Millimeter-wave (mmWave) and Terahertz (THz)-band communications hold great promise in meeting the growing data-rate demands of next-generation wireless networks, offering abundant bandwidth. To mitigate the severe path loss inherent to these high frequencies and reduce hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) systems with hybrid beamforming architectures can deliver substantial beamforming gains and enhanced spectral efficiency. However, accurate channel estimation (CE) in mmWave and THz UM-MIMO systems is challenging due to high channel dimensionality and compressed observations from a limited number of RF chains, while the hybrid near- and far-field radiation patterns, arising from large array apertures and high carrier frequencies, further complicate CE. Conventional compressive sensing based frameworks rely on predefined sparsifying matrices, which cannot faithfully capture the hybrid near-field and far-field channel structures, leading to degraded estimation performance. This paper introduces DiffPace, a diffusion-based plug-and-play method for channel estimation. DiffPace uses a diffusion model (DM) to capture the channel distribution based on the hybrid spherical and planar-wave (HPSM) model. By applying the plug-and-play approach, it leverages the DM as prior knowledge, improving CE accuracy. Moreover, DM performs inference by solving an ordinary differential equation, minimizing the number of required inference steps compared with stochastic sampling method. Experimental results show that DiffPace achieves competitive CE performance, attaining -15 dB normalized mean square error (NMSE) at a signal-to-noise ratio (SNR) of 10 dB, with 90% fewer inference steps compared to state-of-the-art schemes, simultaneously providing high estimation precision and enhanced computational efficiency.

The downlink signal of a paired carrier multiple access (PCMA) satellite system consists of two single-carrier signals that use the same time-frequency resources and hence appear as co-channel interference w.r.t. each other. Any blind receiver for such downlink signal requires knowledge of the various signal parameters in order to enable the detection of the data of both carriers e.g. by sequence estimation schemes. Assuming linear modulation and root-raised cosine pulses, this paper focuses on the estimation of the transmit filter roll-off factors of the respective carrier signals. Their knowledge is critical for blind PCMA receivers because distortions by suboptimal receiver input filtering besides the expected strong inter-carrier interference should be avoided. In contrast to single-carrier scenarios, prior works do not offer solutions for co-channel signals as present in the PCMA scenario. Here, we derive an approximate maximum-likelihood (ML) scheme for estimation of the roll-off factors. Furthermore, we propose an artificial neural network based scheme using a relatively compact architecture comprising only few convolutional and fully-connected layers which overcomes the practical limitations of the ML scheme. The neural network is trained on a data set of simulated PCMA received signals. We evaluate and compare the performance of the different approaches via numerical simulation results for varying system parameters.

Terahertz (THz) integrated sensing and communication (ISAC) systems promise ultra-high data rates and precise sensing capabilities, enabled by narrow and directional beams. However, such beams are susceptible to beam misalignment (BM) fading caused by platform instability. While statistical models for BM fading exist for communication links, their extension to sensing applications, especially in drone-to-drone (D2D) scenarios, remains unexplored. In D2D ISAC networks, misalignment effects from the aerial platforms impact both the forward and return paths. This paper presents closed-form statistical models for BM fading in THz ISAC, addressing both monostatic (co-located transceivers) and bistatic (separated transceivers) sensing configurations, and considering both the spot-filling regime (target larger than beam) and the spot-limited regime (target smaller than beam). The proposed models facilitate the extension of BM fading analysis to sensing in drone-to-drone THz ISAC systems and have been validated through Monte Carlo simulations.

The demand for high-speed, long-range wireless communication systems has been steadily increasing, driven by the growing need for 6G and beyond networks in applications such as industry 4.0, Metaverse, holographic type communication, digital twins, etc. TeraHertz (THz) band communication has emerged as a promising solution to address these needs, offering ample bandwidth and high data rates. To extend the limited coverage and to ensure reliable and low-latency connectivity, this paper explores routing strategies for multi-hop outdoor communication within the THz band, taking into account the unique wireless channel effects associated with this frequency range, including significant path loss, interference, and scattering. A realistic channel model is developed using a hybrid modeling approach that incorporates both the randomness of the wireless channels, i.e., positions of scattering objects in the environment, and the fixed locations of terminals. By combining simulations with real-world measurement data, the performance of multihop THz communication is assessed in terms of connectivity, delay, and capacity. To overcome the limitations of THz band communication and enable longer transmission distances, innovative routing strategies are proposed for multi-hop communication. Global and local routing algorithms are proposed that jointly optimize routing based on end-to-end latency and path capacity, benchmarking them against standard methods such as the shortest path algorithm and local search techniques. Link delays and capacities are assessed using the established channel model, which is then integrated into the routing algorithms. Our approach significantly improves network performance by taking into account the unique characteristics of the THz band in outdoor applications, resulting in a 30% increase in path capacity and a 70% reduction in path delay compared to the local search method in the scenarios studied.

Movable antennas (MAs) represent a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by dynamically adapting the positions of antenna elements within a designated transmit area. In particular, by employing electro-mechanical MA drivers such as stepper motors, the positions of the MA elements can be discretely adjusted to shape a favorable spatial correlation for improving system performance. Although preliminary research has explored beamforming designs for MA-enabled systems, the intricacies of the power consumption and the precise positioning of MA elements are not well understood, yet. Moreover, the assumption of perfect channel state information (CSI) adopted in the current literature is generally impractical due to the significant pilot overhead and the extensive time required for acquiring close-to-perfect CSI. To address these challenges, in this paper, we model the motion of MA elements through discrete steps and quantify the associated power consumption as a function of these movements. Furthermore, by leveraging the properties of the MA channel model, we introduce a novel CSI error model tailored for MA-enabled systems that facilitates robust resource allocation design. In particular, we jointly optimize the beamforming and the MA positions at the base station (BS) for minimization of the total BS power consumption, encompassing both radiated power and MA motion power, while guaranteeing a minimum required signal-to-interference-plus-noise ratio for each user. To this end, novel algorithms exploiting the branch and bound (BnB) method are developed to obtain the globally optimal solution for perfect and imperfect CSI, respectively. Moreover, to support practical real-time implementation, we propose low-complexity suboptimal algorithms with guaranteed convergence by leveraging successive convex approximation (SCA). Our numerical results validate the global optimality of the proposed BnB-based algorithms for both CSI scenarios. Furthermore, we unveil that both proposed SCA-based algorithms approach the optimal performance of the BnB-based algorithms within only a few iterations, thus highlighting their practical advantages. Additionally, we show that compared to the state-of-the-art approach, the proposed low-complexity SCA-based schemes achieve considerable performance gains, especially in high-load systems with a small number of antenna elements.

