We propose a new way of operating a cell-free massive multiple-input multiple-output (MIMO) system with multiple central processing units (CPUs). In scalable cell-free massive MIMO systems, each user equipment (UE) is served by a cluster of access points (APs). The required coordination of the APs is often modeled to be done at a single cloud CPU, but in practice this cloud CPU consists of many physical entities which can be distributed over a large geographical area. Contrary to other works, in this work we explicitly model the cloud CPU as multiple physical CPUs, where each AP is connected to one of these CPUs via a low-latency fronthaul link. The CPUs are connected via midhaul links which may have a much higher latency. Hence, extensive cooperation between APs connected to the same CPU is possible while cooperation between APs connected to different CPUs is limited. We outline a joint initial access, pilot assignment, and clustering procedure in such a network. Further, we propose a new hybrid receive combining approach that takes advantage of this structure and allows for a scalable implementation of the system. We compare the performance of our proposed hybrid cell-free system with multiple CPUs to both the idealized centralized cloud CPU version and to the fully distributed cell-free system. Our results show that the proposed system can achieve a performance close to the idealized cloud CPU system while being practically feasible. Furthermore, our hybrid approach substantially outperforms a cellular Distributed Antenna System (DAS) with no CPU cooperation.

Wireless Avionics Intra-Communications (WAIC) has been proposed to partially replace costly wiring in future generations of aircraft. Besides the allocation of a frequency range and several performance figures, there is no standardized transmission scheme so far. For the design of suitable physical layer techniques, comprehensive models are required to perform realistic link-level simulations for the aircraft environment. This paper presents a wideband channel model for in-cabin WAIC systems, derived from channel measurements performed in an Airbus A321 cabin. The model is validated by comparing experimentally measured and simulated frame error rates using the proposed model in an IEEE 802.11a-based OFDM transmission.

The deployment of satellite mega constellations may enable global coverage, even for direct transmission from satellite to handheld device. Such transmissions come with increased demands in power efficiency. The traveling wave-tube amplifier (TWTA) in satellite payloads fundamentally limits the transmit power and causes distortions to the transmit signal when power efficient transmission close to amplifier saturation is desired. This work introduces a novel joint training paradigm of constellation, amplifier power back-off and data predistortion to maximize the throughput of single carrier transmission over transparent satellite links. The joint design is enabled by means of communication autoencoders, where transmitter and receiver components are adapted together to achieve the lowest bit error rate (BER). We show how constrained constellation optimization can improve performance on selected configurations from the broadcasting standard DVB-S2X. Results are presented in terms of coded BER and information rates.

This paper presents novel techniques for improving the error correction performance and reducing the complexity of coarsely quantized 5G LDPC decoders. The proposed decoder design supports arbitrary message-passing schedules on a base-matrix level by modeling exchanged messages with entry-specific discrete random variables. Variable nodes (VNs) and check nodes (CNs) involve compression operations designed using the information bottleneck method to maximize preserved mutual information between code bits and quantized messages. We introduce alignment regions that assign the messages to groups with aligned reliability levels to decrease the number of individual design parameters. Group compositions with degree-specific separation of messages improve performance by up to 0.4 dB. Further, we generalize our recently proposed CN-aware quantizer design to irregular LDPC codes and layered schedules. The method optimizes the VN quantizer to maximize preserved mutual information at the output of the subsequent CN update, enhancing performance by up to 0.2 dB. A schedule optimization modifies the order of layer updates, reducing the average iteration count by up to 35 %. We integrate all new techniques in a rate-compatible decoder design by extending the alignment regions along a rate-dimension. Our complexity analysis shows that 2-bit decoding can double the area efficiency over 4-bit decoding at comparable performance.

Satellite transponders operate with traveling wave-tube amplifiers (TWTAs) which nonlinearly distort the transmit signal, particularly when transmission power close to amplifier saturation is desired. Hence, the concatenation of pulse shaping filter, input multiplexer (IMUX) filter, TWTA, output multiplexer (OMUX) filter and receive filter causes distorted decision regions and severe intersymbol interference (ISI) at the receive filter output. The standards DVB-S2 and DVB-S2X recommend setups for transparent satellite channels. Here, the distortion is traditionally compensated using a lookup table-based centroid data predistortion algorithm. In this work, we propose an improved approach for data predistortion based on bidirectional recurrent neural networks (BRNNs). We show the superiority of BRNN data predistortion over centroid-based predistortion by comparing information rates and bit error rates on recommended setups from the DVB-S2 standard. Furthermore, we show how BRNN predistortion enables ISI compensation for high order modulations from DVB-S2X, where the traditional lookup table approach fails due to infeasible memory requirements.

This paper proposes a turbo equalizer for inter-symbol interference channels (ISI) that uses coarsely quantized messages across all receiver components. Lookup tables (LUTs) carry out compression operations designed with the information bottleneck method aiming to maximize relevant mutual information. The turbo setup consists of an equalizer and a decoder that provide extrinsic information to each other over multiple turbo iterations. We develop simplified LUT structures to incorporate the decoder feedback in the equalizer with significantly reduced complexity. The proposed receiver is optimized for selected ISI channels. A conceptual hardware implementation is developed to compare the area efficiency and error correction performance. A thorough analysis reveals that LUT-based configurations with very coarse quantization can achieve higher area efficiency than conventional equalizers. Moreover, the proposed turbo setups can outperform the respective non-turbo setups regarding area efficiency and error correction capability.

The so-called fast polar decoding schedules are meant to improve the decoding speed of the sequential-natured successive cancellation list decoders. The decoding speedup is achieved by replacing various parts of the serial decoding process with efficient special-purpose decoder nodes. This work incorporates the fast decoding schedules for polar codes into their quantized finite alphabet decoding. In a finite alphabet successive cancellation list decoder, the log-likelihood ratio computations are replaced with lookup operations on low-resolution integer messages. The lookup tables are designed using the information bottleneck method. It is shown that the finite alphabet decoders can also leverage the special decoder nodes found in the literature. Besides their inherent decoding speed improvement, the use of these special decoder nodes drastically reduces the number of lookup tables required to perform the finite alphabet decoding. In order to perform quantized decoding using lookup operations, the proposed decoders require up to 93% less unique lookup tables as compared to the ones that use the conventional successive cancellation schedule. Moreover, the proposed decoders exhibit negligible loss in error correction performance without necessitating alterations to the lookup table design process.

In high mobility scenarios, cell selection in preparation for handover is a non-trivial task. Particularly a user equipment (UE) installed on board a passenger aircraft faces a high handover frequency and strong interference from a terrestrial radio access network (RAN) due to the high velocities and altitude of the aircraft. Conventional reference signal received power (RSRP) measurements do not reflect the potential for interference mitigation in the aeronautical regime. Therefore, we incorporate beamforming into conditional handovers such that signal to interference power ratio (SIR) measurements are used for the cell selection in order to find a serving base station which provides the best SIR after beamforming. Link budget simulations to evaluate the proposed method are performed using real passenger aircraft flight trajectories and real world base station deployment data. The proposed method improves the link availability by 32 percentage points compared to the conventional handover procedure while decreasing the average handover interval by up to 0.6 s at the high UE velocities. We also show that a handover candidate filtering rule based on the direction of flight offers a trade-off by increasing the average handover interval by up to 2.3 s at the cost of availability.

Traveling-wave tube amplifiers (TWTAs) are common power amplifiers in satellite communications. Saturation and warping effects caused by the nonlinearity make constellation shaping a non-trivial task, when nonlinear amplifiers are involved in the channel. Constellation shaping optimizes geometry and probability of occurrence of constellation points to maximize the mutual information. This paper newly introduces constellation shaping using neural networks to satellite communications by showing its benefits on channels with TWTAs. We study how the TWTA nonlinearity impacts learned constellations both in terms of geometry and point probability of occurrence. The peak-power constraint introduced by TWTA saturation leads to a decreased impact of probabilistic shaping compared to geometric shaping which is illustrated using mutual information curves and displayed constellations. The tuning of the input back-off (IBO) of the TWTA causes an additional trade-off between transmit power at the cost of more severe nonlinear effects. This paper makes a new contribution by training IBO and transmit constellation jointly for TWTAs. By displaying constellations at different IBOs the interdependency of constellation shaping and IBO optimization is motivated. Total degradation analysis on mutual information curves resulting from the cross-entropy loss of the autoencoder illustrates the impact of joint IBO training and constellation shaping. A study of coded bit error rate performance and comparison to conventional constellations with memoryless predistortion emphasizes the effectiveness of training IBO and constellation jointly using autoencoders.

This feasibility study deals with Air to Ground (ATG) communications between a 5G type Terrestrial Mobile Communication Network (TMCN) and an User Equipment (UE) installed onboard a passenger aircraft in approach to an airport. The network model is based on real gNodeB (gNB) deployment data and optimized for serving terrestrial UEs. The evaluation concerns a scenario in which the passenger aircraft is supported by a remote operator via an ATG link to assist its safe landing. To mitigate the interference expected to arise in ATG communications, the aircraft is assumed to be equipped with an Active Electronically Scanned Array (AESA). The simulation results discussed in this paper address the Signal to Interference power Ratio (SIR), link availability, and handover process during ATG communications along representative approach trajectories for different AESA configurations and levels of support by the TMCN. It is shown that SIR above 30 dB and link availability upto 97.7% can be achieved by using an 8 x 8 AESA at the aircraft when the network provides support in the form of dedicated radio resources. It is observed that the frequency of handovers increases with improving link quality.

An implementation-efficient finite alphabet decoder for polar codes relying on coarsely quantized messages and low-complexity operations is proposed. Typically, finite alphabet decoding performs concatenated compression operations on the received channel messages to aggregate compact reliability information for error correction. These compression operations or mappings can be considered as lookup tables. For polar codes, the finite alphabet decoder design boils down to constructing lookup tables for the upper and lower branches of the building blocks within the code structure. A key challenge is to realize a hardware-friendly implementation of the lookup tables. This work uses the min-sum implementation for the upper branch lookup table and, as a novelty, a computational domain implementation for the lower branch lookup table. The computational domain approach drastically reduces the number of implementation parameters. Furthermore, a restriction to uniform quantization in the lower branch allows a very hardware-friendly compression via clipping and bit-shifting. Its behavior is close to the optimal non-uniform quantization, whose implementation would require multiple high-resolution threshold comparisons. Simulation results confirm excellent performance for the developed decoder. Unlike conventional fixed-point decoders, the proposed method involves an offline design that explicitly maximizes the preserved mutual information under coarse quantization.

