Feature attribution has gained prominence as a tool for explaining model decisions, yet evaluating explanation quality remains challenging due to the absence of ground-truth explanations. To circumvent this, explanation-guided input manipulation has emerged as an indirect evaluation strategy, measuring explanation effectiveness through the impact of input modifications on model outcomes during inference. Despite the widespread use, a major concern with inference-based schemes is the distribution shift caused by such manipulations, which undermines the reliability of their assessments. The retraining-based scheme ROAR overcomes this issue by adapting the model to the altered data distribution. However, its evaluation results often contradict the theoretical foundations of widely accepted explainers. This work investigates this misalignment between empirical observations and theoretical expectations. In particular, we identify the Sign issue as a key factor responsible for residual information that ultimately distorts retraining-based evaluation. Based on the analysis, we show that a straightforward reframing of the evaluation process can effectively resolve the identified issue. Building on the existing framework, we further propose novel variants that jointly structure a comprehensive perspective on explanation evaluation. These variants largely improve evaluation efficiency over the standard retraining protocol, thereby enhancing practical applicability for explainer selection and benchmarking. Following our proposed schemes, empirical results across various data scales provide deeper insights into the performance of carefully selected explainers, revealing open challenges and future directions in explainability research.

Beyond its widespread application in signal and image processing, \emph{compressed sensing} principles have been greatly applied to secure information transmission (often termed 'compressive security'). In this scenario, the measurement matrix $Q$ acts as a one time pad encryption key (in complex number domain) which can achieve perfect information-theoretic security together with other benefits such as reduced complexity and energy efficiency particularly useful in IoT. However, unless the matrix is changed for every message it is vulnerable towards known plain text attacks: only $n$ observations suffices to recover a key $Q$ with $n$ columns. In this paper, we invent and analyze a new method (termed 'Bilinear Compressive Security (BCS)') addressing these shortcomings: In addition to the linear encoding of the message $x$ with a matrix $Q$, the sender convolves the resulting vector with a randomly generated filter $h$. Assuming that $h$ and $x$ are sparse, the receiver can then recover $x$ without knowledge of $h$ from $y=h*Qx$ through blind deconvolution. We study a rather idealized known plaintext attack for recovering $Q$ from repeated observations of $y$'s for different, known $x_k$, with varying and unknown $h$ ,giving Eve a number of advantages not present in practice. Our main result for BCS states that under a weak symmetry condition on the filter $h$, recovering $Q$ will require extensive sampling from transmissions of $Ω\left(\max\left(n,(n/s)^2\right)\right)$ messages $x_k$ if they are $s$-sparse. Remarkably, with $s=1$ it is impossible to recover the key. In this way, the scheme is much safer than standard compressed sensing even though our assumptions are much in favor towards a potential attacker.

Hallucination remains a critical failure mode of large language models (LLMs), undermining their trustworthiness in real-world applications.In this work, we focus on confabulation, a foundational aspect of hallucination where the model fabricates facts about unknown entities.We introduce a targeted dataset designed to isolate and analyze this behavior across diverse prompt types.Using this dataset, and building on recent progress in interpreting LLM internals, we extract latent directions associated with confabulation using sparse projections.A simple vector-based steering method demonstrates that these directions can modulate model behavior with minimal disruption, shedding light on the inner representations that drive factual and non-factual output.Our findings contribute to a deeper mechanistic understanding of LLMs and pave the way toward more trustworthy and controllable generation.We release the code and dataset at https://github.com/Thibaud-Ardoin/where- confabulation-lives

Determining the optimal block size is crucial for achieving high throughput in blockchain systems. Many studies have focused on tuning various components, such as databases, network bandwidth, and consensus mechanisms. However, the impact of block size on system performance remains a topic of debate, often resulting in divergent views and even leading to new forks in blockchain networks. This research proposes a mathematical model to maximize performance by determining the ideal block size for Hyperledger Fabric, a prominent consortium blockchain. By leveraging machine learning and solving the model with a genetic algorithm, the proposed approach assesses how factors such as block size, transaction size, and network capacity influence the block processing time. The integration of an optimization solver enables precise adjustments to block size configuration before deployment, ensuring improved performance from the outset. This systematic approach aims to balance block processing efficiency, network latency, and system throughput, offering a robust solution to improve blockchain performance across diverse business contexts.

Feature attribution explains machine decisions by quantifying each feature’s contribution. While numerous approaches rely on exact gradient measurements, recent work has adopted gradient estimation to derive explanatory information under query-level access, a restrictive yet more practical accessibility assumption known as the black-box setting. Following this direction, this paper introduces GEFA (Gradient-estimation-based Explanation For All), a general feature attribution framework leveraging proxy gradient estimation. Unlike the previous attempt that focused on explaining image classifiers, the proposed explainer derives feature attributions in a proxy space, making it generally applicable to arbitrary black-box models, regardless of input type. In addition to its close relationship with Integrated Gradients, our approach, a path method built upon estimated gradients, surprisingly produces unbiased estimates of Shapley Values. Compared to traditional sampling-based Shapley Value estimators, GEFA avoids potential information waste sourced from computing marginal contributions, thereby improving explanation quality, as demonstrated in quantitative evaluations across various settings.

Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to improve synthetic instance quality, especially for explaining text classifiers. These approaches overcome the challenges in neighborhood construction posed by the unstructured nature of texts, thereby improving the quality of explanations. However, the deployed generators are usually implemented via neural networks and lack inherent explainability, sparking arguments over the transparency of the explanation process itself. To address this limitation while preserving neighborhood quality, this paper introduces a probability-based editing method as an alternative to black-box text generators. This approach generates neighboring texts by implementing manipulations based on in-text contexts. Substituting the generator-based construction process with recur-sive probability-based editing, the resultant explanation method, XPROB (explainer with probability-based editing), exhibits com-petitive performance according to the evaluation conducted on two real-world datasets. Additionally, XPROB's fully transparent and more controllable construction process leads to superior stability compared to the generator-based explainers.

Attribution methods shed light on the explainability of data-driven approaches such as deep learning models by uncovering the most influential features in a to-be-explained decision. While determining feature attributions via gradients delivers promising results, the internal access required for acquiring gradients can be impractical under safety concerns, thus limiting the applicability of gradient-based approaches. In response to such limited flexibility, this paper presents GEEX (gradient-estimation-based explanation), a method that produces gradient-like explanations through only query-level access. The proposed approach holds a set of fundamental properties for attribution methods, which are mathematically rigorously proved, ensuring the quality of its explanations. In addition to the theoretical analysis, with a focus on image data, the experimental results empirically demonstrate the superiority of the proposed method over state-of-the-art black-box methods and its competitive performance compared to methods with full access.

We give an operational definition of information-theoretic resources within a given multipartite classical or quantum correlation. We present our causal model that serves as the source coding side of this correlation and introduce a novel concept of resource rate. We argue that, beyond classical secrecy, additional resources exist that are useful for the security of distributed computing problems, which can be captured by the resource rate. Furthermore, we establish a relationship between resource rate and an extension of Shannon's logarithmic information measure, namely, total correlation.