Integrated sensing and communication (ISAC) and ubiquitous connectivity are two usage scenarios of sixth generation (6G) networks. In this context, low earth orbit (LEO) satellite constellations, as an important component of 6G networks, is expected to provide ISAC services across the globe. In this paper, we propose a novel dual-function LEO satellite constellation framework that realizes information communication for multiple user equipments (UEs) and location sensing for interested target simultaneously with the same hardware and spectrum. In order to improve both information transmission rate and location sensing accuracy within limited wireless resources under dynamic environment, we design a multiple-satellite cooperative information communication and location sensing algorithm by jointly optimizing communication beamforming and sensing waveform according to the characteristics of LEO satellite constellation. Finally, extensive simulation results are presented to demonstrate the competitive performance of the proposed algorithms.

Intelligent reflecting surfaces (IRSs) are a promising low-cost solution for achieving high spectral and energy efficiency in future communication systems by enabling the customization of wireless propagation environments. Despite the plethora of research on resource allocation design for IRS-assisted multiuser wireless communication systems, the optimal design and the corresponding performance upper bound are still not fully understood. To bridge this gap in knowledge, in this paper, we investigate the optimal resource allocation design for IRS-assisted multiuser multiple-input single-output (MISO) systems employing practical discrete IRS phase shifters. In particular, we jointly optimize the beamforming vector at the base station (BS) and the discrete IRS phase shifts to minimize the total transmit power for the cases of perfect and imperfect channel state information (CSI) knowledge. To this end, two novel algorithms based on the generalized Benders decomposition (GBD) method are developed to obtain the globally optimal solution for perfect and imperfect CSI, respectively. Moreover, to facilitate practical implementation, we propose two corresponding low-complexity suboptimal algorithms with guaranteed convergence by capitalizing on successive convex approximation (SCA). In particular, for imperfect CSI, we adopt a bounded error model to characterize the CSI uncertainty and propose a new transformation to convexify the robust quality-of-service (QoS) constraints. Our numerical results confirm the optimality of the proposed GBD-based algorithms for the considered system for both perfect and imperfect CSI. Furthermore, we unveil that both proposed SCA-based algorithms can attain a locally optimal solution within a few iterations. Moreover, compared with the state-of-the-art solution based on alternating optimization (AO), the proposed low-complexity SCA-based schemes achieve a significant performance gain, especially for moderate-to-large numbers of IRS elements.

Massive multi-user multiple-input multiple-output (MU-MIMO) systems enable high spatial resolution, high spectral efficiency, and improved link reliability compared to traditional MIMO systems due to the large number of antenna elements deployed at the base station (BS). Nevertheless, conventional massive MU-MIMO BS transceiver designs rely on centralized linear precoding algorithms, which entail high interconnect data rates and a prohibitive complexity at the centralized baseband processing unit. In this paper, we consider an MU-MIMO system, where each user device is served with multiple independent data streams in the downlink. To address the aforementioned challenges, we propose a novel decentralized BS architecture, and develop a novel decentralized precoding algorithm based on eigen-zero-forcing (EZF). Our proposed approach relies on parallelizing the baseband processing tasks across multiple antenna clusters at the BS, while minimizing the interconnection requirements between the clusters, and is shown to closely approach the performance of centralized EZF.

Low earth orbit (LEO) satellite internet of things (IoT) is a promising way achieving global Internet of Everything, and thus has been widely recognized as an important component of sixth-generation (6G) wireless networks. Yet, due to high-speed movement of the LEO satellite, it is challenging to acquire timely channel state information (CSI) and design effective multibeam precoding for various IoT applications. To this end, this paper provides a deep learning (DL)-based joint channel prediction and multibeam precoding scheme under adverse environments, e.g., high Doppler shift, long propagation delay, and low satellite payload. Specifically, this paper first designs a DL-based channel prediction scheme by using convolutional neural networks (CNN) and long short term memory (LSTM), which predicts the CSI of current time slot according to that of previous time slots. With the predicted CSI, this paper designs a DL-based robust multibeam precoding scheme by using a channel augmentation method based on variational auto-encoder (VAE). Finally, extensive simulation results confirm the effectiveness and robustness of the proposed scheme in LEO satellite IoT.

Movable antennas (MAs) are a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by flexibly adapting the positions of the antenna elements within a given transmit area. In this paper, we model the motion of the MA elements as discrete movements and study the corresponding resource allocation problem for MA-enabled multiuser multiple-input single-output (MISO) communication systems. Specifically, we jointly optimize the beamforming and the MA positions at the base station (BS) for the minimization of the total transmit power while guaranteeing the minimum required signal-to-interference-plus-noise ratio (SINR) of each individual user. To obtain the globally optimal solution to the formulated resource allocation problem, we develop an iterative algorithm capitalizing on the generalized Bender's decomposition with guaranteed convergence. Our numerical results demonstrate that the proposed MA-enabled communication system can significantly reduce the BS transmit power and the number of antenna elements needed to achieve a desired performance compared to state-of-the-art techniques, such as antenna selection. Furthermore, we observe that refining the step size of the MA motion driver improves performance at the expense of a higher computational complexity.

High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also on the density and distribution of reference user locations used for fingerprinting.

Wireless high-accuracy positioning has recently attracted growing research interest due to diversified nature of applications such as industrial asset tracking, autonomous driving, process automation, and many more. However, obtaining a highly accurate location information is hampered by challenges due to the radio environment. A major source of error for time-based positioning methods is inaccurate time-of-arrival (ToA) or range estimation. Machine leaning (ML) techniques emerged as potential solutions to mitigate ToA-related errors. However, existing ML-based solutions either employ a set of features representing channel measurements only to a limited extent, or account for only device-specific proprietary methods of ToA estimation. In this paper, we propose a convolutional neural network (CNN) to estimate and mitigate the errors of a variety of ToA estimation methods utilizing channel impulse responses (CIRs). Based on real-world measurements from two independent campaigns, the proposed method yields significant improvements in ranging accuracy (up to 37%) of conventional ToA estimators, often eliminating the need of optimizing the underlying conventional methods.

High-accuracy positioning enables various applications such as industrial asset tracking, autonomous driving and process automation. Accurate location information relies on accurate time-of-arrival (ToA) or time-difference-of-arrival (TDoA) estimation in widely utilized time-based ranging. In this paper, we propose artificial neural network (ANN) based methods either to estimate T(D)oA directly or to mitigate the error of the conventional estimators. Based on real-world channel measurements, we show that the proposed direct ANN estimator outperforms the conventional estimators at least by approximately 37% and 24% in the 90th percentile ranging error, derived from ToA and TDoA estimations, respectively. Additionally, the proposed T(D)oA error-mitigating ANNs outperform the benchmark error mitigation methods with a gain varying between 17–43% in the 90th percentile ranging error, depending on the underlying conventional estimator. The ranging accuracy delivered by the direct estimation and error mitigation methods using ANNs are similar. Furthermore, the ANN estimators yield a more robust performance than the conventional estimators when the carrier frequency of the positioning signal is varied. ANN-based ToA estimation yields a marginally better ranging accuracy than ANN-based TDoA estimation. This advantage comes at the expense of a larger communication latency, while avoiding the need for synchronization among the positioning anchors.