We propose a novel approach based on deep learning to address the hidden node problem which occurs in the coexistence of aeronautical communication standards. The modern aeronautical communication standard L-band Digital Aeronautical Communications System (LDACS) in Air-Air (A/A) mode needs to share spectrum with the Distance Measuring Equipment (DME), which is a legacy system. As DME is safety-critical, causing interference on it must be avoided for all newly proposed aeronautical systems spectrally coexisting with it. Recently, cognitive radio techniques have been proposed for LDACS A/A to access spectrum dynamically and to overcome the limitations of static approaches. For this, a Recurrent Neural Network (RNN) was trained to predict idle time slots on those frequency bands, where both systems operate. By exploiting patterns in the spectrum access of DME, a promising amount of idle resources could be predicted. However, previous approaches would perform poorly in a real-world deployment, as they did not take the hidden node problem into account.This paper formulates the hidden node problem for the case that an LDACS A/A user is within communication range of a DME ground station, but not within range of all airborne DME users connected to it. Through statistical analysis, we underline the problem’s significance in practical cases. We simulate the coexistence of the two systems from a channel access perspective, taking signal propagation and the behavior of the ground station into account. Further, we present an RNN that is able to predict the channel access of hidden nodes. The key idea of our algorithm is to exploit the fact that while DME request pulses from airborne users may appear as hidden, response pulses from the ground station will be visible. Our results show that by inferring DME request channel activity from the response channel, the hidden node problem can be overcome effectively. By using our approach, nearly the same performance can be achieved as in the idealized case where all nodes are visible.

For improving coarsely quantized decoding of LDPC codes, we propose a check node aware design of the variable node update. In contrast to previous works, we optimize the variable node to explicitly maximize the mutual information preserved in the check-to-variable instead of the variable-to-check node messages. The extended optimization leads to a significantly different solution for the compression operation at the variable node. Simulation results for regular LDPC codes confirm that the check node aware design, especially for very coarse quantization with 2- or 3-bit messages, achieves performance gains of up to 0.2 dB - without additional hardware costs. We also show that the 2-bit message resolution enables a very efficient implementation of the check node update, which requires only 2/9 of the 3-bit check node's transistor count and reduces the signal propagation delay by a factor of 4.

Recently, low-resolution LDPC decoders have been introduced that perform mutual information maximizing signal processing. However, the optimal quantization in variable and check nodes requires expensive non-uniform operations. Instead, we propose to use uniform quantization with a simple hardware structure, which reduces the complexity of individual node operations approximately by half and shortens the decoding delay significantly. Our analysis shows that the loss of preserved mutual information resulting from restriction to uniform quantization is very small. Furthermore, the error rate simulations with regular LDPC codes confirm that the uniform quantization causes only minor performance degradation within 0.01 dB compared to the non-uniform alternative. Due to the complexity reduction, especially the proposed 3-bit decoder is a promising candidate to replace 4-bit conventional decoders.

This paper leverages the information bottleneck method to design receivers for ISI channels working with coarsely quantized messages. The proposed equalizer is based on the forward-backward algorithm. In contrast to conventional approaches, state reliability information is exchanged with a finite alphabet message instead of a real-valued probability vector. Moreover, each node update performs only a single lookup instead of many arithmetic operations. The lookup table construction aims at maximizing the preserved relevant mutual information. We propose a symmetric KL-means IB algorithm applied in an iterative discrete density evolution procedure. Based on the resulting distributions, a new static design technique creates three reusable symmetric lookup tables to perform forward, backward and final node updates. This way, coarse quantization is an integral part of the system design in the first place rather than a separate follow-up process. Two different ISI channel setups show that the proposed equalizers can achieve comparable performance to the high-resolution alternatives. Remarkably, the state metrics require an order of magnitude fewer bits, which potentially improves area and energy efficiency. Also, a lookup table sharing approach is presented to spread the implementation cost across sub-block equalizers operating in parallel.

In aeronautical communications, legacy systems often only use a small fraction of their historically assigned frequency spectra sparsely over time. Novel systems such as L-band Digital Aeronautical Communications System (LDACS) Air-Air (A/A) need to coexist with legacy systems, and must ensure not to cause excessive interference. The Distance Measuring Equipment (DME) is the most critical legacy system in this case. To analyze the potential number of idle communication resources in LDACS A/A and DME coexistence, we propose a statistical model, which reveals a substantial number of opportunities. Motivated by the statistical properties of the co-existence scenario, we propose a Recurrent Neural Network (RNN) to predict DME patterns reliably. Our architecture is based on a combination of Long Short-Term Memory (LSTM) and dense layers and was found with the help of hyperparameter optimization techniques. The predictor is trained and evaluated on a synthetic data set using realistic DME parameters. Furthermore, we introduce a baseline algorithm for comparison, which makes perfect predictions on a simplified periodic data set but breaks down for realistic scenarios. We argue that our Deep Learning approach can be used in realistic scenarios to detect idle resources given a strict constraint on correctly predicted busy resources.

Next-generation satellite communication systems require high spectral efficiency to meet future data rate requirements. Faster-than-Nyquist signaling offers improvement potential by exploiting excess bandwidth of the transmit pulses at the price of inter-symbol interference. This paper studies sophisticated receiver structures that lead to linear discrete minimum phase models with uncorrelated noise. They enable equalization of inter-symbol interference with feasible complexity in trellis-based decoders. Another challenge is nonlinear distortion in the transmit signal caused by the satellite’s high power amplifier. The distortions are accounted for using a linearized Volterra filter model. Finally, a comparison to conventional methods shows that the new approach yields considerable performance gains.

This paper uses the reconstruction-computation-quantization (RCQ)paradigm to decode low-density parity-check (LDPC) codes. RCQ facilitates dynamic non-uniform quantization to achieve good frame error rate (FER) performance with very low message precision. For message-passing according to a flooding schedule, the RCQ parameters are designed by discrete density evolution. Simulation results on an IEEE 802.11 LDPC code show that for 4-bit messages, a flooding Min Sum RCQ decoder outperforms table-lookup approaches such as information bottleneck (IB) or Min-IB decoding, with significantly fewer parameters to be stored. Additionally, this paper introduces layer-specific RCQ, an extension of RCQ decoding for layered architectures. Layer-specific RCQ uses layer-specific message representations to achieve the best possible FER performance. For layer-specific RCQ, this paper proposes using layered discrete density evolution featuring hierarchical dynamic quantization (HDQ) to design parameters efficiently. Finally, this paper studies field-programmable gate array (FPGA) implementations of RCQ decoders. Simulation results for a (9472, 8192) quasi-cyclic (QC) LDPC code show that a layered Min Sum RCQ decoder with 3-bit messages achieves more than a 10% reduction in LUTs and routed nets and more than a 6% decrease in register usage while maintaining comparable decoding performance, compared to a 5-bit offset Min Sum decoder.

In recent years coarsely quantized LDPC decoding using a flooding schedule has been extensively studied. However, there exist few works addressing coarse quantization for a layered schedule, which enables improved convergence speed of the message passing algorithm. The layered schedule can especially be beneficial for high throughput applications like fiber optical systems. This paper presents innovative layered decoding approaches, where the information bottleneck method is used for the design of different coarsely quantized decoder architectures. The varieties of investigated node implementations include lookup tables, computational domain techniques as well as reduced complexity approximations. All structures are designed offline using a layered discrete density evolution method. The performance of multiple node architectures is investigated in terms of evolution of mutual information in the design phase and in terms of error rates. We focus in this paper on regular quasi-cyclic codes. Our simulations running on GPUs also allow insights into the error floor behavior.

Massive Internet of Things (IoT) is foreseen to introduce a plethora of applications for a fully connected world. Heterogeneous traffic is envisaged, where packets generated at each device should be differentiated and served according to their priority. This article develops a novel priority-aware spatiotemporal mathematical model to characterize massive IoT networks with uplink prioritized multistream traffic (PMT). Stochastic geometry is utilized to account for the macroscopic network-wide mutual interference between the coexisting devices. Discrete-time Markov chains (DTMCs) are employed to track the microscopic evolution of packets within each priority queue. To provide a systematic and tractable model, we decompose the prioritized queueing model at each device to a single-queue system with a server vacation. To this end, the IoT PMT network is modeled as spatially interacting vacation queues. Dedicated and shared channel priority-aware access strategies are presented. A priority-agnostic scheme is used as a benchmark to highlight the impact of prioritized uplink transmission on the performance of different priorities in terms of transmission probabilities and delays. Additional performance metrics as the average number of packets, the peak age of information, delay distribution, and Pareto frontiers for different parameters are presented, which give insights on the stable operation of uplink IoT networks with PMT.

In this paper upper and lower bounds on the probability of decoding failure under maximum likelihood decoding are derived for different (nonbinary) Raptor code constructions. In particular four different constructions are considered; (i) the standard Raptor code construction, (ii) a multi-edge type construction, (iii) a construction where the Raptor code is nonbinary but the generator matrix of the LT code has only binary entries, (iv) a combination of (ii) and (iii). The latter construction resembles the one employed by RaptorQ codes, which at the time of writing this article represents the state of the art in fountain codes. The bounds are shown to be tight, and provide an important aid for the design of Raptor codes.

This paper proposes a finite-precision decoding method for low-density parity-check (LDPC) codes that features the three steps of Reconstruction, Computation, and Quantization (RCQ). Unlike Mutual-Information-Maximization Quantized Belief Propagation (MIM-QBP), RCQ can approximate either belief propagation or Min-Sum decoding. MIM-QBP decoders do not work well when the fraction of degree-2 variable nodes is large. However, sometimes a large fraction of degree-2 variable nodes is used to facilitate a fast encoding structure, as seen in the IEEE 802.11 standard and the DVB-S2 standard. In contrast to MIM-QBP, the proposed RCQ decoder may be applied to any off-the-shelf LDPC code, including those with a large fraction of degree-2 variable nodes. Simulations show that a 4-bit Min-Sum RCQ decoder delivers frame error rate (FER) performance within 0.1 dB of floating point belief propagation (BP) for the IEEE 802.11 standard LDPC code in the low SNR region. The RCQ decoder actually outperforms floating point BP and Min-Sum in the high SNR region were FER less than 10-5. This paper also introduces Hierarchical Dynamic Quantization (HDQ) to design the time-varying non-uniform quantizers required by RCQ decoders. HDQ is a low-complexity design technique that is slightly sub-optimal. Simulation results comparing HDQ and optimal quantization on the symmetric binary-input memoryless additive white Gaussian noise channel show a mutual information loss of less than 10-6bits, which is negligible in practice.

This paper addresses the problem of multitarget tracking using a network of mobile sensors with unknown positions. In contrast to commonly-used approaches which split the sensor localization and target tracking into two different sub-problems, we propose a holistic approach for joint localization and tracking. The theory of graphical models is used to describe the statistical relationship between sensors, targets, and measurements. To jointly infer the states of sensors and targets, we use the statistical processing of belief propagation.