Compressive Sensing (CS) has well boosted massive random access protocols over the last decade. Usually, on physical layer, the protocols employ some fat matrix with the property that sparse vectors in the much larger column space domain can still be recovered. This, in turn, greatly reduces the chances of collisions between access devices. This basic scheme has meanwhile been enhanced in various directions but the system cannot operate in overload regime, i.e. sustain significantly more users than the row dimension of the fat matrix dictates. In this paper, we take a different route and apply an orthogonal DFT basis as it is used in OFDM, but subdivide its image into so-called sub-channels and let each sub-channel take only a fraction of the load. In a random fashion the subdivision is consecutively applied over a suitable number of time-slots. Within the time-slots the users will not change their sub-channel assignment and send in parallel the data. Activity detection is carried out jointly across time-slots in each of the sub-channels. For such system design we derive three rather fundamental results: i) First, we prove that the subdivision can be driven to the extent that the activity in each sub-channel is sparse by design. An effect that we call sparsity capture effect. ii) Second, we prove that effectively the system can sustain any overload situation relative to the DFT dimension, i.e. detection failure of active and non-active users can be kept below any desired threshold regardless of the number of users. The only price to pay is delay, i.e. the number of time-slots over which cross-detection is performed. We achieve this by jointly exploring the effect of measure concentration in time and frequency and careful system parameter scaling. iii) Third, we prove that parallel to activity detection active users can carry one symbol per pilot and time-slot so it supports so-called one-shot messaging. The key to proving these results are new concentration results for sequences of randomly sub-sampled DFTs detecting the sparse vectors “en bloc”. Eventually, we show by simulations that the system is scalable resulting in a coarsely 20-fold capacity increase compared to standard OFDM.

Hate speech detection is a common downstream application of natural language processing (NLP) in the real world. In spite of the increasing accuracy, current data-driven approaches could easily learn biases from the imbalanced data distributions originating from humans. The deployment of biased models could further enhance the existing social biases. But unlike handling tabular data, defining and mitigating biases in text classifiers, which deal with unstructured data, are more challenging. A popular solution for improving machine learning fairness in NLP is to conduct the debiasing process with a list of potentially discriminated words given by human annotators. In addition to suffering from the risks of overlooking the biased terms, exhaustively identifying bias with human annotators are unsustainable since discrimination is variable among different datasets and may evolve over time. To this end, we propose an automatic misuse detector (MiD) relying on an explanation method for detecting potential bias. And built upon that, an end-to-end debiasing framework with the proposed staged correction is designed for text classifiers without any external resources required.

We present an algorithm for the numerical evaluation of the state-space distribution of an Age-of-Information network. Given enough computational resources, the evaluation can be performed to an arbitrary high precision. An Age-of-Information network is described by a vector of natural numbers, that track how outdated status information from various agents is. Our algorithm yields the means to determine any moment of the corresponding stochastic process. This can be extremely valuable for cases in which the network consists of controllers that communicate with one another, as it potentially allows for less conservative control behavior. It also enables the comparison of different policies regarding their performance (minimizing the average Age-of-Information) to a much more accurate degree than was possible before. This is illustrated using the conventional MaxWeight policy and the optimal policy. We also validate and compare the algorithm with Monte-Carlo-Simulations.

The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems. Of particular importance is the development of model-free techniques that work without knowledge about the underlying channel. Such techniques utilize for example generative adversarial networks to estimate and model the conditional channel distribution, mutual information estimation as a reward function, or reinforcement learning. In this paper, the approach of reinforcement learning is studied and, in particular, the policy gradient method for a model-free approach of neural network-based secure encoding is investigated. Previously developed techniques for enforcing a certain co-set structure on the encoding process can be combined with recent reinforcement learning approaches. This new approach is evaluated by extensive simulations, and it is demonstrated that the resulting decoding performance of an eavesdropper is capped at a certain error level.

The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard inequalities such as the inequality of arithmetic and geometric means or the Hölder inequality. In a probabilistic setting, the Jensen inequality describes the relationship between a convex function and the expected value. In this work, we want to look at the probabilistic setting from the reverse direction of the inequality. We show that under minimal constraints and with a proper scaling, the Jensen inequality can be reversed. We believe that the resulting tool can be helpful for many applications and provide a variational estimation of mutual information, where the reverse inequality leads to a new estimator with superior training behavior compared to current estimators.

Using commodity WiFi data for applications such as indoor localization, object identification and tracking and channel sounding has recently gained considerable attention. We study the problem of channel impulse response (CIR) estimation from commodity WiFi channel state information (CSI). The accuracy of a CIR estimation method in this setup is limited by both the available channel bandwidth as well as various CSI distortions induced by the underlying hardware. We propose a multi-band splicing method that increases channel bandwidth by combining CSI data across multiple frequency bands. In order to compensate for the CSI distortions, we develop a per-band processing algorithm that is able to estimate the distortion parameters and remove them to yield the “clean” CSI. This algorithm incorporates the atomic norm denoising sparse recovery method to exploit channel sparsity. Splicing clean CSI over M frequency bands, we use orthogonal matching pursuit (OMP) as an estimation method to recover the sparse CIR with high (M-fold) resolution. Unlike previous works in the literature, our method does not appeal to any limiting assumption on the CIR (other than the widely accepted sparsity assumption) or any ad hoc processing for distortion removal. We show, empirically, that the proposed method outperforms the state of the art in terms of localization accuracy.

End-to-end learning of communication systems with neural networks and particularly autoencoders is an emerging research direction which gained popularity in the last year. In this approach, neural networks learn to simultaneously optimize encoding and decoding functions to establish reliable message transmission. In this paper, this line of thinking is extended to communication scenarios in which an eavesdropper must further be kept ignorant about the communication. The secrecy of the transmission is achieved by utilizing a modified secure loss function based on cross-entropy which can be implemented with state-of-the-art machine-learning libraries. This secure loss function approach is applied in a Gaussian wiretap channel setup, for which it is shown that the neural network learns a trade-off between reliable communication and information secrecy by clustering learned constellations. As a result, an eavesdropper with higher noise cannot distinguish between the symbols anymore.

We study deterministic approximations of the Gaussian two-user multiple access wiretap channel (G-MAC-WT) and the Gaussian wiretap channel with a helper (G-WT-H). These approximations enable results beyond the recently shown 2/3 and 1/2 secure degrees of freedom (s.d.o.f.) for the G-MAC-WT and the G-WT-H, respectively. While the s.d.o.f. were obtained by real interference alignment, our approach uses signal-scale alignment. We show achievable schemes which are independent of the rationality of the channel gains. Moreover, our results can differentiate between channel strengths, in particular, between both users, and will establish secrecy rates dependent on this difference. We can show that the resulting achievable secrecy rates tend to the s.d.o.f. for vanishing channel gain differences. Moreover, we extend previous and develop new techniques to prove generalized s.d.o.f. bounds for varying channel strengths and show that our achievable schemes reach the bounds for certain channel gain parameters. We believe that our analysis is the next step toward a constant-gap analysis of the G-MAC-WT and the G-WT-H.

