifications. In this paper, simple special cases of space-time trellis codes and space-time block codes are considered, namely the delay-diversity and the Alamouti scheme. The performance improvements obtainable by means of these techniques in the case of a typical urban (TU) wireless channel are demonstrated both on the basis of analytical and simulation results. For the delay-diversity scheme, a lower bound on the bit-error probability is derived and an optimization of the intrinsic delay parameter is considered. For the Alamouti scheme, a novel trellis-based soft-output equalization and detection algorithm is presented, which is of same complexity as in the single transmit antenna case. Moreover, in the case of the Alamouti scheme, appropriate training sequence pairs are required. In this context, optimized partner sequences with respect to the eight GSM sequences are introduced yielding pairs that significantly outperform any pair of GSM sequences. Furthermore, the sensitivity of the delay-diversity and the Alamouti scheme to a time-variant TU channel is addressed as well as to nonperfect knowledge of the channel coefficients at the receiver.

A joint channel and data estimator suitable for wireless differential phase shift keying (DPSK) systems with short block sizes is proposed. No training data symbols are necessary, but a priori information may be incorporated, if available. The basic structure consists of an iterative approximation of the joint maximum-likelihood (ML) solution, which is enhanced by adaptive filtering, by shift operations, by coefficient annealing, as well as by using a priori information. To our best knowledge, this is the first time that: (i) a priori information of the data has been exploited in the context of blind equalization; and that (ii) the DPSK/ISI super trellis has been explicitly exploited for blind equalization rather than doing differential decoding for resolving the problem of phase ambiguity.

By means of the mean reliability (defined as the mean of the absolute values of log-likelihood ratios), a new design of parallel concatenated "turbo" codes is proposed. This criterion allows us to describe the behavior of the constituent decoders and, furthermore, to predict the behavior of the iterative decoder for large block lengths. The mean reliability can also be used as a stopping criterion.

This paper presents a new fractionally-spaced maximum a posteriori (MAP) equalizer for data transmission over frequency-selective fading channels. The technique is applicable to any standard modulation technique. The MAP equalizer uses an expanded hypothesis trellis for the purpose of joint channel estimation and equalization. The fading channel is estimated by coupling minimum mean square error techniques with the (fixed size) expanded trellis. The new MAP equalizer is also presented in an iterative (turbo) receiver structure. Both uncoded and conventionally coded systems (including iterative processing) are studied. Even on frequency-flat fading channels, the proposed receiver outperforms conventional techniques. Simulations demonstrate the performance of the proposed equalizer.

Multiple symbol differential detection is known to fill the gap between conventional differential detection of MPSK (M-DPSK) and coherent detection of M-PSK with differential encoding (M-DEPSK). Emphasis has been so far on soft-input/hard-output detectors applied in uncoded systems. In this paper, we investigate a receiver structure suitable for coded DPSK signals on static and time-varying channels. The kernel is an a posteriori probability (APP) DPSK demodulator. This demodulator accepts a priori information and produces reliability outputs. Due to the availability of reliability outputs, an outer soft-decision channel decoder can be applied. Due to the acceptance of a priori information, if the outer channel decoder also outputs reliability information, iterative ("turbo") processing can be done. The proposed "APP DPSK demodulator" uses linear prediction and per-survivor processing to estimate the channel response. The overall transmission scheme represents a type of serial "turbo code," with a differential encoder concatenated with a convolutional code, separated by interleaving. The investigated system has the potential to improve the performance of coherent PSK without differential encoding and perfect channel estimation for fading cases! Only a small number of iterations are required. The receiver under investigation can be applied to several existing standards without changing the transmission format. Results are presented for uncoded and convolutionally coded 4-DPSK modulation transmitted over the Gaussian channel and the Rayleigh flat-fading channel, respectively.

The potential of pilot-symbol-aided channel estimation in two dimensions are explored. In order to procure this goal, the discrete shift-variant 2-D Wiener filter is derived and analyzed given an arbitrary sampling grid, an arbitrary (but possibly optimized) selection of observations, and the possibility of model mismatch. Filtering in two dimensions is revealed to outperform filtering in just one dimension with respect to overhead and mean-square error performance. However, two cascaded orthogonal 1-D filters are simpler to implement and shown to be virtually as good as true 2-D filters.

Mobile radio systems with high power and bandwidth efficiency can be designed by the combination of code division multiple access (CDMA) and multi-carrier modulation. The problem of channel estimation in the downlink of a multi-carrier (MC)-CDMA system is investigated. We study pilot-symbol-aided channel estimation in two dimensions using two cascaded one-dimensional FIR filters, dimensioned as Wiener filters for the worst case channel conditions. The performance with real channel estimation is demonstrated for various mobile radio environments and velocities. Furthermore, we examine the accuracy of a simple approximation to evaluate the system performance with real channel estimation, where the analysis of the channel estimation is separated from the evaluation of the remaining mobile radio system.

We propose a recursive algorithm to compute the joint maximum a-posteriori (MAP) probability of a subblock of N consecutive symbols (i.e., a sliding window of length N) of a finite-state discrete-time Markov process of length K/spl ges/N observed in white noise given the whole block is received. This "optimal subblock-by-subblock detector" (OBBD, "vector MAP") is a generalization of the "optimal symbol-by-symbol detector" (OSSD, "symbol-by-symbol MAP"), which is obtained for N=1. The new algorithm improves applications with outer stage processing. This is indicated by investigating the average mutual information of a convolutional coding system. An example shows that the gain (in terms of average mutual information) by using joint probabilities could even exceed the gain by delivering soft OSSD outputs instead of hard outputs.>