The new 5G communications standard increases data rates and supports low-latency communication that places constraints on the computational complexity of channel decoders. 5G low-density parity-check (LDPC) codes have the so-called protograph-based raptor-like (PBRL) structure which offers inherent rate-compatibility and excellent performance. Practical LDPC decoder implementations use message-passing decoding with finite precision, which becomes coarse as complexity is more severely constrained. Performance degrades as the precision becomes more coarse. Recently, the information bottleneck (IB) method was used to design mutual-information-maximizing lookup tables that replace conventional finite-precision node computations. The IB approach exchanges messages represented by integers with very small bit width. This paper extends the IB principle to the flexible class of PBRL LDPC codes as standardized in 5G. The extensions include puncturing and rate-compatible IB decoder design. As an example of the new approach, a 4-bit information bottleneck decoder is evaluated for PBRL LDPC codes over a typical range of rates. Frame error rate simulations show that the proposed scheme outperforms offset min-sum decoding algorithms and operates very close to double-precision sum-product belief propagation decoding.

Timely message delivery is a key enabler for Internet of Things (IoT) and cyber-physical systems to support wide range of context-dependent applications. Conventional time-related metrics, such as delay, fails to characterize the timeliness of the system update or to capture the freshness of information from application perspective. Age of information (AoI) is a time-evolving measure of information freshness that has received considerable attention during the past years. In the foreseen large-scale and dense IoT networks, joint temporal (i.e., queue aware) and spatial (i.e., mutual interference aware) characterization of the AoI is required. In this work we provide a spatiotemporal framework that captures the peak AoI for large scale IoT uplink network. To this end, the paper quantifies the peak AoI for large-scale cellular network with Bernoulli uplink traffic. Simulation results are conducted to validate the proposed model and show the effect of traffic load and decoding threshold. Insights are driven to characterize the network stability frontiers and the location-dependent performance within the network.

In this work, non-binary polar codes are investigated for input alphabets sizes being a prime number. The similarities and differences in the encoding and decoding process are shown with respect to binary polar codes. The performance of polar codes over GF(3) and GF(5) in combination with 3-PSK and 5-PSK is evaluated for short messages in comparison to binary polar codes with common phase shift keying constellations.

Timely message delivery is a key enabler for Internet of Things (IoT) and cyber-physical systems to support a wide range of context-dependent applications. Conventional time-related metrics (e.g., delay and jitter) fail to characterize the timeliness of the system update. Age of Information (AoI) is a time-evolving metric that accounts for the packet interarrival and waiting times to assess the freshness of information. In the foreseen large-scale IoT networks, mutual interference imposes a delicate relation between traffic generation patterns and transmission delays. To this end, we provide a spatiotemporal framework that captures the peak AoI (PAoI) for the large-scale IoT uplink network under time-triggered (TT) and event-triggered (ET) traffic. Tools from the stochastic geometry and queueing theory are utilized to account for the macroscopic and microscopic network scales. Simulations are conducted to validate the proposed mathematical framework and assess the effect of traffic load on the PAoI. The results unveil a counter-intuitive superiority of the ET traffic over the TT in terms of PAoI, which is due to the involved temporal interference correlations. Insights regarding the network stability frontiers and the location-dependent performance are presented. Key design recommendations regarding the traffic load and decoding thresholds are highlighted.

Recently, a novel lookup table based decoding method for binary low-density parity-check codes has attracted considerable attention. In this approach, mutual-information-maximizing lookup tables replace the conventional operations of the variable nodes and the check nodes in message passing decoding. Moreover, the exchanged messages are represented by integers with very small bit width. A machine learning framework termed the information bottleneck method is used to design the corresponding lookup tables. In this paper, we extend this decoding principle from binary to non-binary codes. This is not a straightforward extension but requires a more sophisticated lookup table design to cope with the arithmetic in higher order Galois fields. Provided bit error rate simulations show that our proposed scheme outperforms the log-max decoding algorithm and operates close to sum-product decoding.

In today's chip-to-chip communication over multi-drop bus (MDB) interfaces, the trend of high-speed transmission has led to ever growing bandwidth requirements. An MDB channel is frequency-selective with deep notches at low frequencies, namely fnotch, which result from strong reflections at an MDB interface. In conventional chip-to-chip communication systems, the signal power is concentrated in the main lobe of the power spectral density (PSD), i.e., within |f|sNRZ, where fsNRZ= 1/T, is defined as the signal bandwidth. Limited by the complexity on the analog equalizer, fsNRZnotchshould hold to avoid strong distortions of the transmit signal. To cope with this problem, conventional signal processing methods, such as digital equalization and error control coding, are undesired in this application scenario due to extremely tight power and latency constraints. A spectrum shaping scheme named framed repetition code was proposed for the MDB channel of a single strong reflection. It aims at matching the signal spectrum to the channel magnitude response |H(f)|. However, a solution for multi-reflection channels, which is more often encountered in MDB interfaces, has not yet been investigated. This paper proposes a novel code construction algorithm for multi-reflection MDB channels. The algorithm is designed based on the root mean square delay spread. Using the constructed framed repetition codes, the degradation due to the channel filtering is reduced significantly. Simulations reveal a good performance. The data rate in multi-reflection MDB channels is increased by a factor of 2.6 without using any equalization compared to the conventional non-return-to-zero (NRZ) transmission scheme.

In the past, NLOS propagation was proven to be a source of distortion for radio-based positioning systems due to the lack of temporal and spatial resolution of previous cellular systems. Hence, every NLOS component was perceived as a perturbation for localization. Even though 5G is not yet standardized, a strong proposal, which has the potential to overcome the problem of limited temporal and spatial resolution, is the massive MIMO millimeter wave technology. We reconsider the role of NLOS components for position and orientation estimation in 5G millimeter wave MIMO systems. Our analysis is based on the concept of Fisher information. We show that for sufficiently high temporal and spatial resolution, NLOS components always provide position and orientation information that consequently increase position and orientation estimation accuracy. In addition, we show that the information gain of NLOS components depends on the actual location of the reflector or scatter. Our numerical examples suggest that the NLOS components are most informative about the position and orientation of a mobile terminal when the corresponding reflectors or scatterers are illuminated with narrow beams.

Millimeter wave signals with multiple transmit and receive antennas are considered as enabling technology for enhanced mobile broadband services in 5G systems. While this combination is mainly associated with achieving high data rates, it also offers huge potential for radio-based positioning. Recent studies showed that millimeter wave signals with multiple transmit and receive antennas are capable of jointly estimating the position and orientation of a mobile terminal while mapping the radio environment simultaneously. To this end, we present a message passing-based estimator which jointly estimates the position and orientation of the mobile terminal, as well as the location of reflectors or scatterers in the absence of the line-of-sight path. We provide numerical examples showing that our estimator can provide considerably higher estimation accuracy compared to a state-of-the-art estimator. Our examples demonstrate that our message passing-based estimator neither requires the presence of a line-of-sight path nor prior knowledge regarding any of the parameters to be estimated.

In practical implementations, message passing decoding of LDPC codes has to be implemented with finite precision, i.e., the messages are quantized with a small number of bits. This results in a significant performance degradation with respect to decoding with high-precision messages. Recently, we have proposed so-called information bottleneck decoders to design finite-precision decoders with error-correction performance close to high-precision belief-propagation decoding. Earlier works solely focus on the design of information bottleneck decoders for regular LDPC codes or specifically optimized irregular LDPC codes. In this paper, we extend the concept of information bottleneck decoders to irregular LDPC with arbitrary degree distribution. We show that this extension is not straightforward and requires an additional information-optimum step. Therefore, we devise a novel intermediate construction step which we call message alignment. Exemplary numerical simulations using an irregular LDPC code taken from the IEEE 802.11 standard show that incorporating message alignment in the construction yields a 4-bit information bottleneck decoder which performs only 0.15 dB worse than a double-precision belief propagation decoder and outperforms a min-sum decoder.

We present an information-theoretic approach to discrete signal processing called information-optimum signal processing for the example of a wireless receiver comprising LDPC decoding, channel estimation and detection. All operations are replaced by simple lookup tables and all messages which are exchanged between detection stages are unsigned integers. The lookup tables are designed offline using the information bottleneck concept of preserving relevant information. We show that the approach allows for simple signal processing with coarse quantization while achieving virtually the same performance as conventional signal processing approaches with high resolution, e.g. double precision. The contribution of the paper is a tutorial style explanation of the concept as well as an exemplary survey of applications and performance results which illustrate the potential of the concept.

To meet the goal of tenfold increase in spectral efficiency, interference cancellation and multiuser detection are expected to be important tasks of fifth-generation (5G) radio access systems. Both tasks can be realized by joint detection algorithms. However, joint detection algorithms such as maximum likelihood (ML) detection have a high computational complexity. Previous works have shown that joint detectors based on Markov chain Monte Carlo (MCMC) methods can achieve similar results compared to ML detection with a large reduction in the computational complexity for systems with a large number of streams or users. The purpose of this work is to present a MIMO joint detector based on MCMC methods and evaluate it within the constraints of LTE Advanced (LTE-A), namely, using at most 8 transmit antennas and 64-QAM modulation. The evaluation is done separately from the channel decoder. Moreover, the complexity of the presented algorithm is compared to the one of an ML detector. The results show that the proposed MIMO detector offers a similar detection error rate compared to an ML detector. Furthermore, it was observed that the complexity reduction is significant for systems with more than six transmit antennas.

Mutual information maximizing clustering techniques, like the information bottleneck method, enable message passing based on compressed but highly informative beliefs. In this paper, we apply this concept to joint maximum a-posteriori detection problems in sensor networks. We show that by leveraging the information bottleneck method both the amount of exchanged data and the complexity of the operations performed in the involved sensor nodes respectively the fusion center is significantly reduced. In the considered network, distributed sensor nodes quantize their measurements and forward only cluster indices instead of high-precision cluster representatives to a fusion center. Due to a spatial distribution of the sensor nodes, the quantizers in the sensor nodes are optimized to the actual, varying measurement conditions. Thus, the meaning of a cluster index is sensor-dependent and cannot be uniquely recaptured if the transmitting sensor is unknown. Using a technique which we call message alignment we resolve this ambiguity without transmitting additional information to the fusion center. Additionally, we present a novel iterative message alignment algorithm to solve the generalized message alignment problem. Although only 4-bit integer-valued cluster indices are transmitted, included simulations show that our proposed system encounters no considerable performance degradation compared to an optimum maximum a-posteriori detection strategy.

We consider cooperative position estimation in wireless networks as Bayesian inference problems in which nodes with unknown positions (agents) infer their positions based on distance measurements with respect to reference nodes (anchors) and other agents. In the indoor environment, the positions of agents can be constrained to finite geometric sets due to non-negative errors on the range estimates which arise from non-line-of-sight and multipath effects. First, we exploit the non-negativity of ranging errors in order to confine the positions of nodes to convex polygons. Subsequently, we exploit these polygons to relax the inference-based position estimation problems in terms of computational complexity. Using this two-stage approach, we show a tremendous reduction in terms of computational complexity, improvements in terms of localization accuracy, as well as the quicker convergence of the inference algorithm.