Numerical evidence suggests that compressive sensing (CS) approaches for wideband massive MIMO channel estimation can achieve very good performance with limited training overhead by exploiting the sparsity of the physical channel. However, analytical characterization of the (minimum) training overhead requirements is still an open issue. By observing that the wideband massive MIMO channel can be represented by a vector that is not simply sparse but has well defined structural properties, referred to as hierarchical sparsity, we propose low complexity channel estimators for the uplink multiuser scenario that take this property into account. By employing the framework of the hierarchical restricted isometry property, rigorous performance guarantees for these algorithms are provided suggesting concrete design goals for the user pilot sequences. For a specific design, we analytically characterize the scaling of the required pilot overhead with increasing number of antennas and bandwidth, revealing that, as long as the number of antennas is sufficiently large, it is independent of the per user channel sparsity level as well as the number of active users. These analytical insights are verified by simulations demonstrating also the superiority of the proposed algorithm over conventional CS algorithms that ignore the hierarchical sparsity property.

Towards reducing the training signaling overhead in large scale and dense cloud radio access networks (CRAN), various approaches have been proposed based on the channel sparsification assumption, namely, only a small subset of the deployed remote radio heads (RRHs) are of significance to any user in the system. Motivated by the potential of compressive sensing (CS) techniques in this setting, this paper provides a rigorous description of the performance limits of many practical CS algorithms by considering the performance of the, so called, oracle estimator, which knows a priori which RRHs are of significance but not their corresponding channel values. By using tools from stochastic geometry, a closed form analytical expression of the oracle estimator performance is obtained, averaged over distribution of RRH positions and channel statistics. Apart from a bound on practical CS algorithms, the analysis provides important design insights, e.g., on how the training sequence length affects performance, and identifies the operational conditions where the channel sparsification assumption is valid. It is shown that the latter is true only in operational conditions with sufficiently large path loss exponents.

ONE5G (E2E-aware Optimizations and advancements for the Network Edge of 5G New Radio) is an European funded collaborative project, aiming at designing Radio Access Network (RAN) enhancements to address the multiplicity of services and deployment scenarios for 5G. The project will build upon the specification already defined in 3GPP to propose advanced link techniques and optimization schemes taking into account an end-to-end (E2E) performance view. This paper describes the scenarios considered in the project and the set of uses cases considered, as well as the approach to define Key Performance Indicators reflecting the E2E performance. The technical areas investigated in the project are presented, as well as the planned prototypes.

It is well-known that wireless channel reciprocity together with fading can be exploited to generate a common secret key between two legitimate communication partners. This can be achieved by exchanging known deterministic pilot signals between both partners from which the random fading gains can be estimated and processed. However, the entropy and thus quality of the generated key depends on the channel coherence time. This can result in poor key generation rates in a low mobility environment, where the fading gains are nearly constant. Therefore, wide-spread deployment of wireless channel-based secret key generation is limited. To overcome these issues, we follow up on a recent idea which uses unknown random pilots and enables “on-the-fly” key generation. In addition, the scheme is able to incorporate local sources of randomness but performance bounds are hard to obtain with standard methods. In this paper, we analyse such a scheme analytically and derive achievable key rates in the Alice-Bob-Eve setting. For this purpose, we develop a novel approximation model which is inspired by the linear deterministic and the lower triangular deterministic model. We claim that our novel approach provides an intuitive and clear framework to analyse similar key generation problems.

In this paper, we study the prediction of a circularly symmetric zero-mean stationary Gaussian process from a window of observations consisting of finitely many samples. This is a prevalent problem in a wide range of applications in communication theory and signal processing. Due to stationarity, when the autocorrelation function or equivalently the power spectral density (PSD) of the process is available, the Minimum Mean Squared Error (MMSE) predictor is readily obtained. In particular, it is given by a linear operator that depends on autocorrelation of the process as well as the noise power in the observed samples. The prediction becomes, however, quite challenging when the PSD of the process is unknown. In this paper, we propose a blind predictor that does not require the a priori knowledge of the PSD of the process and compare its performance with that of an MMSE predictor that has a full knowledge of the PSD. To design such a blind predictor, we use the random spectral representation of a stationary Gaussian process. We apply the well-known atomic-norm minimization technique to the observed samples to obtain a discrete quantization of the underlying random spectrum, which we use to predict the process. Our simulation results show that this estimator has a good performance comparable with that of the MMSE estimator.

A novel waveform design approach is taken to develop a waveform based on precoding of data symbols, referred to as Precoded OFDM. The waveform is tailored for robustness against asynchronous transmission and a simple OFDM receiver to keep its intricacies hidden from the system. These features are most desired in massive Machine Type Communications (mMTC) where better intrinsic robustness is critical in avoiding the overheads used in cellular systems for synchronization.

In this paper, a new subband-filtered and pulse-shaped multicarrier (SBF-PS-MC) scheme is introduced and investigated in a generalized framework. The main motivation behind is to capture inter-user quasi-synchronicity (QS) and channel spread in 5G massive machine-type communication (mMTC) use case where uplink (UL) synchronization and other control procedures need to be simplified or even dropped to reduce signaling/protocol overhead. Based on downlink synchronization, UL transmissions from geographically distant users can entail severe relative symbol time and carrier frequency offsets by fractional amounts of multicarrier (MC) symbol interval and subcarrier spacing, respectively (i.e., interuser QS). SBF-PS-MC scheme can take advantage both of subband filtering and pulse shaping, whereby, on the one hand, other-subband (or other-user) interference (OBI) is effectively suppressed by subband filtering and, on the other hand, intra-subband and inter-symbol interference is reduced and OBI even further mitigated by pulse shaping. A main contribution is to provide a generalized framework for SBF-PS-MC scheme to handle each of the interference terms analytically dependent on the used subband filter and prototype pulse. Eventually, SBF-PS-MC is numerically evaluated over inter-user QS in time and delay spread channel that per-subband average signal to interference plus noise ratio (SINR) of SBF-PS-MC is superior to those of conventional cyclic prefix orthogonal frequency division multiplexing (OFDM), weighted overlap-add OFDM, universal filtered OFDM, and subband-filtered OFDM.

There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.

Recent investigations have shown that the sum secure degrees of freedom of the Gaussian wiretap channel with a helper is 1/2. The achievable scheme for this result is based on the real interference alignment approach. While providing a good way to show degrees of freedom results, this technique has the disadvantage of relying on the Khintchine-Groshev theorem and is therefore limited to almost all channel gains. This means that there are infinitely many channel gains, where the scheme fails. Furthermore, the real interference alignment approach cannot be used to yield stronger constant-gap results. We approach this topic from a signal-scale alignment perspective and use the linear deterministic model as a first approximation. Here we can show a constant-gap sum capacity for certain channel gain parameters. We transfer these results to the Gaussian model and discuss the results.