To meet the ever-increasing requirements of high-rate data transmission, the significant amount of spectrum available in the TeraHertz (THz) band is considered for future wireless communications. However, the performance of THz communications is limited by strong phase noise (PN) introduced by oscillators and the complexity added by channel state information (CSI) acquisition. To overcome such impediments, we propose a new transmission concept based on constant-weight (CW) codes which enable low-complexity maximum-likelihood (ML) sequence detection at the receiver side in the presence of strong PN without requiring statistical or instantaneous CSI knowledge. In addition, the error rate of the ML receiver for the proposed CW codes is analyzed. Simulation results verify the analytical derivations and illustrate that the proposed transmission scheme outperforms transmission with on-off keying modulation and coherent detection.

Terahertz (THz) communications is envisaged as one of the key enablers for beyond fifth-generation (5G) wireless networks because of its massive bandwidth opportunities. However, wireless transmission at such high frequencies comes with its own challenges, including the increased power consumption and hardware complexity, the challenging task of channel estimation, as well as the strong impact of phase noise (PN). In this work, we consider coherent and noncoherent transmission techniques based on spatial modulation (SM) for the THz channel with PN impairments. SM schemes carry part of the information by the baseband modulated symbols and part by the used antenna or subarray index. Our investigations show that the index detection in the SM chain remains robust against the strong PN impairment, unlike the symbol detection which deteriorates as the modulation order increases. The robustness of index detection under PN coupled with low modulation order renders SM an attractive candidate for THz communications. Under strong PN, space-shift keying (SSK) schemes, i.e., SM schemes which solely transmit index information, are preferable.

For the design of a blind receiver for paired carrier multiple access (PCMA) signals in satellite communications, several detection schemes have been proposed that require knowledge of the channel. In a frequency-nonselective environ-ment, the overall channel is determined by the synchronization and transmit signal parameters of the respective carrier signals. In this paper, schemes for estimation of the carrier and symbol timing phases at the receiver are proposed, assuming that knowledge of the frequency offsets and symbol rate is obtained by previous estimations. Two different approaches for inferring the desired parameters are introduced. The proposed schemes exploit the cyclostationary nature of the respective carrier signals and the difference in their frequency offsets, and are based on estimates of the time-varying moments of the received signal and their cycle coefficients.

Location information is essential for a wide range of applications requiring, for example, highly accurate positioning information such as industrial automation, autonomous driving, inbound logistics and augmented reality. One of the major error sources in positioning is non-line-of-sight (NLOS) propagation while a line-of-sight (LOS) propagation is anticipated. Therefore, classifying positioning measurements as LOS or NLOS plays a key role to enable high accuracy positioning use cases.Existing solutions for the classification task either rely on availability of label or reference measurement data or do not consider effects of different scenarios or channel models. In this paper, we propose an unsupervised method for the classification task through a channel feature selection process to select only useful channel features to be used for the classification. Then, we show based on real-world data from several measurement campaigns that the proposed method outperforms the existing solutions both in classification performance and ranging accuracy.

With the rapid expansion of wireless technologies, monitoring and regulating the Radio Frequency (RF) spectrum usage becomes more important than ever. In this paper, we present a Deep Learning (DL) based approach to analyze the RF spectrum by detecting, localizing, and classifying active signals in RF frequency bands. We represent the radio signals in wideband spectrograms and formulate the signal detection and classification problem as an object detection task related to the computer vision field. To this end, You Only Look Once (YOLO), a state-of-the-art object detector, is adapted and optimized to detect, localize, and classify signals in spectrograms. For the experimental evaluation of YOLO as a signal detector, a rich dataset was simulated, consisting of diverse signals modulated with digital and analog modulation schemes and transmitted over channels with realistic propagation conditions. Our proposed method achieves an Average Precision (AP) of almost 87% and an average Intersection over Union (IoU) of 90%, thus demonstrating significant potential for analyzing RF spectral activity with high accuracy.

Many blind detection algorithms for paired carrier multiple access (PCMA) signals in satellite communications require information about the signal and channel parameters. A blind receiver needs to obtain knowledge of at least some of these prior to the symbol detection process. In this paper, schemes for the blind estimation of the symbol rates and carrier frequencies of the two overlapping co-channel signals in PCMA are proposed. The methods exploit the cyclic statistics of the PCMA signal for the estimation of the desired parameters. Effects of the mutual interference of the carriers in the estimation process are analyzed and the performance of the proposed methods is evaluated via numerical simulations.

Mobile communication systems need to adapt to temporally and spatially changing mobile network traffic, due to dynamic characteristics of mobile users, in order to provide high quality of service. Since these changes are not purely random, one can extract the deterministic portion and patterns from the observed network traffic to predict the future network traffic status. Such prediction can be utilized for a series of proactive network management procedures including coordinated beam management, beam activation/deactivation and load balancing. To this end, in this paper, an intelligent predictor using artificial neural networks is proposed and compared with a baseline scheme that uses linear prediction. It is shown that the neural network scheme outperforms the baseline scheme for relatively balanced data traffic between highly random and deterministic mobility patterns. For highly random or deterministic mobility patterns, the performance of the two considered schemes is similar to each other.

Low Power Wide Area (LPWA) networks are known to be highly vulnerable to external in-band interference in terms of packet collisions which may substantially degrade the system performance. In order to enhance the performance in such cases, the telegram splitting (TS) method has been proposed recently. This approach exploits the typical burstiness of the interference via forward error correction (FEC) and offers a substantial performance improvement compared to other methods for packet transmissions in LPWA networks. While it has been already demonstrated that the TS method benefits from knowledge on the current interference state at the receiver side, corresponding practical receiver algorithms of high performance are still missing. The modeling of the bursty interference via Markov chains leads to the optimal detector in terms of a-posteriori symbol error probability. However, this solution requires a high computational complexity, assumes an a-priori knowledge on the interference characteristics and lacks flexibility. We propose a further developed scheme with increased flexibility and introduce an approach to reduce its complexity while maintaining a close-to-optimum performance. In particular, the proposed low-complexity solution substantially outperforms existing practical methods in terms of packet error rate and therefore is highly beneficial for practical LPWA network scenarios.

Low PowerWide Area (LPWA) networks are known to be very vulnerable to external in-band interference in terms of packet collisions, which may substantially degrade the system performance. In order to improve the performance under collisions, the so-called telegram splitting (TS) method has been proposed recently. This approach is based on the assumption of a bursty behavior of the interference, which can be efficiently accounted for by forward error correction. In this paper, we assume that all interferers have the same structure in terms of packet duration and signal variance, which are assumed to be known to the receiver. However, the number of simultaneously active interfering transmissions and their scheduling are unknown. Hence, the amount of interference observed in each symbol interval can vary which needs to be accounted for by the receiver in order to increase the reliability of transmission. For this scenario, we develop the optimal signal detection strategy. We also show that the proposed method substantially outperforms a baseline scheme in terms of packet error rate.