Recent works describe the design of discrete decoders for low-density parity-check codes by application of mutual information maximizing clustering algorithms in discrete density evolution. In the resulting discrete message passing decoders only integers are exchanged and node operations become simple lookup operations. Earlier works only describe discrete decoders for binary modulation schemes. This paper presents a new technique called message alignment which enables to design discrete decoders for higher-order modulation schemes. First, we design a channel output quantizer for quadrature amplitude modulation with the Information Bottleneck method. The quantizer attempts to preserve the relevant information on the modulation symbols. Afterwards, we illustrate that the assignment of several bits to one modulation symbol does not allow straightforward decoder design with the available discrete density evolution technique. The proposed message alignment solves this problem. The resulting discrete decoder is compared with state-of-the-art decoders using bit error rate simulations.

When time-based radio range measurements between network nodes are perturbed by a line-of-sight blocking obstacle, position estimation accuracy degrades significantly. The perturbation is caused by multipath propagation or excess delays since waves travel at slower speed while piercing the obstacles. Degradation of positioning accuracy results from position estimators not being aware of the line-of-sight blocking obstacle, i.e. the estimator assumes line-of-sight, but reality is non-line-of-sight. Hence the assumed model of the estimator differs from reality. In such a scenario, the model is said to be misspecified or mismatched. To assess the performance of position estimators operating under model misspecification, we employ the misspecified Cramér-Rao bound. We use the misspecified Cramér-Rao bound to predict the performance of a maximum likelihood position estimator under model mismatch. We numerically show a large increase of the mean squared error when the maximum likelihood estimator is misspecified. Based on our findings, we emphasize the importance of non-line-of-sight identification for designing position estimators.

In this paper, we analyze Luby transform (LT) and Raptor codes under inactivation decoding. A first-order analysis is introduced, which provides the expected number of inactivations for an LT code, as a function of the output distribution, the number of input symbols, and the decoding overhead. The analysis is then extended to the calculation of the distribution of the number of inactivations. In both cases, random inactivation is assumed. The developed analytical tools are then exploited to design LT and Raptor codes, enabling a tight control on the decoding complexity versus failure probability tradeoff. The accuracy of the approach is confirmed by numerical simulations.

In this paper q-ary Raptor codes under ML decoding are considered. An upper bound on the probability of decoding failure is derived using the weight enumerator of the outer code, or its expected weight enumerator if the outer code is drawn randomly from some ensemble of codes. The bound is shown to be tight by means of simulations. This bound provides a new insight into Raptor codes since it shows how Raptor codes can be analyzed similarly to a classical fixed rate serial concatenation.

Starting from a discrete density evolution scheme originally introduced by Brian M. Kurkoski et al. which we improved by applying the Information Bottleneck method, we recently presented results on message passing decoders for Low Density Parity Check codes that have much lower complexity than state of the art decoders. In the decoders all node operations are replaced by discrete message mappings of unsigned integers what yields a great complexity reduction. Anyway the decoders perform very close to belief propagation decoding. New included simulation results prove that using a 4 bit integer architecture these decoders loose only 0.1 dB over Eb/No in comparison to an exact belief propagation decoder applied to the quantized output of a Gaussian channel. The most important contribution of this paper is the derivation of the message mapping decoders from the sum-product algorithm. Until now these decoders are assumed to not be linked to this algorithm. In order to reveal the hidden connection, we explain the decoding principle of the message mapping decoders in general factor graphs.

A generic design method for low complexity receivers is presented. The method pairs factor graphs and the Information Bottleneck method in one framework. Consequently, the method is called Information Bottleneck Graphs. The main idea of Information Bottleneck Graphs is optimizing the flow of relevant information through the signal processors. In contrast to most topical receivers with high precision signal processing units, Information Bottleneck Graphs yield receivers purely working on unsigned integers. All signal processing degenerates to lookup operations in tables of integers. Information Bottleneck Graphs are exemplarily applied to develop a complete coherent receiver including analog-to-digital conversion, channel estimation and decoding of Low Density Parity Check codes that only works on unsigned integers. This receiver uses recently introduced discrete decoders for Low Density Parity Check codes.

We propose a novel message compression scheme for distributed nonparametric belief propagation in the context of cooperative localization. The messages to be exchanged in nonparametric belief propagation are given as particle representations. Communication constraints on the links make the transmission of entire particle representations prohibitive and require compressed (approximated) messages. We tackle the problem of compressing messages in an information-theoretic manner. For that reason, we define a relevant random variable and consider the mutual information shared with it for particle selection. We show that the shared mutual information depends on the choice of the particles. We propose a particle-based message compression algorithm that flexibly trades localization accuracy for communications, i.e. we incorporate a scalar parameter to adjust the degree of compression. We show that the proposed message compression method outperforms its parametrized counterpart in terms of localization accuracy for a well-chosen degree of compression, but comes at the expense of somewhat increased communications.

We propose a novel scheduler for an LTE-A network, which dynamically coordinates inter-cell interference for vulnerable users while limiting the effort of coordination. Vulnerable users are identified first, and then protected by assigning resources exclusively to them. Inter-cell interference coordination (ICIC) messages are transmitted to neighboring base stations (eNodeBs) via the X2 interface using LTE-typical constraints. The proposed scheduler and ICIC methods are compliant with the LTE and LTE-A standards. Closed-loop spatial multiplexing is used. The proposed scheduler is evaluated in a 2-tier heterogeneous network (HetNet). In comparison with the benchmark schedulers, simulation results for vulnerable users show a substantial gain in terms of throughput. In addition, the proposed scheduler guarantees a minimum data rate for a longer time.

Raptor code ensembles with linear random outer codes in a fixed-rate setting are considered. An expression for the average distance spectrum is derived and this expression is used to obtain the asymptotic exponent of the weight distribution. The asymptotic growth rate analysis is then exploited to develop a necessary and sufficient condition under which the fixed-rate Raptor code ensemble exhibits a strictly positive typical minimum distance. The condition involves the rate of the outer code, the rate of the inner fixed-rate Luby Transform (LT) code and the LT code degree distribution. Additionally, it is shown that for ensembles fulfilling this condition, the minimum distance of a code randomly drawn from the ensemble has a linear growth with the block length. The analytical results can be used to make accurate predictions of the performance of finite length Raptor codes. These results are particularly useful for fixed-rate Raptor codes under maximum likelihood erasure decoding, whose performance is driven by their weight distribution.

Cell-edge users suffer from harsh interference due to cell range expansion in a heterogeneous network. An information theoretic approach, namely the joint decoding, can in principle achieve the sum-capacity, but practical implementation remains a challenge. For example, a rate splitting (RS) scheme is an optimal alterative for multiple access channels, but it is not optimal for interference channels. This motivates us to develop a new technique. In this paper, a practical scheme called multi-layer rate splitting (MLRS) is introduced. We prove that this MLRS scheme can achieve the sum capacity when the interference channel satisfies certain conditions. Moreover, a power allocation algorithm is proposed to find the optimal power splitting ratios for the MLRS scheme. Simulation results show that the MLRS scheme is an effective alternative to exploit the benefit of the joint decoding for interference channels.

In this paper Raptor code ensembles with linear random precodes in a fixed-rate setting are considered. An expression for the average distance spectrum is derived and this expression is used to obtain the asymptotic exponent of the weight distribution. The asymptotic growth rate analysis is then exploited to develop a necessary and sufficient condition under which the fixed-rate Raptor code ensemble exhibits a strictly positive typical minimum distance.

Harsh interference is a major obstacle to achieve high capacity, especially when the state-of-the-art wireless networks intend to reuse the same resource. Information theoretic study shows that joint decoding with interference can achieve the sum capacity of a strong interference channel. However, the optimal joint decoding technique is too complex for practical applications, because it requires detecting and decoding all messages simultaneously. A two-user strong interference channel can be formed by two two- user multiple access channels (MACs), so a natural question arises as whether the decoding schemes optimal for the MAC remains optimal when applied to the interference channel. This paper investigates the relevant decoding techniques, namely the MAC rate splitting (RS) and iterative detection-decoding. Although these techniques have been shown to be optimal for MACs, we show that they cannot achieve the optimal performance anymore under interference channels.

We present a simple model of inactivation decoding for LT codes which can be used to estimate the decoding complexity as a function of the LT code degree distribution. The model is shown to be accurate in variety of settings of practical importance. The proposed method allows to perform a numerical optimization on the degree distribution of a LT code aiming at minimizing the number of inactivations required for decoding.

An LTE-Advanced Radio Access Network (RAN) would be able to activate an additional component carrier in a Licensed Spectrum Access (LSA) channel, allowing the mobile network operator (MNO) to boost capacity and data rates. But such a dynamic spectrum access strategy requires an effective method for estimating the impact of the LTA-Advanced RAN operation on the primary user and minimizing it. LTE-Advanced provides a useful feature, namely the minimization of drive test (MDT) reporting system, wherein the network collects measurement data performed by the user equipment. Each report provides position and signal strength information. In this paper, we compare the performance of some spatial interpolation methods that would be used to reconstruct the interference map of an LTE-Advanced RAN based on the MDT reports. In this way, the MNO can determine if an LSA channel could be used without potential harm to the primary user operation just by checking the estimated interference map. Furthermore, the performance of different interpolation methods for establishment of interference map is analyzed, when the MDT reports containing errors in the values or locations of the reported measurements. Estimation accuracy is also evaluated respect to spatial correlation.

The potential of Time Interference Alignment is investigated in this work, with particular reference to the attainable degrees of freedom. The K-user interference channel is considered, in which transmitters and receivers are placed randomly in a Euclidean space. A model for long delay networks is introduced and the degrees of freedom for different cases (with and without transmitter delay coordination) are evaluated. It is shown how time interference alignment can provide more degrees of freedom than TDMA when the transmitters jointly coordinate their transmission delay and the number of pairs is K ≥ 5. Closed form expressions are derived for several cases of interest which provide insight and useful predictions. This work is concluded with an investigation of the achievable degrees of freedom for multi-satellite networks, where it is shown that the results obtained under several assumptions do predict accurately the degrees of freedom in a real setting.

The main challenge when providing quality of services in future mobile networks is the low signal quality regimes, mainly experienced by the users located at the cell edge. The deployment of relay nodes promises significant improvement of the received signal quality and enables the establishment of QoS-aware services for such users. In this work, we propose and study the performance of a novel QoS-aware scheduling algorithm focusing on LTE-Advanced relay-enhanced networks. Using traffic with different QoS requirements for the downlink stream, we demonstrate that the proposed QoS-aware scheduling algorithm efficiently serve traffic with mixed rate and delay requirements. Moreover, it manages to maintain a reasonable service quality for all the users including the relayed users. We finalize this work comparing the performance of the proposed scheme to a reference scheduler.

Differential modulation causes a performance penalty compared to non-differential transmission, that can be diminished by a turbo loop between the differential demodulation and the forward error correction (FEC) decoder. To enhance this effect the low density parity check (LDPC) code has to be adjusted to this loop. Dependent on the used LDPC code the differential penalty can be completely or only partly overcome. In this paper we show under which conditions regarding code rate and complexity this penalty can be compensated completely. Therefore we use the extrinsic information transfer (EXIT) chart analysis and optimize the check edge degree distributions. We also show how we can construct LDPC codes that perform well in both the coherent and differential case. These codes are of interest, as in certain situation, like e.g. for a low quality channel estimation, due to fast changing channels, differential transmission is essential, while in other situations the channel estimation is sufficiently accurate to avoid differential transmission. Codes that perform well in both, the differential and coherent case allow to design only one code for both schemes. Finally we compare the optimized degree distributions in certain cases.