The use of wireless channel reciprocity properties for secret key generation has been an attractive method for many wireless applications. Most of the available methods rely on some sort of pilot signalling from the transceiver. This reduces the intrinsic security of the generated key. In this paper we have introduced a novel key generation and exchange method that requires no pilot signalling and have also included an algorithm to restore wireless channel reciprocity properties for varying transmission power. Finally we presented information theoretic analysis and the result of our implementation on off-the-shelf TelosB motes.

In November 2015 the latest world radio conference took place and as a result the below 6 GHz RF bands have become more important for 5G in the foreseeable future. In the 5GPPP project Fantastic5G options for higher spectral efficiency based on massive MIMO, joint transmission cooperative multi point and interference mitigation are being investigated. Most of these concepts have been already developed in previous projects like Artist4G or METIS, but now it is important to integrate these into a flexible framework supporting a rudimentary phase I as well as a future proof phase II system. Interference mitigation might become the main differentiator to LTE and the inevitable enablers will be shortly highlighted. According to current results the novel 5G system has the potential to increase spectral efficiency by roughly a factor of ten compared to a 4x2 LTE system, thereby significantly outperforming also latest LTE results for full dimension MIMO.

Recent investigations have shown that approximations of certain channels by the linear deterministic model (LDM) are sub-optimal in some cases. Replacing the LDM by a more sophisticated deterministic model possibly results in improvements of previous results. An example is use of the lower triangular deterministic model (LTDM) to replace the LDM. Due to the integrated dependence on the fine channel gains, results can be obtained which were not previously possible. In this paper, we investigate the IMAC under the approximation of this refined model. It was previously demonstrated, that multi-user gain is present in the conventional deterministic IMAC model. However, the capacity could be shown just for discrete points due to limitations of the approximation model. In this paper, we propose a scheme for the new approximation model, which reaches the upper bound completely and therefore provides the sum-capacity. Moreover, the scheme itself has a much more simplistic structure.

Recent investigations have shown that multi-user gain can be enabled in frequency-flat time-invariant single-antenna cellular networks, for example the interfering multiple access channel (IMAC). These investigations have shown gain beyond results through treating interference as noise (TIN) techniques in the weak interference regime, rendering TIN sub-optimal for these networks. However, it was shown previously that multi-user gain for the multiple access channel interfering with a point-to-point link (MAC-P2P) is limited to the regime equation, with a relapse of the rate to IC level above this regime. Previous results for the IMAC were limited to the very weak interference regime α ≤ 1 over 2. The question is if multi-user gain of the deterministic IMAC is also limited to this regime. We answer this question with no and show the sum-capacity approximated by the lower triangular deterministic model for the IMAC and that there is significant multi-user gain for α ≥ 2 over 3. We therefore explore the GDoF of the deterministic IMAC for the whole interference range.

The high PAPR problem has been a major concern in waveform design and imposes substantial limitations. On the other hand, the area of research on enabling technologies, specifically waveform design, is very active due to the demands of the next generations of wireless systems. A promising method to reduce the Peak-to-Mean Envelope Power Ratio (PMEPR) of the OFDM signal is to choose the best sign changes for the underlying data symbols through a deterministic or derandomized algorithm. In particular, high performance and mathematical tractability of the method motivates further research on its usefulness. This paper investigates the performance of this method for FBMC waveform, as one of the candidate waveforms which has attracted considerable attention. It will be shown that despite the complex structure of the FBMC signal, the method is completely applicable. The performance is impressive, about 4.6 dB for 1024 subcarriers. In addition, some interesting properties of the algorithm are noticed.

In theory, coordinated multipoint transmission (CoMP) promises vast gains in spectral efficiency. However, industrial field trials show rather disappointing throughput gains, whereby the major limiting factor is proper sharing of channel state information. Many recent papers have considered this so-called limited feedback problem in the context of CoMP, usually taking the following assumptions, namely, infinite SNR regime, no user selection, and ideal link adaptation, rendering the analysis too optimistic. In this paper, we make a step forward toward a more realistic assessment of the limited feedback problem by introducing an improved metric for the performance evaluation, which better captures the throughput degradation. We find the relevant scaling laws (lower and upper bounds) and show that they are different from existing ones. Moreover, we provide a robust iterative interference alignment algorithm and corresponding feedback strategies achieving the obtained scaling laws. The main idea is that, instead of sending the complete channel matrix, each user fixes a receive filter and feeds back a quantized version of the effective channel. Finally, we underline our findings with simulations for the proposed system.

The MAC-BC duality of information theory and wireless communications is an intriguing concept for efficient algorithm design. However, no concept is known so far for the important cellular channel. To make progress on this front, we consider in this paper the linear deterministic cellular channel. In particular, we prove duality of a network with two interfering MACs in each cell and a network with two interfering BCs in each cell. The operational region is confined to the weak interference regime. First, achievable schemes as well as upper bounds will be provided. These bounds are the same for both channels. We will show, that for specific cases the upper bound corresponds to the achievable scheme and hence establishing a duality relationship between them.

There has been major progress over the last decade in understanding the classical interference channel (IC). Recent key results show that constant bit gap capacity results can be obtained from linear deterministic models (LDMs). However, it is widely unrecognized that the time-invariant, frequency-flat cellular channel, which contains the IC as a special case, possesses some additional generalized degrees of freedom (GDoF) due to multi-user operation. This was proved for the LDM cellular channel very recently but is an open question for the corresponding Gaussian counterpart. In this paper, we close this gap and provide an achievable sum-rate for the Gaussian cellular channel which is within a constant bit gap of the LDM sum capacity. We show that the additional GDoFs from the LDM cellular channel carry over. This is enabled by signal scale alignment. In particular, the multi-user gain reduces the interference by half in the 2-user per cell case compared to the IC.

LTE and LTE-Advanced have been optimized to deliver high bandwidth pipes to wireless users. The transport mechanisms have been tailored to maximize single cell performance by enforcing strict synchronism and orthogonality within a single cell and within a single contiguous frequency band. Various emerging trends reveal major shortcomings of those design criteria: (1) The fraction of machine-type-communications (MTC) is growing fast. Transmissions of this kind are suffering from the bulky procedures necessary to ensure strict synchronism. (2) Collaborative schemes have been introduced to boost capacity and coverage (CoMP), and wireless networks are becoming more and more heterogeneous following the non-uniform distribution of users. Tremendous efforts must be spent to collect the gains and to manage such systems under the premise of strict synchronism and orthogonality. (3) The advent of the Digital Agenda and the introduction of carrier aggregation are forcing the transmission systems to deal with fragmented spectrum. 5GNOW will question the design targets of LTE and LTE-Advanced having these shortcomings in mind. The obedience of LTE and LTE-Advanced to strict synchronism and orthogonality will be challenged. It will develop new PHY and MAC layer concepts being better suited to meet the upcoming needs with respect to service variety and heterogeneous transmission setups. A demonstrator will be built as Proof-of-Concept relying upon continuously growing capabilities of silicon based processing. Wireless transmission networks following the outcomes of 5GNOW will be better suited to meet the manifoldness of services, device classes and transmission setups being present in envisioned future scenarios like smart cities. The integration of systems relying heavily on MTC, e.g. sensor networks, into the communication network will be eased. The per-user experience will be more uniform and satisfying. To ensure this 5GNOW will contribute to upcoming 5G standardization.