Underground communication systems present a variety of new research challenges. Here, the goal is to establish an efficient wireless connection between the transceivers in the challenging underground medium. Typical applications for this type of communication systems include soil condition monitoring, earthquake prediction, communication in mines/tunnels, etc. These applications require a gathering of relevant information from multiple locations, which suggests the use of multiple sensor nodes that would be organized in wireless underground sensor networks (WUSNs). Due to the harsh propagation conditions in the soil medium (including rock, sand, and water sheds), traditional wireless signal propagation techniques using electromagnetic waves can only be applied for very short transmission ranges. In recent years, magnetic induction (MI)-based transmission has been proposed to overcome these issues. In this approach, induction coils are utilized as antennas in the transceivers in order to reduce the vulnerability of signal propagation to the soil properties, in particular the soil conductivity. Correspondingly, the design rules for optimum MI-WUSNs have been shown to substantially differ from the design rules for the traditional wireless communication systems due to unique properties of the transmission channel. In this survey paper, the recent advances in the area of MI-WUSNs are discussed, which range from signal transmission techniques and network design to wireless power transfer and localization. Also, new research challenges in this area are provided.

In this paper, we propose a robust minimum maximum mean square error Tomlinson-Harashima precoding (Min-Max-MSE THP) scheme and a low-complexity robust Min-Max-MSE hierarchical linear/THP (HL-THP) scheme for downlink massive multiuser multiple-input-multiple-output (MU-MIMO) systems with imperfect channel state information (CSI) at the transmitter. The proposed robust Min-Max-MSE HL-THP scheme comprises an inner linear beamformer (BF), which is designed based on second-order CSI statistics, and outer THP modules, which exploit the instantaneous overall CSI of the cascade of the actual channel and the inner BF. Thereby, the user terminals are divided into groups, where for each group a THP module successively mitigates the intra-group interference, whereas the inter-group interference is canceled by the inner BF. To ensure fairness, we adopt the maximization of the asymptotic signal-to-leakage-plus-noise ratio in the large system limit and the Min-Max-MSE as an optimization criterion for designing the inner BF and the per-group THP modules, respectively. Our analytical and simulation results show that the proposed robust Min-Max-MSE HL-THP scheme achieves a substantially improved performance in terms of the Max-MSE, maximum bit error rate, and minimum rate compared to linear regularized zero-forcing precoding. Moreover, the performance loss of the proposed robust Min-Max-MSE HL-THP scheme compared to the robust Min-Max-MSE THP scheme is small. In addition, our complexity analysis reveals that the proposed robust Min-Max-MSE HL-THP scheme has a much lower computational complexity than the Min-Max-MSE THP scheme. Hence, the robust Min-Max-MSE HL-THP scheme provides a favorable tradeoff between complexity and performance.

Given the prevalence of multicarrier waveforms in wireless systems, the sensing of such signals is of high importance for cognitive radio applications. In this work, we exploit periodic power boosting of subcarriers in multicarrier signals which is applied e.g. in LTE to improve the performance of sensing compared to state-of-the-art schemes under practical scenarios where noise uncertainties are present. First, we establish an analytical connection between the subcarrier power distribution and the autocorrelation function of a general multicarrier signal. Second, using this result, we demonstrate that periodic power boosting causes strong peaks in the magnitude of the autocorrelation function. The obtained analytical results match well with over-the-air measurement results corresponding to an orthogonal frequency division multiplexing (OFDM) signal of an LTE transmission. Here, the measurements indicate the presence of a boosting with a period of three subcarriers in the LTE signal which arises due to the cell-specific reference signals. Subsequently, a sensing algorithm exploiting the special features of the autocorrelation function resulting from periodic power boosting is developed which outperforms energy detection under realistic noise uncertainties and a state-of-the-art method that relies only on the cyclic prefix.

Wireless underground sensor networks (WUSNs) based on magnetic induction (MI) have been recently proposed as a promising candidate for underground networking. The benefit of the MI-WUSNs compared to other solutions (e.g. so-called Through-The-Earth communication) is related to the substantially lower path loss and lower vulnerability to the changes of the soil properties. In the past, some efforts have been made to characterize the signal transmission in MI-WUSNs. Those investigations, however, refer mostly to the information transmission. One of the target applications of the WUSNs is the object localization in the underground medium, which remains an open issue due to the complicated characteristics of the MI channels corrupted by the influence of soil. In this work, we propose a machine learning based solution for localization. In addition, a novel passive localization technique is introduced, which requires no signal from the target node and thus proves useful for rescue operations, where the battery of the node to be localized is either empty or damaged.

With the emergence of numerous novel dataintensive applications, the demand on fast wireless access is experiencing an unprecedented growth. Following this trend, the “Terabit era” is expected to become a reality in the near future. Terahertz technology is promising as an enabler due to its feature of an extremely high bandwidth. However, due to the high carrier frequency along with a high molecular absorption, the transmission distance is limited to a few meters only. In this work, the coverage problem and the achievable data rate performance of indoor THz wireless networks (THz-WNs) are investigated. In order to overcome the severe propagation loss and to improve the transmission range, a single frequency network (SFN) is advocated. The minimum individual user rate for different resource allocation schemes is analyzed, taking into account the effects of inter-symbol interference due to channel dispersion in the THz band and as a consequence of the SFN transmit protocol. Results demonstrate that the proposed SFN scheme is able to provide a high minimum achievable user data rate. The coverage probability increases from 25% when only a single access point (AP) is employed up to 95% when 20 APs are considered, for an output power of 1 W.

We propose a max-min multicell-aware regularized zero-forcing (MCA-RZF) precoding and power allocation scheme for downlink multicell massive multiple-input multiple-output systems. A general correlated channel model is considered, and the adopted channel state information (CSI) acquisition model includes the effects of estimation errors and pilot contamination. We use results from random matrix theory to derive deterministic equivalents for the proposed max-min power allocation in the large system limit, which solely depend on statistical CSI, but not on individual channel realizations. Our numerical results show that the proposed max-min MCA-RZF precoder achieves a substantially higher network-wide minimum rate than the MCA-RZF and the conventional RZF precoders with uniform power allocation, respectively, as well as the conventional RZF precoder with max-min power allocation.

Magnetic induction (MI)-based communication systems have gained increased attention in recent years. Typical applications for these systems lie in the area of wireless power transfer, near-field communication (NFC), and wireless sensor networks in challenging environments. In this paper, a system for simultaneous wireless information and power transfer (SWIPT) using MI-based signal transmission is designed for supporting one data stream and multiple parallel power streams. One of the possible applications for this scheme is an NFC-based access point. The overall system is optimized to guarantee a certain quality-of-service for the data stream as well as a maximum sum receive power for all power receivers (max-sum problem) or a maximum receive power for the worst power receiver (max-min problem), respectively. Both optimization problems turn out to be non-convex, such that the optimum solution cannot be found with limited computational complexity. Hence, we provide efficient suboptimal solutions. In this context, a convex approximation of the transmit power constraint in MI-based multiple-input multiple-output systems turns out to be very useful. A very high achievable power efficiency renders the proposed MI-based SWIPT system very promising.