Spectrum licensing schemes are subject to fixed allocation, little sharing and long term holding, which is leading to the current problem of false scarcity. Considering current spectrum allocation methodologies and given the exponential growth of traffic demand, spectrum scarcity becomes problematic since in the near future current radio access technologies will not be able to cope with this traffic demand increase anymore. Thus, new licensing schemes must be designed to enable sharing of spectrum frequency, so that rarely used spectrum channels can be reallocated dynamically between primary users and mobile services. Cognitive radio based dynamic spectrum access is foreseen as a solution to this intricate problem. Further, simple dynamic spectrum access schemes could be integrated into an LTE-Advanced mobile network, making use of carrier aggregation and the standardized user equipment measurement reports. In this paper, we focus on geographical division of spectrum for an LTE-Advanced network, where the spectrum is classified into Frequency/Location/Time bundles. These bundles work as independent tradeable units that can be aggregated and tailored to the needs of the participants in spectrum adjudication. Geographic locations are divided into a square grid, where an estimation of the Radio Environment Map based on propagation parameters is used to assess the sets of tradeable units assigned to a given mobile network operator. Furthermore we make an analysis of how spectrum exclusion areas can be characterized and defined in order to coexist with the operator's network.

In wireless networks, interference from adjacent base stations is usually a dominant factor for user performance degradation. A maximum likelihood (ML) detection can provide superior performance by jointly detecting the serving and interfering signals. However, the complexity is extremely high. This paper presents a soft tree pruning based fixed-complexity sphere decoder. It uses reliability information on bits to estimate a search radius and thereby prunes unpromising nodes in the early stage of the tree search detection to reduce complexity. Based on the soft tree pruning method, the fixed-complexity sphere decoder can still work in a fully pipelined mode. Simulation results show that the soft tree pruning based fixed-complexity sphere decoder can provide a better trade-off between complexity and performance in interference-limited scenarios.

Low-rate non-binary low-density parity-check (LDPC) codes for coherent and blockwise non-coherent additive white Gaussian noise (AWGN) channels are developed. The proposed construction is based on the concatenation of non-binary outer LDPC codes with inner binary codes. In case the binary codes are chosen to be Hadamard or Reed-Muller (RM) codes, the complexity of the decoding scheme is considerably reduced. An asymptotic analysis of the concatenation with help of composite capacity considerations anddensity evolution (DE) is provided, from which guidelines on the choice of both inner and outer codes are devised. Finite length designs presented in this work confirm the excellent performance of the proposed codes.

Cell-edge users inevitably suffer from strong interference from adjacent cells, and consequently their throughput will decrease. Recent investigations show that the Han-Kobayashi (HK) rate splitting scheme is the best known strategy to mitigate the interference for a two-user interference channel. However, its receiver is too complicated to implement in practical systems. This paper takes the multi-layer rate splitting (MLRS) scheme as an alternative and proposes a new mutual-information based power allocation algorithm for the MLRS scheme. Simulation results for the scenarios of two cell-edge users show that the proposed power allocation algorithm brings two advantages. First, it can reduce the complexity of receivers for the MLRS scheme. Second, it can achieve performance comparable to the simple HK scheme in a two-user fading interference channel and provide about 39% gains in terms of average throughput, compared with a fixed frequency reuse 2 scheme, in a realistic tri-sectored wireless network.

In a multi-cell wireless network, cell-edge user equipments (UEs) inevitably suffer from strong interference from adjacent cells which leads to the UE throughput decrease. Contrary to conventional interference mitigation techniques which treat the interference as noise, we propose to use a rate splitting based interference exploitation technique. A novel distributed power allocation algorithm is adopted to optimize the performance. Simulation results show that the multi-layer rate splitting scheme with the proposed power allocation algorithm can approach the best known achievable rate bound of a two-user interference channel and substantially improve the performance of cell-edge UEs in a tri-sectored network.

In a multi-cell wireless network, an efficient interference mitigation technique is an inevitable part of the current state-of-the-art wireless system. As opposed to conventional interference mitigation techniques which treat the interference as noise, a multi-layer rate splitting scheme can be considered to improve the performance by decoding part of the interference. In this paper, we adapt the multi-layer rate splitting scheme to a Long Term Evolution Advanced framework. Simulation results for two UEs show that the proposed multi-layer rate splitting scheme substantially improves the performance of the cell-edge UE and achieves better fairness between the involved UEs.

Cell-edge users inevitably experience strong interference from adjacent cells, and consequently suffer from performance degradation. Currently, Han-Kobayashi (HK) rate splitting scheme is viewed as the best strategy to cancel the interference and improve the performance. As a special superposition coding scheme, the HK rate splitting scheme has been shown to potentially achieve the channel capacity. However, the way from theory to practice is not straightforward. Current investigations adapting the HK rate splitting scheme to practical applications only deal with the strongest interference and treat the other interferences as noise. In general, more than one strong interferer usually exists, which is targeted in this work. By applying a multi-layer rate splitting scheme for more than one strong interferer, a new ordered detection is proposed and a power allocation algorithm is derived associated with the detection order. Simulation results show that the multi-layer rate splitting scheme mitigates the interference from two different interferers and provides more than twice the gain in terms of average throughput compared with a reference scheme with a fixed frequency reuse factor of 3 in a realistic wireless network.

The multiple-access relay channel with two sources, a single relay, and one destination is considered. Under the assumption of noisy source-relay links causing the relay to be unable to decode without error, we propose a framework for designing one- and two-dimensional quantizers for quantizing the soft information at the relay. These quantizers are mutual-information preserving. Simulation results show a) that mutual-information preserving quantization schemes outperform techniques in which the soft information is forwarded in an analog fashion to the destination, b) that two-dimensional quantization outperforms one-dimensional quantization for source-relay links of different quality, and c) that diversity order of two can be gained in block Rayleigh fading channels by having the relay adaptively select a two-dimensional quantizer from a fixed set of quantizers shared with the destination, depending on the channel state on the source-relay links.

This article presents comprehensive comparisons of interleave division multiple access (IDMA) and direct sequence code division multiple access (DS-CDMA) in terms of performance and complexity assuming iterative multiuser detection. IDMA can be seen as a special case of DS-CDMA with spreading gain of one using very low rate code and user-specific interleavers for user separation. We focus on three suboptimum linear detectors: minimum mean square error (MMSE), rake (or matched filter), and soft-rake detectors from practical concerns. We analytically prove that the three detectors are equivalent for asynchronous users of IDMA on frequency flat channels for complex modulation alphabets. Such equivalence has been shown only for binary phase shift keying (BPSK) in the literature. The equivalence guarantees the MMSE solution for IDMA without computationally expensive matrix inversions or matrix-vector multiplications. This is generally not the case for DS-CDMA since DS-CDMA is sensitive to user asynchronism. We also discuss complexity aspects when the MMSE detector is used where we focus on essential differences in complexity between IDMA and DS-CDMA, instead of discussing particular complexity reduction techniques. Computer simulations are performed in various scenarios and the performance is analyzed by bit error rate simulations as well as by extrinsic information transfer (EXIT) charts. The analysis reveals the advantages of IDMA over DS-CDMA in terms of performance and complexity under practical considerations, particularly in highly user loaded scenarios.

Time Interference Alignment is a flavor of Interference Alignment that increases the network capacity by suitably staggering the transmission delays of the senders. In this work the analysis of the existing literature is generalized and the focus is on the computation of the dof for networks with randomly placed users in a n-dimensional Euclidean space. In the basic case without coordination among the transmitters analytical expressions of the sum dof can be derived. If the transmit delays are coordinated, in 20% of the cases time Interference Alignment yields additional dof with respect to orthogonal access schemes. The potential capacity improvements for satellite networks are also investigated.

The orthogonal multiple-access relay channel with two sources is considered. The goal of this paper is to show the applicability and effectiveness of a previously introduced quantize-and-forward scheme to a more realistic channel and system model, including orthogonal frequency division multiple access and multipath fading channels. Simulation results are provided to demonstrate the gain of quantize-and-forward relayed communication as opposed to the point-to-point links without the relay.

The current coordinated multi-point transmission schemes require the transmission coordination to avoid the interference. This paper considers the benefit from the rate splitting method, which has been shown to improve the capacity of the interference channel by decoding part of the interference. In this paper, a coordinated multi-point transmission framework based on the cooperative rate splitting scheme is proposed in the Long Term Evolution Advanced system, where a user equipment pair selection algorithm and a cooperative proportional fair scheduling algorithm are newly developed. The simulation results have shown that the coordinated multi-point transmission with cooperative rate splitting can substantially improve the performance of the cell-edge user equipment without or with a slight performance loss at the average rate in a realistic network.

The source coding rate allocation problem for the orthogonal multiple-access relay channel with M users and compress-and-forward at the relay is addressed. In case of Gaussian codebooks at the sources and Gaussian channels, we show that the sum-rate-optimal assignment of source coding rate at the relay is given by waterfilling. For general modulation alphabets at the sources and finite-alphabet discrete memoryless channels, the source coding rate allocation problem is formulated using the information bottleneck method, based on which we appropriately modify a standard cutting-plane algorithm to numerically compute an optimal source coding rate vector at the relay.

We propose a novel transmission scheme for the broadcast phase of two-way relaying systems. The proposed scheme employs network coding at the relay and is able to transmit with asymmetric data rates to the receivers according to their individual link qualities. The idea is that the weaker link receiver exploits a priori bit information in each transmit symbol, so that it only needs to decode on a subset of the transmit symbol constellation. Subject to the same bit error rate constraint, the weaker link receiver can decode at lower signal-to-noise ratio compared to the stronger link. The signal labeling used for mapping bits to symbols at the relay is shown to be crucial for the performance at the receivers, and we provide the criterion and method for finding the optimized labeling schemes. Simulations show that the proposed transmission scheme can be applied to practical scenarios with asymmetric channel qualities, and the optimized labeling greatly outperforms conventional ones at both receivers.

This paper studies the computation of postprocessing signal-to-interference plus noise ratio (SINR) for maximum likelihood detector (MLD) in multiple-input and multiple output (MIMO)-orthogonal frequency division multiplexing (OFDM) spatial multiplexing systems. We derive an effective post-MLD SINR for each spatial stream, which is computed as post minimum mean-squared error (MMSE) SINR plus gain factor, where the gain factor is adaptively computed based on the instantaneous channel and modulation format of interfering streams. The post-MLD SINR is then applied to modulation and coding scheme (MCS) selection in adaptive modulation and coding. Simulation results show that the MCS selection using proposed post-MLD SINR can achieve throughput performance close to that of the optimum approach, and considerable gain can be achieved over linear-MMSE receiver.