In the downlink of Long Term Evolution (LTE) systems, feedback and processing delays cause a mismatch between the current channel state and the Channel Quality Information (CQI) at the base station. This CQI aging leads to inaccurate channel adaptation and can, thus, highly degrade the cell capacity. To compensate for this performance loss, we study several CQI predictors under realistic delay and channel assumptions. Our results on cell throughput show that linear prediction with Stochastic Approximation provides at least the performance gains of the computationally more complex covariance-based linear predictors and Kalman filters. This surprising result points to Stochastic Approximation as a powerful and practical technique to increase downlink performance with limited channel knowledge.

An iterative interference alignment algorithm for cellular systems with multiple signaling dimensions is introduced. The main invention is that we combine iterative interference alignment with user selection, which naturally increases the system sum rate by exploiting the multiuser diversity. Extensive simulations show significant gains over single cell processing and joint/coherent transmit schemes, also with partial channel state information.

In this paper, we use the linear deterministic approximation model to study a two user multiple access channel (MAC) mutually interfering with a point to point link, which represents a basic model for the situation of device-to-device communication inside a cell. We derive upper bounds on the achievable sum rate and construct coding schemes achieving the upper bounds. For a large parameter range, the sum capacity is identical to the sum capacity of the interference channel obtained by silencing the weaker user in the MAC. For other interference configurations, the sum rate can be increased using interference alignment, which exploits the channel gain difference of the users in the MAC. From these results, lower bounds on the generalized degrees of freedom for the Gaussian counterpart are derived.

The control of large queueing networks is a notoriously difficult problem. Recently, an interesting new policy design framework for the control problem called h-MaxWeight has been proposed: h-MaxWeight is a natural generalization of the famous MaxWeight policy where instead of the quadratic any other surrogate value function can be applied. Stability of the policy is then achieved through a perturbation technique. However, stability crucially depends on parameter choice which has to be adapted in simulations. In this paper we use a different perturbation technique where the required properties are much easier to implement. Specifically, we derive the theoretical fundamentals which guarantee universal stability while still operating `close' to the underlying cost criterion. Simulation examples suggest that the new approach to policy synthesis can provide significantly higher gains irrespective of any further assumptions on the network model or parameter choice.

We present and analyze a new robust feedback and transmit strategy for multiuser MIMO downlink communication systems, termed Rate Approximation (RA). RA combines the flexibility and robustness needed for reliable communications with the user terminal under a limited feedback constraint. It responds to two important observations. First, it is not so significant to approximate the channel but rather the rate, such that the optimal scheduling decision can be mimicked at the base station. Second, a fixed transmit codebook at the transmitter is often better when therefore the channel state information is more accurate. In the RA scheme the transmit and feedback codebook are separated and user rates are delivered to the base station subject to a controlled uniform error. The scheme is analyzed and proved to have better performance below a certain interference plus noise margin and better behavior than the classical Jindal formula. LTE system simulations sustain the analytic results showing performance gains of up to 50% or 70% compared to zeroforcing when using multiple antennas at the base station and multiple antennas or a single antenna at the terminals, respectively. A new feedback protocol is developed which inherently considers the transmit codebook and which is able to deal with the complexity issue at the terminal.

In this paper, we study the uplink of a cellular system using the linear deterministic approximation model, where there are two users transmitting to a receiver, mutually interfering with a third transmitter communicating with a second receiver. We give an achievable coding scheme and prove its optimality, i.e. characterize the capacity region. This scheme is a form of interference alignment which exploits the channel gain difference of the two-user cell.

The effective PAPR of the transmit signal is the standard metric to capture the effect of nonlinear distortion in OFDM transmission. A common rule of thumb is the log(N) barrier where N is the number of subcarriers which has been theoretically analyzed by many authors. Recently, new alternative metrics have been proposed in practice leading potentially to different system design rules which are theoretically analyzed in this paper. One of the main findings is that, most surprisingly, the log(N) barrier turns out to be much too conservative: e.g. for the so-called amplifier-oriented metric the scaling is rather log [log (N)]. To prove this result, new upper bounds on the PAPR distribution for coded systems are presented as well as a theorem relating PAPR results to these alternative metrics.

This paper deals with computation of the capacity region of the discrete memoryless multiple-access channel (MAC), which is equivalent to solving a difficult nonconvex optimization problem. In the literature, it is claimed that for elementary MACs, i.e., MACs for which the size of output alphabet is greater or equal to the sizes of all input alphabets, the Karush-Kuhn-Tucker conditions provide a necessary and sufficient condition for sum-rate optimality. In this paper, we demonstrate that this claim does not hold, even for two-user channels with binary input and binary output alphabets. Consequently, the capacity computation problem for the discrete MAC remains an interesting and mostly unsolved problem.

In this paper, we study duality relations for the fading broadcast channel (BC) under additive white Gaussian noise and a strict ordering of the fading distributions without channel state information at the transmitter and the natural corresponding dual fading multiple-access channel (MAC). We show that if the fading distribution for the weaker user is non-deterministic, the achievable rate region using superposition coding and successive decoding and Gaussian signaling, which is conjectured to be the capacity region, is different from the dual MAC region. Duality holds only in the case of one-sided fading, where the fading distribution for the weaker user is deterministic. Despite this lack of duality, we propose to use the dual MAC in order to approximatively solve a non-convex problem for the BC. Specifically, we consider the problem of weighted sum-rate optimization for the BC and give, under some assumptions, upper bounds on the error incurred using this procedure.

In this paper we present a new paradigm for multiuser MIMO downlink communications called Rate Approximation with significant impact on the upcoming LTE standard. Rate Approximation combines flexibility and robustness needed for reliable communications with the terminal in the downlink and taking advantage of the channel state information. The scheme responds to two major developments in the recent literature: One observation is that it is not so important to approximate the channel but rather the rate itself. The second observation is that a fixed codebook at the transmitter is often better when simultaneously the channel state information is more accurate. Both observations are incorporated in the new scheme where the transmit and feedback codebook are strictly separated and user rates are brought to the base station subject to a controlled uniform error. The new metric is amenable to further numerical optimization. Analysis and simulations show the superior performance of the scheme and, furthermore, a strong impact on upcoming cooperative schemes is expected.