We consider a two-way relaying system employing physical layer network coding in channels suffering from frequency-selective fading. We study decision-feedback equalization (DFE), a technique based on delayed decision-feedback sequence estimation (DDFSE), and Tomlinson-Harashima precoding (THP) approaches for mitigating the distortions introduced by the channel. For DFE, we introduce transmit filtering schemes that generate identical overall source-to-relay channel impulse responses for both source nodes, while achieving the maximum signal-to-noise ratio after equalization for the zero-forcing and the minimum mean-squared error criterion, respectively. The advocated DDFSE approach also relies on transmit filtering at the source nodes. For the THP scheme, we derive the optimal precoding filters that guarantee an intersymbol interference free source-to-relay transmission. In order to obtain a two-way relaying system, which uses the same equalization techniques in both transmission directions, we design a THP-based compromise precoding scheme for the relay-to-destination transmission. The performance of the proposed techniques is compared with that of benchmark schemes with simpler filtering/precoding, where identical overall source-to-relay channel impulse responses are enforced by straightforward but suboptimum choices for the transmit filters. Our results reveal that the developed schemes enable significant gains compared with the benchmark schemes.

In this paper, we propose a new framework for uplink-downlink duality in multi-cell multi-user multiple-input multiple-output systems suffering from residual hardware impairments (HWIs) at the base stations and the user terminals. We apply the proposed uplink-downlink duality framework to derive a multi-cell interference and HWI aware minimum mean square error (MCHA-MMSE) precoder which also takes channel state information estimation errors into account. We use results from random matrix theory to derive an analytical expression for the downlink power allocation for the proposed MCHA-MMSE precoder in the large system limit, which only depends on the channel's second order statistics. In contrast to the conventional MMSE precoder, the proposed MCHA-MMSE precoder takes inter-cell interference, pilot contamination, and residual HWIs into account and therefore achieves substantially higher sum rates. The proposed MCHA-MMSE precoder exploits statistical channel knowledge, but does not require data exchange between base stations via backhaul links. In order to reduce the computational complexity, the matrix inversion required for the computation of the MCHA-MMSE precoder is approximated by a matrix polynomial leading to a new polynomial-expansion MCHA-MMSE precoder. Using results from the random matrix theory, we derive closed-form expressions for the asymptotically optimal coefficients of the matrix polynomial, which only depend on the channel's second order statistics.

Body Area Sensor Networks (BASNs) are a challenging research area with applications in healthcare and entertainment. Due to the importance of the target applications in the daily life, BASNs are a promising candidate for being included into the future Internet of Things (IoT). In particular, the data gathering of the human activity may help customizing the services provided by the IoT and thus dramatically improve the IoT user experience. Magnetic Induction (MI) based communication is known in the context of wireless power transfer (WPT), near-field communication (NFC), and wireless sensor networks (WSNs) in challenging environments. In this approach, induction coils are utilized as antennas in the sensor nodes. Distributed beamforming is a well-known technique, that has been thoroughly investigated in the past. Here, the basic idea is to align the phases of signals from different sensor nodes in such a way that a virtual multiple-input multiple-output (MIMO) system with favorable properties is created. For example, this strategy may lead to an improved directionality of the transmitted signals and increase the achievable data rate. In this work, we analyze the potential of the distributed beamforming based MI-BASNs. We observe a significant increase of the achievable data rate for the proposed distributed beamforming compared to our selected baseline scheme.

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Magnetic induction (MI) based transmission has been proposed to overcome the very harsh propagation conditions in underground communications in recent years. In this approach, induction coils are utilized as antennas in the sensor nodes. This solution achieves larger transmission ranges compared to the traditional electromagnetic (EM) waves based approach. In the past, some efforts have been made to characterize the signal transmission in MI-WUSNs. Those investigations, however, refer mostly to the information transmission. One of the open issues, that may constrain the system design in some of the applications, is the powering of the individual sensor nodes. Due to the low accessibility of the nodes, a new method of wireless power transfer (WPT) for MI-WUSNs is proposed in this work. This method is mainly based on simultaneous signal transmissions from multiple sensor nodes with optimized signal constellations. Furthermore, the optimal scheduling for power transmission and reception is provided, which maximizes the energy efficiency of the network charging procedure. The proposed method is compared with the naive approach and shows a significant improvement of the system performance in terms of energy efficiency.

With the emergence of numerous novel data-intensive applications, the demand on fast wireless access for huge data file transfer and fast mobile data access is growing rapidly. Following this trend, the “Terabit era” is expected to become a reality in the near future. Terahertz (THz) technology is promising as an enabler due to its unique features, among others such as extremely high bandwidth, resistance to eavesdropping and minimal risk to human health. However, a number of technical hurdles need to be overcome to achieve such ultra-fast data rate of Terabit-per-second (Tbps) in the THz spectrum. In this work, a fundamental insight into the modulation scheme design is aimed to be established for THz communication systems. A strategy for transmission scheme selection and a corresponding efficient power allocation algorithm are proposed. Bounds on the maximum distance are determined for which data rates in the order of Tbps can be achieved. The results provide a fundamental insight into the selection of THz transmission schemes for given performance requirements.

In this paper, we provide a new framework for the uplink/downlink duality in single-cell massive multiple-input multiple-output (MIMO) systems suffering from residual hardware impairments (HWIs) at the base station and the user terminals. Using the proposed duality, complex downlink optimization problems can be converted to equivalent dual uplink problems, which are easier to solve. As an example, we apply the proposed uplink/downlink duality to derive an HWI aware minimum mean square error (HWIA-MMSE) precoder, which minimizes the sum mean square error under a sum power constraint in a single-cell massive MIMO system with residual HWIs. Thereby, we use results from random matrix theory to derive an asymptotic expression for the downlink power allocation for large numbers of antennas, which only depends on the channel statistics and not on the individual channel realizations. Analytical results for the asymptotic achievable sum rate of the proposed HWIA-MMSE precoder for a large number of BS antennas are also provided. Our simulation and analytical results show that the proposed HWIA-MMSE precoder achieves a higher sum rate than the conventional regularized zero-forcing precoder for moderately large numbers of base station antennas.

We explore the application of cognitive radio (CR) principles to 3GPP LTE-A. In such a system, a cognitive radio (CR), synchronized to the macro base station (BS), exploits the spectral holes in the orthogonal frequency division multiple access (OFDMA) time-frequency grid. Spectrum sharing between different network operators and device-to-device (D2D) communications in LTE-A are presented as potential applications. Sensing is expected to play a prominent role in such systems. We focus on simultaneous sensing and reception (SSR) that can avoid the overhead in sensing before transmission (SBT) protocols. Firstly, for a flat fading channel, we develop generalized likelihood ratio tests (GLRTs) based on the demodulation reference signals (DMRSs) of transmission mode 9 in LTE-A that do not require the knowledge of signal powers, channel statistics and noise parameters. Secondly, using a polynomial fitting, we extend the algorithm to frequency-selective fading scenarios. Simulation results are presented to compare the different algorithms which indicate that good performance can be achieved for SSR with full knowledge of the DMRSs.