A method called open loop cyclic delay diversity (CDD) precoding has recently become part of the standard for 3GPP Long-Term Evolution (LTE) cellular communications systems. CDD had previously been proposed as single stream diversity method for OFDM systems. The proposal in LTE is an extension to spatial multiplexing using so called large delay CDD. The purpose of this paper is twofold: First, we give two different interpretations of the CDD precoding scheme in LTE as beam switching and transformation of spatial diversity to frequency diversity. We then discuss the impact of the delay parameter in CDD precoding on the achievable rates and BER performance. It turns out that while the choice of large delay causes a capacity degradation in single stream CDD, the achievable rates in CDD based spatial multiplexing are robust to variations of the delay parameter. This is even true in correlated scenarios and when only a resource block of a few subcarriers is allocated to a specific user. However, in case of separate encoding of the spatial streams, large delay CDD precoding shows a BER advantage over small delay CDD precoding since the spatial diversity is transformed into frequency diversity among adjacent subcarriers and, hence, is easily picked up by the decoder. Finally, we give a performance comparison of open loop CDD based precoding and closed loop precoding in LTE. It is shown that for 2 transmit antenna systems, only little gain is achieved by the additional effort of closed loop precoding. Closed loop precoding shows substantial benefits over CDD based precoding only for a higher number of transmit antennas.

This paper presents comprehensive comparisons of interleave division multiple access (IDMA) and direct sequence code division multiple access (DS-CDMA) in terms of performance and complexity using iterative multiuser detection technique, where we restrict ourself to three suboptimum linear detectors: minimum mean square error (MMSE), rake (or matched filter), and soft-rake detectors from practical concerns. We first analytically compare these detectors, which are found to be equivalent for IDMA with asynchronous users on flat channels, whereas this does not hold for DS-CDMA, which is sensitive to user asynchronism. This implies that, on flat channels, simple detector suffices to get the MMSE output for IDMA while DS-CDMA requires more complexity such as computing matrix inversions. We also discuss several aspects of complexities for IDMA and DSCDMA when the MMSE detector is used. Computer simulations are performed in various scenarios and the performance is analyzed by bit error rate as well as by extrinsic information transfer chart. The analysis reveals some advantages of IDMA over DS-CDMA, particularly in highly user loaded scenarios.

A network with two sources, one relay, and one destination is considered. Under the assumption of noisy source- relay links causing the relay to be unable to decode without error, we propose a quantizer design framework where the quantizer jointly compresses the soft information available for both sources at the relay. The quantizer design is based on the information bottleneck method using the notion of relevant information as an optimization criterion.

We consider a wireless relay network where two sources transmit independent information on mutually orthogonal channels to a common destination with the help of one relay. Based on the expression for the achievable rate in such a network for compress-and-forward relaying without Wyner-Ziv coding, we design mutual-information preserving quantizers for compression at the relay. In the proposed relaying scheme, both the relay and the destination share a fixed set of quantizers, among which the relay selects a suitable one depending on the channel quality on the source-relay links for compression of its received values. Simulations performed in Rayleigh block fading channels reveal that full diversity order of two can be achieved using that scheme. We also comment on the size of the quantizer set and the associated signaling overhead.

In this paper, we consider the broadcast (BRC) phase of two-way decode-and-forward (DF) relaying systems. The channel in that phase is called the bidirectional broadcast channel. Its achievable rates are calculated when the self-interference aided channel estimation scheme is applied. We consider a block- fading channel model in the BRC phase and exploit the self- interference that is inherent in two-way relaying techniques to get an initial estimate of the channel at the receiving terminals. This initial channel estimate is utilized to decode the data in the first several time slots of each coherence interval. Data- aided approaches are then employed to improve the channel estimates in the following time slots of each coherence interval. The spectral efficiency improvement for systems employing this self-interference aided channel estimation scheme is quantified by comparing its achievable rates to that of the traditional pilot- aided channel estimation scheme.

Recently, the capacity region of the Gaussian broadcast channel has been characterized. For a given transmit power constraint, those points on the boundary of the capacity region can be regarded as the set of optimal operational points. The present work addresses the problem of selecting the point within this set that satisfies given constraints on the ratios between rates achieved by the different users in the network. This problem is usually known as rate balancing. To this end, the optimum iterative approach for general MIMO channels is revisited and adapted to an OFDM transmission scheme. Specifically, an algorithm is proposed that exploits the structure of the OFDM channel and whose convergence speed is essentially insensitive to the number of subcarriers. This is in contrast to a straightforward extension of the general MIMO algorithm to an OFDM scheme. Still, relatively high complexity and the need of a time-sharing policy to reach certain rates are at least two obstacles for a practical implementation of the optimum solution. Based on a novel decomposition technique for broadcast channels a suboptimum non-iterative algorithm is introduced that does not require time-sharing and very closely approaches the optimum solution.

A random non-line-of-sight environment with stationary transmitter and receiver is considered. In such an environment movement of a scatterer will lead to perturbations of the otherwise static channel with a resulting Doppler spectrum. This is quite a general situation in outdoor environments with moving traffic or indoor situations with moving people. Here we study the latter situation in detail with experimental results from a large office environment. A general theory of Doppler spectra is developed. The impact of a scatterer depends on the angular distribution of scattered energy, and uniform as well as sharply peaked distributions are considered in the theory. The Doppler spectra are in all cases sharply peaked at zero frequency due to forward scattering, but the actually measured distribution depends on the degree and type of activity in the environment, as well as the spectrum estimation accuracy.

In this paper, we propose a novel channel estimation scheme for the broadcast phase of the newly invented two- way relaying technique. Instead of using pilot sequences, we exploit theself-interference, which contains the data known at the receivers, to get a first estimate of the channel. Then a decision- directed iterative estimation process is started to improve the accuracy of the channel estimates. We consider a block fading channel model. The simulation results show that the proposed scheme has similar performance as pilot-aided channel estimation schemes in our simulation environment. Since pilot sequences are no longer needed in our proposed scheme, higher spectrum efficiency is achieved without performance loss.

Future wireless systems are expected to support high data rates of 1 Gbit/s or more in a variety of scenarios. A key technology in order to achieve the required high spectral efficiency is the application of multiple input multiple output (MIMO) techniques, which exploit spatial diversity, array gain or spatial multiplexing gain. Another source of diversity - inherent to wireless systems- is that of the multiuser diversity. Multiuser (MU) MIMO algorithms combine both MIMO gains with multiuser diversity benefits. Although MU MIMO techniques have been extensively studied and were shown to provide considerable average cell throughput gains, they often prove inadequate to cope with intercell interference and can only offer poor cell edge performance. Network coordination (multisite MIMO) can be applied in this case, which can achieve significant improvements for the users including those at the cell edge, based on coordinated transmission and reception by multiple base stations. In this paper we present an overview of the most promising MIMO technologies and discuss their relative merits and requirements.

Finding the matrix with the maximum singular value amongst a set of matrices is a common problem occurring in transmit signal processing algorithms for multiuser multiple-input multiple-output (MIMO) systems. However, computing the principal singular value of a matrix is a rather numerically complex task. Furthermore, in many practical scenarios, the number of users is large and for each user this task has to be conducted. In this paper we therefore propose a novel user preselection method which reduces the computational complexity at no performance loss. This is achieved by deselecting some users based on a simple criterion and thus avoiding explicit computations of the singular values of those users. This criterion is based on easily computable bounds for the principal singular values. Finally, a statistical analysis is provided and the application to the Successive Encoding Successive Allocation Method (SESAM) is shown.

Utilizing multiple interleavers recently attracted increasing attention in many research areas. Interleaver generation should be simple in order to avoid huge memory requirements for storing interleaving patterns. We propose to derive multiple interleavers by cyclically shifting and self-interleaving a common mother interleaver in a few steps. Our focus is on the good user separation in interleave division multiple access systems. The proposed method may also find other application areas such as multi-dimensional concatenated codes.

In this paper, we present a differential codebook precoding technique for MIMO spatial multiplexing systems. The proposed scheme updates the precoding matrix by multiplying the codebook matrix specified by the receiver to the previous precoding matrix. The codebook matrices are designed to include quasi-diagonal matrices for finer quantization of the channel. We analyze the capacity of the differential precoding scheme as well as existing precoding schemes by taking into account various practical aspects such as precoding update interval, feedback delay, and feedback error. Simulation results show that in a MIMO system with four transmit and two receive antennas, the proposed precoding scheme requires smaller codebook size by about 1 - 2 bits than the one-shot precoding scheme with Grassmannian codebook to achieve the same capacity level when the channel variation is relatively small. Finally, we show that the proposed precoding scheme provides similar throughput performance gain in MIMO-OFDM systems in frequency selective fading channels.

The downlink of amultiusermultiple-inputmultiple-outputsystem is considered. Compared to nonlinear precoding strategies which are designed to achieve the channel capacity in case of perfectchannelstateinformation(CSI) at the transmitter, we focus on systems which are restricted to use only linear precoders due to complexity constraints and where only partial CSI is available at the transmitter due to limited feedback. Here, we compare two different feedback approaches: in the first, users feed back the best entry of a precoder codebook, and in the second, users provide quantized channel information based on a channel codebook. The presented transmitters exploit the available feedback information to select the users for transmission (scheduling) and to compute the corresponding precoder. Sum rate investigations show that channel codebook based feedback should be preferred in systems with a very low feedback rate.

Conventional medium access control protocols are designed to avoid simultaneous transmissions, based on a simple collision model in the underlying physical layer. Recently, strong physical layer capabilities enabled by multiuser detection techniques have been studied in connection with simple medium access control protocols. We think that neither of these extreme approaches is optimum, in particular for general scenarios where network nodes with different signal processing capabilities coexist. Instead of dealing with interferences in either of the two layers alone, both medium access control and physical layer functionalities should cooperate and complement each other. We discuss several key aspects for designing such a new type of protocol, especially with an emphasis on iterative multiuser detection, which can provide a good tradeoff between performance and complexity. We propose a new protocol which satisfies these key aspects. We analyze its throughput bound and also perform numerical simulations. The results evidence excellent throughput improvements.

Interleave division multiple access (IDMA) recently attracted many research activities because of its excellent performance despite its reasonable low complexity. The low complexity is usually realized by the multiuser detector that applies an approximation similar to the rake receiver for CDMA systems. So far, this type of detector has been most frequently considered in IDMA literature. In this paper we investigate the performance of IDMA based on linearminimummeansquareerror(MMSE) detection. The MMSE detector is more complex than the rake-like approximation. At the price of the complexity, however, it is shown that the MMSE detector brings several advantages over the rake-like approach such as the superior performance on channels with spectrally poor characteristics, effective iterative processing for lower SNR values, faster convergence and therefore shorter decoding delays, and better performance for short block length. We also confirm that the complexity can be drastically reduced by the low rank approximation of the MMSE filter by its multistage representation without compromising on the performance.