Duality between multiple-access (MAC) and broadcast channels (BC) is an interesting and useful concept in multi-user information theory. While duality relationships have been established for some Gaussian and deterministic channels, the question to what extent and under which conditions duality holds for the case of discrete memoryless channels still remains open. In this paper, we define the notion of weak duality, closely related to the existing duality definitions. We give a sufficient condition and a necessary condition for a BC to be weakly dual to a discrete MAC of a certain type. Exemplarily, we use these conditions to derive a weak duality relation between the binary symmetric BC and this class of discrete MACs.

We present a new robust feedback and transmit strategy for multiuser MIMO downlink communication systems, termed Rate Approximation (RA), and analyze its performance. The new scheme combines flexibility and robustness needed for reliable communications with the user terminal, under a limited feedback constraint. The scheme responds to two major developments in the recent literature: One observation is that it is not so important to approximate the channel but rather the rate itself. The second observation is that a fixed codebook at the transmitter is often better when simultaneously the channel state information is more accurate. Both observations are incorporated in the new scheme where the transmit and feedback codebook are strictly separated and user rates are brought to the base station subject to a controlled uniform error. The analysis provides two astonishing results. First, under perfect channel state information at the transmitter RA outperforms zeroforcing beamforming (ZFBF) for a large fraction of the practically relevant signal-to-noise ratio range for the considered operating point. Second, under a limited feedback constraint the quantization error scaling of RA is shown to be doubly exponentially in the number of feedback bits, whereas the quantization error of ZFBF with random vector quantization was shown by Jindal to scale only exponentially. Simulations sustain our analytic results showing the superior performance of the new scheme.

This paper studies the problem of admission control and air interface (AI) selection in heterogeneous network environments. The statistics of random user arrivals, channel conditions and service durations are considered for the optimization of heterogeneous access management strategies with respect to minimizing the expected mean cost for blocking events. Based on state aggregation in a semi-Markov decision process formulation, an efficient approximation algorithm using state aggregation for policy optimization is proposed. Though this solution is suboptimal, it still offers considerable performance gains in comparison to simpler heuristic strategies, which is demonstrated by simulations in a heterogeneous GSM-EDGE (Global System for Mobile Communications Enhanced Data Rate)/UMTS (Universal Mobile Telecommunications System) scenario. Furthermore, structural properties of optimal user assignment policies are studied, proving certain monotonicity properties for a specific type of systems.

We propose a new limited feedback scheme for the downlink of multiuser MIMO OFDM systems based on fixed linear beamforming, i.e. the linear beamforming vectors are chosen from a fixed transmit codebook. The proposed feedback method allows the base station to uniformly approximate all multiuser rates for any selection of users and any combination of beamforming vectors defined by the transmit codebook; thus providing all degrees of freedom for the user selection. The approximation of the multiuser rates is enabled by using an additional codebook for the feedback. This has several advantages: the transmit codebook can be designed independent of the feedback codebook, the accuracy of the approximated rates can be scaled by changing the size of the feedback codebook and the feedback codebook can be adapted to the environment. We show how feedback codebooks can be designed for arbitrary environments using the LBG algorithm. Moreover, we show how the computational complexity of the proposed feedback method can be reduced without a significant performance loss. In the simulations we demonstrate that the proposed method outperforms other methods within the LTE context.

This paper deals with the problem of computing the boundary of the capacity region for the memoryless two-user binary-input binary-output multiple-access channel ((2, 2; 2)-MAC), or equivalently, the computation of input probability distributions maximizing weighted sum-rate. This is equivalent to solving a difficult nonconvex optimization problem. For a restricted class of (2, 2; 2)-MACs and weight vectors, it is shown that, depending on an ordering property of the channel matrix, the optimal solution is located on the boundary, or the objective function has at most one stationary point in the interior of the domain. For this, the problem is reduced to a pseudoconcave one-dimensional optimization and the single-user problem.

Stability with respect to a given scheduling policy has become an important issue for wireless communication systems; but hard to prove in particular scenarios. In this paper two sufficient conditions for stability in a broadcast setting are derived, which are often very easy to check. Moreover, it is shown that if the given scheduling policy complies with both conditions the resulting throughput region of the policy equals the ergodic achievable rate region and the system is stable in a strong sense. This extends results presented in where the statement was shown for scheduling policies fulfilling so-called integrability condition. Additionally, in this paper we also present a partial converse to the statement which is demonstrated by an application example.

This paper studies the problem of admission control and air interface selection in heterogeneous network environments. The statistics of random user arrivals and random service durations are considered for the optimization of heterogeneous access management strategies with respect to minimizing the expected mean cost for denial-of-service events. Based on state aggregation in a semi-Markov decision process formulation, an efficient approximation algorithm for policy optimization is proposed. Though this solution is suboptimal, it still offers considerable performance gains in comparison to simpler heuristic strategies, which is demonstrated by simulations in a heterogeneous GSM-EDGE/UMTS scenario.

A single queue incorporating a retransmission protocol is investigated, assuming that the sequence of per effort success probabilities in the Automatic Retransmission reQuest (ARQ) chain is a priori defined and no channel state information at the transmitter is available. A Markov Decision Problem with an average cost criterion is formulated where the possible actions are to either continue the retransmission process of an erroneous packet at the next time slot or to drop the packet and move on to the next packet awaiting for transmission. The cost per slot is a linear combination of the current queue length and a penalty term in case dropping is chosen as action. The investigation seeks policies that provide the best possible average packet delay-dropping trade-off for Quality of Service guarantees. An optimal deterministic stationary policy is shown to exist, several structural properties of which are obtained. Based on that, a class of suboptimal-policies is introduced. These suggest that it is almost optimal to use a K-truncated ARQ protocol as long as the queue length is lower than L, else send all packets in one shot. The work concludes with an evaluation of the optimal delay-dropping tradeoff using dynamic programming and a comparison between the optimal and suboptimal policies.

Delay matters in future wireless communication. An appropriate limit for rates achievable under delay constraints is the delay limited capacity (DLC). In this work, the DLC of OFDM systems is investigated. Despite its complicated correlation structure the OFDM DLC is fully characterized for low and high SNR. It is shown that (under weak assumptions) the OFDM DLC is almost independent of the fading distribution in the low SNR region but strongly depends on the delay spread thereby achieving a capacity gain over AWGN capacity. In the high SNR region the roles are exchanged. Here, the impact of delay spread is negligible while the impact of the fading distribution becomes dominant. The relevant quantities and their asymptotic behaviour are derived without employing simplifying assumptions on the OFDM correlation structure. Using a general convergence framework the analysis further shows that if the delay spread becomes large even the predominant impact of the fading distribution vanishes and DLC capacity loss compared to AWGN capacity approaches 0.58[nats/s/Hz]. The convergence speed, the loss due to non-uniform power delay profile, and the relation to ergodic capacity is also analyzed and underlined with simulations and application examples. The main conclusion here is that OFDM fully takes advantage of the degrees of freedom of the underlying fading channel in terms of delay spread and, regardless of the fading distribution, delay sensitive capacity measures such as the DLC converge to the ergodic capacity. Finally, since universal bounds are obtained which apply to any fading distribution the results can also be used for other classes of parallel channels extending the range of applicability.