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Magnetic induction (MI)-based transmission has been proposed to overcome the very harsh propagation conditions in underground communications in recent years. In this approach, induction coils are utilized as antennas in the sensor nodes. This solution achieves larger transmission ranges compared with the traditional electromagnetic wave-based approach. In the past, some efforts have been made to characterize the signal transmission in MI-WUSNs. Those investigations, however, mostly refer to the information transmission. One of the open issues that may constrain the system design in some of the applications is the powering of the individual sensor nodes. Due to the low accessibility of the nodes, a new method of wireless power transfer for MI-WUSNs is proposed in this paper. This method is mainly based on simultaneous signal transmissions from multiple sensor nodes with optimized signal constellations. Furthermore, the optimal scheduling for power transmission and reception is provided, which maximizes the energy efficiency of the network charging procedure. The proposed method is compared with the naive approaches and shows a significant improvement of the system performance in terms of energy efficiency.

In this paper, we address the problem of joint power and time allocation for secure communications in a decode-and-forward massive multiple-input multiple-output (M-MIMO) relaying system in the presence of a passive eavesdropper. We apply the M-MIMO relaying technique to enhance the secrecy performance under very practical and adverse conditions, i.e., no availability of instantaneous eavesdropper channel state information (CSI) and only imperfect instantaneous legitimate CSI. We first provide a performance analysis of secrecy outage capacity, which reveals the minimum required number of relay antennas for achieving a positive secrecy outage capacity. Then, we propose an optimization framework to jointly optimize source transmit power, relay transmit power, and transmission time in each hop, with the goal of maximizing the secrecy outage capacity. Although the secrecy outage capacity is not a concave function with respect to the optimization variables, we show that it can be maximized by first maximizing over some of the variables, and then maximizing over the rest. To this end, we first derive a closed-form solution of optimal relay transmit power, afterward obtain that of optimal source transmit power, and then derive the optimal ratio of the first-hop duration to a complete transmission time. Moreover, several important system design insights are provided through asymptotic performance analysis. Finally, simulation results validate the effectiveness of the proposed joint resource allocation scheme.

In-phase/quadrature-phase imbalance (IQI) is one of the most important hardware impairments in communication systems. It arises in the analogue parts of direct conversion transceivers and can cause severe performance losses. In this paper, IQI aware widely-linear (WL) channel estimation and data detection schemes for uplink multicell massive multiple-input multiple-output (MIMO) systems are proposed. The resulting receiver is a WL extension of the minimum mean-square-error (MMSE) receiver and jointly mitigates multiuser interference and IQI by processing the real and the imaginary parts of the received signal separately. Thereby, the IQI arising at both the base station and the user terminals is taken into account. The considered channel state information acquisition model includes the effect of pilot contamination, which is caused by the reuse of the same training sequences in neighbouring cells. We apply results from random matrix theory to derive analytical expressions for the asymptotic achievable sum rates of the proposed IQI aware and conventional IQI unaware (IQU) receivers in the large system limit. Our simulation and analytical results show that the performance of the proposed IQI aware WLMMSE (IQA-WLMMSE) receiver in a system with IQI is close to that of the MMSE receiver in an ideal system without IQI.

In this paper, we address the problem of optimal power allocation at the relay in two-hop secure communications under practical conditions. To guarantee secure communication during the long-distance transmission, the massive MIMO (M-MIMO) relaying techniques are explored to significantly enhance wireless security. The focus of this paper is on the analysis and design of optimal power assignment for a decode-and-forward (DF) M-MIMO relay, so as to maximize the secrecy outage capacity and minimize the interception probability, respectively. Our study reveals the condition for a nonnegative the secrecy outage capacity, obtains closed-form expressions for optimal power, and presents the asymptotic characteristics of secrecy performance. Finally, simulation results validate the effectiveness of the proposed schemes.

Magnetic induction (MI) based communication and power transfer systems have gained an increased attention in the recent years. Typical applications for these systems lie in the area of wireless charging, near-field communication, and wireless sensor networks. For an optimal system performance, the power efficiency needs to be maximized. Typically, this optimization refers to the impedance matching and tracking of the split-frequencies. However, an important role of magnitude and phase of the input signal has been mostly overlooked. Especially for the wireless power transfer systems with multiple transmitter coils, the optimization of the transmit signals can dramatically improve the power efficiency. In this work, we propose an iterative algorithm for the optimization of the transmit signals for a transmitter with three orthogonal coils and multiple single coil receivers. The proposed scheme significantly outperforms the traditional baseline algorithms in terms of power efficiency.

In this paper, we propose a multi-cell aware (MCA) detector for uplink multi-cell massive multiple-input multiple-output (MIMO) systems. The proposed detector exploits knowledge of the channel statistics but data exchange between different base stations over backhaul links is not required. A correlated channel model is considered and the adopted channel state information (CSI) acquisition model includes the effects of estimation errors and pilot contamination. In contrast to the conventional minimum mean square error (MMSE) detector, which mitigates only the multiple-access interference (MAI) in the target cell, the proposed detector takes the interference from neighboring cells and pilot contamination into account and therefore achieves substantially higher sum rates. Moreover, in order to reduce the computational complexity, the matrix inversion required for the MCA detector is approximated by a matrix polynomial leading to a new polynomial-expansion MCA (PEMCA) detector. Using results from random matrix theory, we derive closed-form expressions for the optimal coefficients of the matrix polynomial, which only depend on the channel statistics but not on the channel realizations. Our simulation results show that the PEMCA detector with only a few terms in the matrix polynomial achieves a considerably higher sum rate than the conventional MMSE detector while having a lower computational complexity.

The effective rate is an important performance metric of real-time applications in next generation wireless networks. In this paper, we present an analysis of the effective rate of multiple-input single-output (MISO) systems over α-μ fading channels under a maximum delay constraint. More specifically, novel and highly accurate closed-form approximate expressions of the effective rate are derived for such systems assuming the generalized α-μ channel model. In order to examine the impact of system and channel parameters on the effective rate, we also derive closed-form expressions of the effective rate in asymptotically high and low signal-to-noise ratio (SNR) regimes. Furthermore, connections between our derived results and existing results from the literature are revealed for the sake of completeness. Our results demonstrate that the effective rate is a monotonically increasing function of channel fading parameters α and μ, as well as the number of transmit antennas, while it decreases to zero when the delay constraint becomes stringent.