We consider an innovative downlink multiuser MIMO scheme. Several users compete for the available resources in time, frequency and space. The proposed scheme exploits multiuser diversity and uses interference cancellation at the transmitter. We evaluate it for indoor, hot spot and multihop scenarios. Significant gains in terms of sum capacity can be achieved even under line of sight conditions. The theoretical limit can be approximately achieved in most scenarios. A nice feature of the proposed scheme is that it inherently provides some fairness regarding rate distribution among users even though fairness is not explicitly taken into account by the scheduler.

Concatenated zigzag codes with iterative decoding show excellent performance close to the performance of turbo codes while having significantly lower decoding complexity. A strong code is built by concatenation of several weak high rate codes which encode differently interleaved versions of the data sequence. The need for multiple interleavers implies a new problem in interleaver design: Multiple interleavers have to be constructed which allow good performance in iterative decoding and are mutually random. Furthermore, for practical reasons, they should be constructed either from a simple equation or by simple permutations of a common mother interleaver in order to minimize the required memory for storage of the permutation pattern. We propose two simple methods for interleaver construction which meet those requirements. Our first proposal is to obtain multiple interleavers by cyclic shifts and self-interleaving from a common mother interleaver. The second proposal uses modified read out processes in intermediate steps during construction of the UMTS turbo code internal interleaver. Particularly the proposed cyclic shifted interleavers are shown to provide superior performance with zigzag codes compared to other interleaving schemes such as congruential interleavers.

We consider a low mobility cellular relaying system downlink where two mobile users are served by two neighboring decode-and-forward relays concurrently using the same frequency channel. We propose two cooperative relaying transmission schemes where each relay can choose proper precoding vectors based on its local channel knowledge to transmit data in the second hop. Each user can receive its own data without interference, which simplifies the user receiver design. We show that the diversity of each user's received data signal can be improved by receiving data from multiple relays. In addition, higher array gain can be achieved by the first scheme at the cost of higher synchronization accuracy requirements. Furthermore, we show that the two transmission schemes achieve higher transmission rate than serving different users in separate channels.

The exploitation of multiuser diversity and the application of multiple antennas at transmitter and receiver are considered to be key technologies for future highly bandwidth-efficient wireless systems. We combine both ideas in a downlink multicarrier transmission scheme where multiple users compete for the available resources in time, frequency and space. The instantaneous channel impulse responses for all users are assumed to be perfectly known at the transmitter. Our proposed algorithm allocates each spatial dimension on a subcarrier to the user which has the highest channel tap gain on the respective spatial dimension. The scheduling strategy is optimized for sum capacity maximization. In this paper, we restrict ourselves to a more illustrative description of the idea rather then providing mathematical details. We demonstrate the potential of the proposed scheme by capacity results for measured real world channels in a large office environment. Finally, video streaming is used as a potential application with high data rate and low latency demands. It is shown that the proposed method has the potential to exploit multiuser diversity while still providing stable video streams even though QoS constraints are not explicitly taken into account by the scheduler.

Placing relays around the base station (BS) to assist wireless communication is an effective way of extending coverage in cellular systems. This paper provides a quantitative analysis of coverage extension by using decode-and-forward (DF) relays. To describe the relation between the number of relays and the coverage range extension, we introduce the concept of coverage angle and coverage range. We provide analytical upper and lower bounds for the coverage range in a cellular system for any given coverage angle. By means of simulations, we show the tightness of our analytical approach.

We consider a multiuser MIMO-OFDMA scheme which exploits multiuser diversity in all dimensions: time, frequency and space. The main contribution of this paper is the evaluation and explanation of multiuser MIMO in a real world scenario, i.e. a large office room, based on measured channels. We report interesting results of a measurement campaign which suggest that significant MIMO gains are possible in an indoor environment even under strong line-of-sight condition as long as the distance of the users from the base station is larger than a reverberation distance which only depends on room surface and material. We show results on the achievable multiuser MIMO data rates for the given scenario compare to theoretical limits and discuss the results in the light of the insights gained from the measurement campaign. We also introduce restrictions on the rate distribution between users, i.e. QoS constraints. It is shown that the theoretical limits can be approximately achieved provided that the users which compete for the spatial resources are carefully chosen.

We propose a simple strategy to generate multiple interleavers. As suggested in literature, using user-distinct interleavers is an effective means to separate multiple users, if applied in addition to user-specific spreading codes in code division multiple access (CDMA). In this paper we particularly focus on interleave division multiple access (IDMA), which has a close relation to CDMA, but users are separated only by user- distinct interleavers. Hence, multiple interleavers are essential system components for IDMA. Despite its importance, usually interleavers for such systems are randomly chosen and there are only few papers on the generation of multiple interleavers. We show that the conventional multiple interleavers proposed for CDMA are not sufficient for the user separation in IDMA. Moreover, in order to minimize memory requirements and signaling overheads to store and exchange interleavers, a simple interleaver construction rule is desirable in practical systems. Therefore, we propose to derive multiple interleavers from a single interleaver, common for all users, with only a few user- distinct cyclic shifts. Although this simple design is empirical and no optimality is claimed, simulation results show sufficiently good performance. We also proposed a simple procedure to exchange the information of interleavers.

In the work at hand a general procedure to analyze the impact of channel estimation errors on the performance of decomposition techniques for multiuser MIMO channels is presented. In particular, this procedure is applied to a decomposition technique called cooperative zero-forcing with successive encoding and successive allocation method (CZF-SESAM). Based on the resulting analytical expressions the transmitter is able to adjust bit and power loading so that in spite of estimation errors transmission quality requirements can still be met

In the work at hand relevant issues concerning implementation of optimal and nearly optimal transmission approaches for the MIMO OFDM broadcast channel are discussed. In particular, algorithms proposed to compute optimum covariance matrices are efficiently extended to the multicarrier setting. Furthermore, a method is proposed to transform the resulting vector channels into a set of scalar subchannels over which information can be independently transmitted without incurring any capacity loss. This effective diagonalization of the broadcast channel is most convenient for practical purposes as, so far, existing techniques for coding with side information have exclusively been conceived for scalar subchannels. Finally, we discuss the practical advantages of a suboptimum technique such as the cooperative zero-forcing with successive encoding and successive allocation method (CZF-SESAM). This technique exhibits a nearly optimum performance and significantly simplifies both computation of transmit covariance matrices and downlink signaling.

In a future 4G or WLAN wideband application we can imagine multiple users in a large office environment consisting of a single room with partitions. Up to now, indoor radio channel measurement and modelling has mainly concentrated on scenarios with several office rooms and corridors. We present here measurements at 5.8 GHz for 100 MHz bandwidth and a novel modelling approach for the wideband radio channel in a large office room environment. An acoustic like reverberation theory is proposed that allows to specify a tapped delay line model just from the room dimensions and an average absorption coefficient of the delimiting walls. The proposed model agrees amazingly well with the measurements, showing that the diffuse part is uniformly spread over the room with a constant energy level and a constant temporal decay slope. Furthermore, we analyze fading statistics and capacities calculated from the measurements. The proposed model can likely also be applied to indoor hot spot scenarios

MIMO transmission systems exploit scattering in the radio channel to achieve high capacity for a given SNR. A high pathloss is generally expected for channels with rich scattering, suggesting that a high SNR and rich multipath are competing goals. The current work investigates this issue based on measurements obtained with a 16 times 32 MIMO channel sounder for the 5.8 GHz band. The measurements were carried out in various indoor scenarios where different sizes of both the transmitter and receiver antenna arrays are investigated, 1 times 1 up to 16 times 32. A moderate correlation between pathloss and median capacity was found. However, the higher richness can not compensate for the decrease in capacity due to increased pathloss. Assuming a fixed Tx power, the median capacity was found to depend approximately linearly on the pathloss. The slope of the linear relation depends on the effective rank of the channel, which in turn was found to be approximately linearly dependent on the number of antennas, assuming a symmetric MIMO channel

The application of multi-symbol detectors for FEC coded DPSK allows to achieve significant turbo gains in a non-coherent iterative detection scheme. However, the complexity is increased compared to standard differential DPSK detection based on two received symbols. For example, in a trellis based detector, the complexity grows exponentially with the number of symbols which are taken into consideration. In this paper, we propose to apply a list-sequential (LISS) algorithm with soft augmentation in the turbo scheme in order to overcome the complexity problem. We unveil problems which appear particularly in non-coherent DPSK detection and show that the LISS detector can achieve the same performance as an optimum but prohibitively complex APP detector at reasonable SNR

Interleave Division Multiple Access (IDMA) is a multiple access scheme similar to CDMA and it relies on an iterative ("turbo") multiuser detection and decoding technique. IDMA applies a very low rate code which is typically realized by a convolutional code followed by a simple repetition code. We propose to limit the decoding of the convolutional code at the receiver aiming at complexity reduction. Although the transmitted bits cannot be recovered without decoding the convolutional code, decoding only the repetition code already gives some improvements of soft information to the next ("turbo") iteration. We show that such a simple scheme can be of advantage of IDMA against CDMA to achieve significant complexity reduction with graceful performance degradation. We also provide analyses to answer when it makes sense to apply the simple strategy.

We propose a new scheme for differential modulation in orthogonal frequency-division multiplexing (OFDM) with cyclic delay diversity. Delay diversity is done in a cyclic manner in order not to exceed the guard interval. However, the increased frequency selectivity, and consequently, reduced coherence bandwidth, causes problems for noncoherent detection of differential modulation in frequency direction. Our proposal is able to cope with the increased frequency selectivity,and enables picking up the spatial diversity in frequency-selective channels with unknown delay spread in combination with standard differential modulation techniques. The new scheme is less complex than differential unitary space-time modulation. The overhead due to reference symbols is minimized and the detection delay is reduced, compared with differential modulation in time direction.

Assuming perfect channel state information at the transmitter of a Gaussian broadcast channel, strategies are investigated on how to assign subchannels in frequency and space domain to each receiver aiming at a maximization of the sum rate transmitted over the channel. For the general sum capacity maximizing solution, which has recently been found, a method is proposed that transforms each of the resulting vector channels into a set of scalar channels. This makes possible to achieve capacity by simply using scalar coding and detection techniques. The high complexity involved in the computation of this optimum solution motivates the introduction of a novel suboptimum zero-forcing allocation strategy that directly results in a set of virtually decoupled scalar channels. Simulation results show that this technique tightly approaches the performance of the optimum solution, i.e., complexity reduction comes at almost no cost in terms of sum capacity. As the optimum solution, the zero-forcing allocation strategy applies to any number of transmit antennas, receive antennas and users

We consider cyclic delay diversity in OFDMA. Cyclic delay diversity is an elegant way to obtain spatial diversity in an FEC coded OFDM system without exceeding the guard interval. We first address the problem of choosing the cyclic delays and propose a new robust design rule which enables to pick up the full spatial and frequency diversity which is inherent in a frequency-selective MIMO channel. Our choice of cyclic delays has consequences for the interleaving and multiple access scheme since the spatial diversity appears to be transformed into frequency diversity between neighbouring subcarriers. Therefore, a system with a conventional block frequency interleaver will fail to exploit the spatial diversity. We propose an interleaving and multiple access strategy which guarantees that all users obtain the maximum possible diversity advantage using FEC codes with a limited constraint length. Furthermore, we provide a performance comparison to transmit diversity from orthogonal designs

We consider bit-interleaved coded modulation with iterative (turbo) detection and non-unique mappings of bits to QAM/PSK symbols which allow better performance in the error floor region compared to unique mappings. Ambiguities are resolved in the iterative detection process. Furthermore, we propose partially unique mappings which together with a carefully designed channel coding scheme enable a good trade-off between performance without and with turbo iterations. This makes turbo iterations optional but not mandatory. Moreover, the detection complexity and the peak to average power ratio can be reduced with non-unique mappings compared to unique-mappings with turbo detection.