While multiuser scheduling strategies have been intensively studied in the context of OFDMA downlink recently, the resulting throughput and delay performance affecting strongly end-to-end performance of wireless communication systems can still be evaluated by simulations only. In this paper we approach this problem and consider bounds for delay and queue backlog for a large class of scheduling policies. Adopting a general state space Markov chain model the concept of policy-specific throughput regions is introduced. Then, under the regime of the policy, a recursive formula for calculating all polynomial moments of the queue backlog is derived. Moreover, it is shown that even exponential decay of the tail distribution can be obtained under proper circumstances. Based on these results, upper bounds on the buffer overflow probability are derived giving insights for practical buffer dimensioning problems in UMTS LTE systems.

While multiuser scheduling strategies have been intensively studied recently, the resulting throughput and delay performance affecting strongly end-to-end performance of wireless communication systems can still be evaluated by simulations only. In this paper we approach this problem and consider bounds for delay and queue backlog for a large class of scheduling policies. Adopting a general state space Markov chain model the concept of policy-specific throughput regions is introduced. Then, under the regime of the policy, a recursive formula for calculating all polynomial moments of the queue backlog is provided. Moreover, it is shown that even exponential decay of the tail distribution can be obtained under proper circumstances. Based on these results, upper bounds on the buffer overflow probability are derived giving insights for practical buffer dimensioning problems.

In this paper we cover the problem of resource allocation in terms of optimum air-interface and cell selection in cellular, heterogeneous multi-air-interface scenarios. For a given set of voice users with minimum quality of service requirements and a set of best effort users we find the optimum allocation that guarantees service for the voice users and maximizes the sum utility of the best effort users. Our model applies for arbitrary heterogeneous scenarios where the air-interfaces belong to the class of interference limited systems like UMTS or to a class with orthogonal resource assignment such as TDMA based GSM or WLAN. We achieve convexity of the problem by a transformation into the domain of mean square errors. Using a dual problem formulation we derive straight forward assignment rules and develop a decentralized algorithm, which solves the optimization problem. Simulation results for a heterogeneous UMTS/GSM scenario show high performance gains of the proposed algorithm compared to a Load Balancing strategy.

A new scheduling algorithm that achieves very low delay is presented in this paper and applied to Orthogonal Frequency Division Multiplexing (OFDM) broadcast channels (BC). The general approach consists of two parts: First, weight factors are found for the delay minimization problem with respect to the ergodic capacity region. An efficient, so called Idle State Prediction Algorithm is provided. In the second step resources are allocated by maximizing the weighted sum of rates according to the instantaneous capacity region. This technique largely simplifies the combinatorial structure of the cross-layer optimization problem. Simulation results confirm the superior performance of the presented scheme.

The main task in OFDM downlink scheduling design is to maximize some kind of utility considering user specific as well as system induced constraints. A typical example is the weighted sum rate maximization under a sum power constraint and possibly additional rate requirements. A major challenge is to incorporate the fact that in real systems the number of modulation depths and coding schemes is limited due to technical conditions. Thus, the scheduling problem results in a discrete optimization problem with non-differentiable non-convex objective. In this paper a new approach is presented that can deal with this kind of constraints in a general way. By transforming the discrete problem in a suitable weight matching problem an efficient algorithm which is easy to implement is devised. The performance of this algorithm is evaluated by simulations with parameters obtained from the recently proposed OFDM-HSDPA concept [1].

This paper covers the issue of how mobile users of different service classes should be assigned to a set of radio access technologies (RATs) with overlapping coverage from a practical perspective. The aim is to allocate users with minimum rate constraints to all RATs that the weighted sum of assignable users is maximized. We introduce the concept of resource costs to formulate an optimization problem, which is very expensive to solve (NP-complete). Using the approach of continuous relaxation, we develop a suboptimal radio access selection algorithm which is designed for practical applications and converges close to the optimum. Simulation results show considerable gains of the utility in comparison to a standard Load Balancing Strategy.

We consider the downlink of a multi-antenna system where the transmitter performs nonlinear dirty-paper coding (DPC) and each receiver is equipped with a single antenna. Assuming that the base station must support a certain quality of service (QoS) for each user, we analyze the minimum power transmit strategy consisting of filters, power allocation and precoding order. We derive necessary and sufficient conditions for the optimality of pure strategies, i.e. strategies achieving the QoS without time-sharing. Based on these conditions, we present a very simple algorithm yielding a suboptimal pure strategy showing excellent performance. This is of considerable interest especially for real systems, where time-sharing is hard to implement.

Optimal link adaption to the scattering function of wide sense stationary uncorrelated scattering (WSSUS) mobile communication channels is still an unsolved problem despite its importance for the next-generation system design. In a multicarrier transmission, such link adaption is performed by pulse shaping, i.e., by properly adjusting the transmit and receive filters. Pulse-shaped offset–quadratic-amplitude-modulation systems have been recently shown to have superior performance over standard cyclic prefix orthogonal frequency-division multiplexing (while operating at higher spectral efficiency). In this paper, we establish a general mathematical framework for joint transmitter and receiver pulse-shape optimization for so-called Weyl–Heisenberg or Gabor signaling, with respect to the scattering function of the WSSUS channel. In our framework, the pulse shape optimization problem is translated to an optimization problem over trace class operators, which in turn is related to fidelity optimization in quantum information processing. By convexity relaxation, the problem is shown to be equivalent to a convex constraint quasi-convex maximization problem, thereby revealing the nonconvex nature of the overall WSSUS pulse design problem. We present several iterative algorithms for optimization, providing applicable results even for large-scale problem constellations. We show that with transmitter-side knowledge of the channel statistics, a gain of 3–6 dB in signal-to-interference-plus-noise ratio can be expected.

Optimal link adaption to the scattering function of wide-sense stationary uncorrelated scattering (WSSUS) mobile communication channels is still an unsolved problem despite its importance for next-generation system design. In multicarrier transmission, such link adaption is performed by pulse shaping, i.e., by properly adjusting the transmit and receive filters. For example, pulse-shaped offset-quadrature amplitude modulation (OQAM) systems have recently been shown to have superior performance over standard cyclic prefix orthogonal frequency-division multiplexing (OFDM) (while operating at higher spectral efficiency). In this paper, we establish a general mathematical framework for joint transmitter and receiver pulse shape optimization for so-called Weyl-Heisenberg or Gabor signaling with respect to the scattering function of the WSSUS channel. In our framework, the pulse shape optimization problem is translated to an optimization problem over trace class operators which, in turn, is related to fidelity optimization in quantum information processing. By convexity relaxation, the problem is shown to be equivalent to a convex constraint quasi-convex maximization problem thereby revealing the nonconvex nature of the overall WSSUS pulse design problem. We present several iterative algorithms for optimization providing applicable results even for large-scale problem constellations. We show that with transmitter-side knowledge of the channel statistics a gain of 3-6 dB in signal-to-interference-and-noise-ratio (SINR) can be expected.