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Magnetic induction (MI) based transmission has been proposed to overcome the very harsh propagation conditions in underground communications in recent years. In this approach, induction coils are utilized as antennas in the sensor nodes. This solution achieves longer transmission ranges compared to the traditional electromagnetic (EM) waves based approach. Furthermore, a passive relaying technique has been proposed in the literature where additional resonant circuits are deployed between the nodes. However, this solution is shown to provide only a limited performance improvement under practical system design contraints. In this work, the potential of an active relay device is investigated which may improve the performance of the system by combining the benefits of the traditional wireless relaying and the MI based signal transmission.

Linear pilot-aided channel estimation in orthogonal-frequency division multiplexing (OFDM) systems is carried out optimally via 2D Wiener filters. The standard approach employed in conventional receivers is based on cascaded 2×1D Wiener filtering, which has a lower complexity but also suboptimal performance. The 2D+1D filtering scheme proposed in this paper combines the benefits of the two other schemes and is not affected by some restrictions of 2×1D filtering as shown by theoretical analysis of such schemes. For comparison, the mean-squared error (MSE) performance of the different channel estimation schemes is evaluated. Based on this analysis, results from full link-level simulations w.r.t. the digital video broadcasting standard for satellite services to handheld devices (DVB-SH) are presented applying adaptive channel estimation algorithms instead of the Wiener filters. The novel 2D+1D scheme shows enhanced bit error rate (BER) performance since it provides more accurate channel estimates in high Doppler channel conditions compared to the conventional cascaded 2×1D schemes.

Spatial suppression of interference using interference rejection combining (IRC) is an important feature considered in LTE-A systems for improving the reception performance of users, especially at the cell-edges. The efficiency of interference suppression depends on the accuracy of the channel estimation and the spatial covariance estimation. In this work, we first analyze two different approaches of covariance estimation, namely data based covariance estimation and reference signal based covariance estimation. It is shown that the reference signal based covariance estimation is superior, even for a block flat fading channel and the reasons behind this behavior are investigated. Secondly, we propose an iterative IRC receiver with soft iterative channel estimation and covariance estimation for highly frequency-selective channels. The performance results demonstrate that the iterative IRC receiver can provide a significant gain.

The objective of Wireless Underground Sensor Networks (WUSNs) is to establish an efficient wireless communication in the underground medium. A magnetic induction (MI)-based signal transmission scheme has been proposed to overcome the very harsh propagation conditions in WUSNs. Due to a much lower vulnerability to the environmental changes, the MI technique has been shown to improve the system performance in terms of achievable data rates and coverage compared to the traditional EM wave based transmission. Two different approaches are known from the literature: direct MI transmission and MI waveguides, where many resonant relay circuits are deployed in the latter between the two nodes to be connected. In this work, digital transmission schemes are investigated for MI-WUSNs employing these two approaches. The influence of transmission parameters like symbol duration and modulation scheme are studied and new methods for their optimization are proposed. In this context, significant gains can be achieved compared to the naive straightforward approaches.

Different schemes for single antenna interference cancellation (SAIC) in the Global System for Mobile Communications (GSM) have been proposed so far. Most of these schemes work best in synchronous interference environments and are limited to Gaussian minimum-shift keying (GMSK) modulation. In this paper, we propose several modifications of the conventional GSM receiver, which target the harmful effects of asynchronous co-channel interference (ACCI). Based on the observation that the impairment caused by ACCI cannot be modeled as additive Gaussian noise, which was assumed for the derivation of the metric used in state-of-the-art receivers, we propose modeling the impairment as Generalized Gaussian noise (GGN). This leads to modifications of the conventional branch metrics of the reduced-state equalizer and the decoder employed at the receiver. Both proposed modifications are applicable to any linear modulation alphabet and do not require any modification of the GSM air interface. Hence, the proposed novel branch metrics are also applicable in Enhanced Data Rates for GSM Evolution (EDGE) systems which employ 8-ary phase-shift keying (8-PSK) modulation and where the SAIC algorithms tailored for GMSK cannot be used. Furthermore, we show that the proposed branch metric modifications also improve the performance of the new Voice Services over Adaptive Multiuser channels on One Slot (VAMOS) extension of GSM in ACCI environments. Based on simulation results, the performance of the proposed schemes is compared with that of an unmodified receiver employing the conventional Gaussian metric. We show that the proposed receivers outperform the unmodified receiver in ACCI environments and discuss the complexity entailed by the proposed branch metric modifications.

Wireless underground sensor networks (WUSNs) are an emerging and promising research area. The aim of WUSNs is to establish an efficient wireless communication in the underground medium. A magnetic induction (MI)-based waveguide technique has been proposed to overcome the very harsh propagation conditions in WUSNs. In this approach, several resonant relay circuits are deployed between the two nodes to be connected. This technique allows for an extension of the transmission range. In this work, we investigate digital transmission schemes for MI-WUSNs. We analyze the influence of transmission parameters like symbol duration and modulation scheme and propose methods for their optimization.

In this work we present a case for dynamic spectrum sharing between different operators in systems with carrier aggregation (CA) which is an important feature in 3GPP LTE-A systems. Cross-carrier scheduling and sensing are identified as key enablers for such spectrum sharing in LTE-A. Sensing is classified as Type 1 sensing and Type 2 sensing and the role of each in the system operation is discussed. The more challenging Type 2 sensing which involves sensing the interfering signal in the presence of a desired signal is studied for a single-input singleoutput system. Energy detection and the most powerful test are formulated. The probability of false alarm and of detection are analyzed for energy detectors. Performance evaluations show that reasonable sensing performance can be achieved with the use of channel state information, making such sensing practically viable.

In this paper, we consider joint user pairing and frequency allocation with beamforming (BF) assuming per user Quality of Service (QoS) requirements. A single-carrier frequency-division multiple-access (SC-FDMA) transmission employing a zero-forcing (ZF) detector at the receiver is assumed. The BF filter coefficients are jointly optimized with the user pairing and frequency allocation of the users. The transmit power savings achievable by combining BF with user pairing and frequency allocation are compared to these of a constant power allocation over all subcarriers. To this end, BF filter coefficient optimization and constant power allocation are briefly revisited and novel BF filter solutions for single-input multiple-output (SIMO) and single-input single-output (SISO) transmission, respectively, are derived for SC-FDMA. Numerical results show that the transmit power can be significantly reduced with BF for suboptimum algorithms, but for optimal joint user pairing and frequency allocation the gain of BF compared to constant power allocation is negligible.

Wireless underground sensor networks (WUSNs) present a variety of new research challenges. Recently, a magnetoinductive (MI) waveguide technique has been proposed to cope with the very harsh propagation conditions in WUSNs. This relay-based approach allows for an extension of the transmission range, which can be quite limited if relays are not deployed. In this paper, tree-based WUSNs are considered. The objective of our work is to determine the optimal system parameters, topology, and deployment strategy in order to avoid bottlenecks in the system and achieve optimal network throughput. We compare two different deployment schemes: MI waveguides and direct MI transmission (no relays deployed) based connections between sensors. The two schemes are different in nature and propagation characteristics. Therefore, different optimization techniques are utilized. The optimal set of system parameters is chosen to maximize the channel capacity of the worst link and therefore optimize the available data rate. The bottleneck throughput of the direct MI transmission based network can be then compared with the respective results of the MI waveguides based network. In several cases, we observe a better performance of the direct MI transmission based networks.