Nonlinear minimum mean square error Tomlinson-Harashima precoding considered in this paper is an attractive solution for a scenario where a transmitter serves spatially separated receivers and no cooperation among them is possible. Unfortunately, the large performance gain against linear precoding comes along with significantly higher complexity than linear filters in the case of a large number of receivers. We show that superior performance of the nonlinear minimum mean square error Tomlinson-Harashima precoding can be obtained with complexity equivalent to linear precoding. Our proposed algorithm reduces the complexity by a factor of N/sub R/ which is the number of receivers.

We consider differential matrix modulation with forward error control coding and iterative (turbo) detection. Optimized mappings of bits to matrices which allow turbo gains are given for different unitary differential matrix modulation schemes. Particularly, we propose a differential matrix modulation where the number of possible transmit matrices is smaller than the number of possible bit sequences. The ambiguities are resolved in an iterative ("turbo") detector. The proposed scheme enables improved performance compared to existing proposals particularly in the error floor region. Since the number of possible transmit matrices is reduced compared to state of the art schemes, the detection complexity is reduced. Furthermore, the peak to average power ratio is reduced which relaxes the requirements for linearity of amplifiers. The backoff of amplifiers can be reduced which results in lower battery consumption and costs

This paper provides comprehensive comparison of the iterative multiuser detection techniques for code devision multiple access (CDMA) and interleave division multiple access (IDMA). We investigate the performance in various scenarios such as user-asynchronism, multipath channels, near-far problem, and overloaded scenarios. We develop our system model which is general enough to take into account the above mentioned scenarios as well as being capable of incorporating aspects relevant to these access schemes. We provide formal descriptions of both schemes using our system model that illuminates the similarities and differences of the two different schemes. Computer simulations are performed in a variety of scenarios. It is observed that IDMA performs better than or as good as CDMA despite its simplicity.

In the work at hand, decomposition approaches are investigated for the downlink of a multiuser MIMO setting. The focus is on approaches that use successive encoding to eliminate part of the interference between users or information streams. We start reviewing the well known zero-forcing with successive encoding (ZF-SE) approach and generalize the basic idea behind this technique to arrive at a block ZF-SE that can be applied to the case of users with multiple antennas exploiting their cooperation capability. Aiming at a maximization of the achievable sum-rate we elaborate on the ZF-SE and block ZF-SE approaches to come up with the ZF-SE with successive allocation method (ZF-SESAM) and the cooperative ZF-SE with successive allocation method (CZF-SESAM), respectively. This two approaches proceed successively selecting at each step a user to which the next spatial dimension is assigned. Specifically, we propose a largest gain criterion for this selection and provide some rationale for that. Using an OFDM transmission scheme, Tomlinson-Harashima precoding and a common bit loading algorithm we compare the sum-rate achieved by the different approaches. Finally, some simulation results show that in a large MIMO-OFDM system with a moderate number of users even the weakest user can profit from the increase in sum-rate if compared to a maximally fair system, where the same number of dimensions is assigned to every user.

A cyclic delay diversity in OFDM with noncoherent detection is considered. Delay diversity is done in a cyclic manner in order not to exceed the guard interval. We give a robust design criterion for the cyclic delay which enables to pick up the full spatial diversity in frequency selective channels with unknown delay spread. However, the effective channel coefficients of neighbouring subcarriers appear to be uncorrelated which makes differential modulation in frequency direction impossible. Therefore, we propose a multistream scheme which allows to noncoherently exploit the full spatial diversity in combination with standard differential modulation techniques. The new scheme is less complex than differential unitary space-time modulation. The overhead due to reference symbols is minimized and the detection delay is reduced compared to differential modulation in time direction.

Orthogonal space-time block codes (STBC) constitute a simple way of exploiting transmit diversity. If no channel knowledge is available at the transmitter the use of diversity can increase performance significantly. However, if some partial channel state information (CSI) is available, such as knowledge of the transmit correlation matrix, adapting transmission to this knowledge provides additional performance gains. In such case, adaptivity can be introduced by using a unitary eigenbeamformer with beams pointing along the directions of the eigenvectors of the transmit correlation matrix and applying a convenient power loading along the resulting beams. While for Rayleigh-fading channel eigenbeamforming has been shown to be optimum in terms of ergodic capacity, so far, no closed solution for the optimum power loading has been found. In the work at hand, orthogonal STBC are combined with eigenbeamforming in an orthogonal frequency-division multiplexing (OFDM) context and an optimum power loading is found, where optimality refers to an upperbound of pairwise error probability (PEP). The resulting signaling scheme can be viewed as an extension of STBC to OFDM with partial channel knowledge. The solution represents a very interesting trade-off between transmit diversity and antenna gain.

We propose an extension of differential unitary space-time matrix modulation by an additional differential amplitude modulation for bandwidth-efficient transmission with noncoherent detection in a wireless system with multiple transmit antennas. The input bits are subdivided into two groups. The first group chooses a unitary matrix, the second group determines the amplitude of the transmit matrix. Compared to differential unitary space-time modulation, the proposed scheme has lower detection complexity and provides superior performance for bandwidth-efficient transmission, particularly in time-varying channels.

We provide a capacity analysis of different MIMO techniques such as spatial multiplexing and space-time block coding. In contrast to most capacity evaluations which consider only Gaussian transmit symbols, we take into account restrictions on the transmit symbol alphabet and analyze real world signal constellations. Moreover, we include different detectors such as maximum likelihood, MMSE, zero-forcing, BLAST in the capacity evaluation. Furthermore, we consider not only spatially uncorrelated full rank channels but also channel degradations such as spatial correlation and keyhole effects. The results show that in many practical relevant cases, simple space-time block codes are a robust solution which achieves similar, sometimes even better capacities than spatial multiplexing even though they do not exploit all available MIMO dimensions.

Differential transmit diversity which can be detected noncoherently is an attractive alternative to coherent space-time block codes since channel estimation is more difficult in multi-antenna systems than in single antenna systems. We focus on differential space-time block codes from orthogonal designs which derive the transmit symbols from an M-PSK constellation in order to fulfill the requirement that the transmit vectors have unit length. Since PSK is only advantageous for M /spl les/ 8, we propose to extend the transmit symbol constellation for bandwidth-efficient transmission such that multiple levels are allowed for the length of the transmit vector. Furthermore, we derive a simple soft-output detector which does not require channel estimation. The new scheme outperforms the existing unit-length approach at high bandwidth-efficiency, particularly in time-varying channels. Furthermore, it is more flexible in terms of data rate adaptation and has lower detection complexity.

We investigate bit-interleaved coded modulation with iterative decoding (BICM-ID) for bandwidth efficient transmission, where the bit error rate is reduced through iterations between a multilevel demapper and a simple channel decoder. In order to achieve a significant turbo-gain, the assignment strategy of the binary indices to signal points is crucial. We address the problem of finding the most suitable index assignments to arbitrary, high order signal constellations. A new method based on the binary switching algorithm is proposed that finds optimized mappings outperforming previously known ones.

We consider turbo detection for coherent and non-coherent orthogonal transmit diversity. The turbo iterations are done between a convolutional code and the diversity combiner. We show that due to the orthogonal structure of the space-time block codes, extrinsic information which enables turbo gains can only be obtained for special symbol mappings. Such optimized symbol mappings arc given for 8-PSK, 16-PSK and 16-QAM. Furthermore, we present a simple non-coherent soft-output detector for differential space-time block codes. Extrinsic information transfer ( EXIT) charts are used as a powerful tool for analysis and performance prediction of the turbo detector. We show that a turbo detector benefits from spatial diversity in terms of earlier convergence. For quasistatic fading, we introduce the x%-EXIT chart.

We consider turbo equalization of space-time-coded transmission over frequency-selective fading multiple-input-multiple-output (MIMO) channels. A MIMO finite-impulse-response prefilter is proposed and shown to reduce the turbo equalizer complexity significantly at a small performance loss. Advantages of the proposed scheme are that we do not alter the equalization algorithm or require the channel to be minimum phase. The prefiltered turbo equalizer is an attractive receiver structure for broadband wireless transmission using spectrally-efficient high-order modulation schemes as in EDGE.

We consider FEC coded transmit diversity with space-time block codes. We show that all space-time block codes are equivalent in terms of SNR per bit and give a new view on space-time block codes as a transformation of the fading MIMO channel towards a Gaussian SISO channel. Using a simple equivalent channel model we provide analytical results on the BER of uncoded and FEC coded transmit diversity. Finally, we roughly discuss using multiple antennas for transmit diversity or beamforming.

An architecture that theoretically achieves channel capacity on multi-input multi-output channels was proposed by Foschini (1996) as BLAST. We show that in practical systems BLAST performance is limited by error propagation. The theoretically possible increase in diversity level during successive detection steps cannot be achieved. We evaluate the possibilities to improve BLAST performance. We first analyze an MMSE solution compared to zero forcing. Furthermore, we show the benefits of soft over hard interference cancellation and propose an iterative turbo detection algorithm. Finally, we apply transmit diversity with space-time block codes to BLAST. This improves the overall diversity level and reduces the number of required receive antennas.

For high data rate transmission over wireless fading channels space-time block codes provide the maximal possible diversity advantage for multiple transmit antenna systems with a very simple decoding algorithm. To achieve also a significant coding gain space-time block codes have to be concatenated with an outer code. We derive a symbol by symbol MAP decoding rule for space-time block codes. We describe two schemes for "turbo"-TCM which have been shown to achieve decoding results close to the Shannon limit in AWGN channels. These turbo-TCM schemes are concatenated to space-time block codes. The MAP decoding algorithm is described. We also discuss a feedback to the space-time block decoder. Significant coding gain in addition to the diversity advantage is shown to be achieved while the decoding complexity is mainly determined by the trellis complexity of the outer code.

This paper addresses the problem of equalization space-time codes with transmit diversity. We derive a symbol-by-symbol MAP equalizer/decoder for space-time coded signals over frequency selective channels. We describe a turbo equalization/decoding scheme where the results of the decoding are fed back to the equalizer. Simulation results show an improved equalizer performance due to the feedback.