A system consisting of$K$parallel Gaussian broadcast channels over which a transmitter communicates with$M$receivers is considered. Assuming perfect channel state information, we study the problem of maximizing a weighted sum of rates under a sum power constraint with additional receiver-specific rate constraints. This problem formulation occurs frequently, e.g., in the context of scheduling for cellular downlink systems. Two algorithms are proposed which of one is based on a modified water-filling procedure. This algorithm is also suited to solve the minimum sum power problem.

In this paper, we solve the weighted sum rate problem for an orthogonal frequency division multiplexing (OFDM) broadcast channel (BC) under a sum power constraint, if minimum rates have to be guaranteed in each fading state and perfect channel state information (CSI) is assumed at the base station and the mobiles. The problem is subdivided into two problems. First, we tackle the problem of feasibility, which occurs since the system is power limited and not all required rates might be supportable. Subsequently, the optimal resource allocation in case of feasibility is derived. Moreover, the optimal decoding order, which is not determined by the problem formulation itself, is obtained as the ordering of the Lagrangian factors of the main problem. Finally, we show that the problem is closely related to the weighted rate sum maximization and sum power minimization and all three can be interpreted in a unifying framework embedded in a higher dimensionality

In this work, we calculate the delay-limited capacity (DLC) region of an OFDM (orthogonal frequency division multiplexing) broadcast channel. Furthermore, we analyze the impact of system parameters. The main results are that under weak assumptions, the single user DLC is almost independent of the distribution of the path attenuations in the low SNR region but depends strongly on the delay spread. In the high SNR region the roles are exchanged. Here, the impact of delay spread is negligible and the impact of the distribution is dominant. Finally, we provide a result for the multiuser case.

In this paper generalized bounds on the crest-factor (CF) distribution in orthogonal frequency-division multiplexing (OFDM) transmission for both independent and dependent subcarriers are derived. Here, the latter situation represents the coded case. For independent subcarriers, a general path for bounding practical constellations is provided. Moreover, a complete characterization of their asymptotic behavior is devised and discussed. The results are shown to carry over to the spherical constellations improving on recent results. For dependent subcarriers, the focus is mainly on binary codes where bounds on the CF distribution are obtained in terms of the distance distributions and their duals. The asymptotic behavior of codes is analyzed and it is shown that the upper bound on the effective crest-factor of a large class of Bose-Chaudhuri-Hocquenghem (BCH) codes behaves asymptotically as radiclogN. Finally, two applications of the results to code design are presented: first, fixed phase shifts on the subcarriers for all codewords are used and an algorithm to calculate the phase shifts is designed. That way, it is proved that the effective CF of any binary code can be scaled to be of order radiclogN for large N without sacrificing on rate. Furthermore, the same approach is applied to calculation of the phases of redundant subcarriers for each codeword. It is shown by simulations that the values can be effectively chosen so that the CF is significantly reduced with nonexponential complexity

In this paper, we address the problem of optimal power allocation for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) broadcast channels with a sum power constraint when transmitter and receiver have perfect channel state information (CSI). We derive optimality conditions for power allocation and present an efficient algorithm achieving the optimal power allocation. Furthermore, we obtain necessary and sufficient conditions for the optimality of frequency division multiple access (FDMA) transmission and single user transmission. It turns out that the assignment of multiple users to one subcarrier is optimal in general. We propose a low complexity algorithm using a pure FDMA-scheme without multiple assignment of users and characterize the achievable rate region in comparison to the complete capacity region.

We consider the pulse design problem in multicarrier transmission where the pulse shapes are adapted to the second order statistics of the WSSUS channel. Even though the problem has been addressed by many authors analytical insights are rather limited. First we show that the problem is equivalent to the pure state channel fidelity in quantum information theory. Next we present a new approach where the original optimization functional is related to an eigenvalue problem for a pseudo differential operator by utilizing unitary representations of the Weyl-Heisenberg group. A local approximation of the operator for underspread channels is derived which implicitly covers the concepts of pulse scaling and optimal phase space displacement. The problem is reformulated as a differential equation and the optimal pulses occur as eigenstates of the harmonic oscillator Hamiltonian. Furthermore this operator-algebraic approach is extended to provide exact solutions for different classes of scattering environments

Phase noise and frequency offsets are, due to their time-variant behavior, one of the most limiting disturbances in practical orthogonal frequency-division multiplexing (OFDM) designs, and therefore, intensively studied by many authors. In this paper, we present a generalized framework for the prediction of uncoded system performance in the presence of time-variant distortions, including the transmitter and receiver pulse shapes, as well as the channel. Therefore, unlike existing studies, our approach can be employed for more general multicarrier schemes. To show the usefulness of our approach, we apply the results to OFDM in the context of frequency offset and Wiener phase noise, yielding improved bounds on the uncoded performance. In particular, we obtain exact formulas for the averaged performance in additive white Gaussian noise and time-invariant multipath channels.

In this paper bounds on the crest-factor (CF) distribution of OFDM signals are generalized as firstly, independent subcarriers a complete characterization of arbitrary complex constellations is derived; secondly, for dependent subcarriers bounds are derived in terms of the weight distribution and their duals of the underlying code. Implications on code design are also discussed. Generalizations to linear, binary codes are given in terms of the weight distribution

This paper describes the crest-factor analysis of carrier interferometry MC-CDMA and OFDM systems. The maximum crest factor of CF signals grows as log(N). Thus there appears to be an impressive reduction in the crest factor of CI signals. The crest factor performance for carrier interferometry systems is the same as in standard systems, in the sense that the statistical distributions of CF are identical. Hence, the carrier interferometry approach has no CF gain in the asymptotic regime. This analysis is advantageous in terms of BER and diversity.

The paper presents a new approach to the calculation of the crest-factor (CF) distribution in coded OFDM systems. The approach reveals an interesting connection between the weight distribution and the CF distribution of linear, binary codes that can be exploited to give an upper bound. The weight distribution of codes has attracted a great deal of attention in the past and many expressions and bounds are known. Thus we can expect to obtain a large number of bounds on the CF distribution. The upper bounds can serve as approximate curves for the CF distribution in the low probability region.

This article analyzes the crest-factor behavior of an orthogonal frequency-division multiplexing (OFDM) modem. The behavior is characterized in terms of the complementary distribution function of the crest-factor (CDFC). The CDFC is a key parameter of OFDM systems, for example it provides limits on achievable information rates. Several approximations have been developed in the literature so far, but no true upper bounds dependent on the different constellations that are used in OFDM modulation have been derived. A new approach is given providing upper bounds that substitute for time-consuming simulations.