Voice services over Adaptive Multi-user channels on One Slot (VAMOS) has been standardized as an extension to the Global System for Mobile Communications (GSM). The aim of VAMOS is to increase the capacity of GSM, while maintaining backward compatibility with the legacy system. To this end, the Orthogonal Sub-channels (OSC) concept is employed, where two Gaussian minimum-shift keying (GMSK) signals are transmitted in the same time slot and with the same carrier frequency. To fully exploit the possible capacity gain of OSC, new receiver concepts are necessary. In contrast to the base station, where multiple antennas can be employed, the mobile station is typically equipped with only one receive antenna. Therefore, the downlink receiver design is a very challenging task. Different concepts for channel estimation, user separation, and equalization at the receiver of an OSC downlink transmission are introduced in this paper. Furthermore, the system capacity must be improved by suitable downlink power and resource allocation algorithms. Making realistic assumptions on the information available at the base station, an algorithm for joint power and radio resource allocation is proposed. Simulation results show the excellent performance of the proposed channel estimation algorithms, equalization schemes, and joint radio resource and power allocation algorithms in realistic VAMOS environments.

Wireless Underground Sensor Networks (WUSNs) present a variety of new research challenges. Recently, a magneto-inductive (MI) waveguide technique has been proposed to cope with the very harsh propagation conditions in WUSNs. This approach allows for an extension of the transmission range, which can be quite limited if relays are not deployed. In this paper, tree-based WUSNs are considered with sensors connected via MI-waveguides. The objective of our work is to determine the optimal system parameters and topology in order to avoid bottlenecks in the system and achieve optimal network throughput.

In this work, we present a low-complexity linear precoding scheme for downlink large-scale multiple-input multiple-output (MIMO) systems. The proposed scheme can achieve near minimum mean square error (MMSE) precoding performance in terms of the sum rate and is based on a matrix polynomial instead of matrix inversion. Simulation results show that matrix polynomials consisting of only a few terms are sufficient to closely approach the sum rate of the classical MMSE precoder and to perform orders of magnitude better than the simple conjugate beamforming (BF) precoder. We derive exact expressions for the computational complexity of the proposed scheme in terms of the number of additions and multiplications and compare it to the complexity of the BF and MMSE precoders. Our complexity analysis shows that for large number of base station antennas N compared to the number of generated transmit symbols τ per channel estimate and large number of users K, the proposed polynomial precoder has a lower complexity than the classical MMSE precoder.

Wireless Underground Sensor Networks (WUSNs) present a variety of new research challenges. Recently a magneto-inductive (MI) waveguide technique has been proposed to overcome the very harsh propagation conditions in WUSNs. In this approach, several resonant relay circuits are deployed between the two nodes to be connected. This technique allows for an extension of the transmission range, which can be quite limited, if relays are not deployed. In this paper, channel and noise models for MI-WUSNs using MI-waveguides are developed. Results of a numerical evaluation of the channel capacity under practical constraints are provided and the influence of the system parameters on the performance is discussed.

Magnetic Induction (MI) techniques enable efficient wireless communications in dense media with high material absorptions, such as underground soil medium and oil reservoirs. A wide range of novel and important applications in such RF-challenged environments can be realized based on the MI communication mechanism. Despite the potential advantages, the major bottleneck of the MI communication is the limited channel capacity due to the low MI bandwidth. In this paper, the Spread Resonance (RS) strategy is developed for the MI communication in RF-challenged environments which greatly increases the MI channel capacity. Specifically, instead of using the same resonant frequency for all the MI coils, the spread resonance strategy allocates different resonant frequencies for different MI relay and transceiver coils. An optimization solution for the resonant frequency allocation is formulated to maximize the MI channel capacity which captures multiple unique MI effects, including the parasitic capacitor in each MI coil, the Eddy currents in various transmission media with limited conductivities, and the random direction of each coil. Numerical evaluations are provided to validate the significant channel capacity improvements by the proposed SR strategy for MI communication systems.

In this paper, we consider two-way relaying in a physical layer network coded system in a scenario where transmissions are subject to frequency-selective fading. Physical Layer Network Coding (PNC) enabled single-carrier transmission systems operating in flat fading environments have been subjected to scrutiny extensively, yet approaches for tackling the problem of extending them to environments causing multi-path fading are quite limited. We present three equalization schemes that can be used at the relay nodes to retrieve the data to be relayed in the presence of intersymbol interference (ISI). The most basic scheme jointly estimates both data streams and combines them afterwards. Furthermore, a scheme that already combines state and transition probabilities inside the trellis diagram is devised. Finally, we present a complexity-reduced algorithm where state combinations that yield the same bit to be relayed can be combined into a single state. All of the three algorithms can be regarded as different types of a decode-and-forward (DF) scheme.

We consider prefiltering for a single-carrier transmission over frequency-selective channels, where reduced-state trellis-based equalization is employed at the receiver, such as delayed decision-feedback sequence estimation (DDFSE) or reduced-state sequence estimation (RSSE). While previously proposed prefiltering schemes are based on the optimum filters of decision-feedback equalization (DFE), the prefiltering scheme introduced in this paper is designed according to a signal- to-interference-plus-noise ratio (SINR), whose definition takes into account explicitely the subsequent trellis-based equalizer and its complexity. In addition to the prefilter, a finite-length target impulse response for DDFSE/RSSE and an infinite- length feedback filter for state-dependent decision feedback in DDFSE/RSSE, respectively, is optimized. The developed solutions lend themselves to an interpretation of the tasks of the optimum filters. The presented numerical results show that noticeable gains can be achieved compared to state-of-the-art prefilters.

In this paper, we consider filter-and-forward beamforming (FF-BF) for two-way relay networks employing single-carrier transmission over frequency-selective channels. In FF-BF, the relay nodes filter the received signal using finite impulse response (FIR) or infinite impulse response (IIR) filters. For the processing at the transceivers, we investigate two different cases: (1) simple slicing without equalization and (2) linear equalization. For both cases, we optimize FF-BF filters for maximization of the minimum transceiver signal-to-interference-plus-noise ratio (SINR) subject to a relay transmit power constraint. For case (1), we show that the optimization problem for FIR filters can be transformed into a convex second-order cone programming problem, which can be efficiently solved using standard tools. For case (2), leveraging results from IIR FF-BF for one-way relaying, we establish an upper and an achievable lower bound for the max-min problem. Since the gap between the upper and the lower bound is small, a close-to-optimal solution is obtained. Our simulation results reveal that the performance of FF-BF without equalization at the transceivers crucially depends on the slicer decision delay and transceivers with slicers can closely approach the performance of transceivers with equalizers provided that the FIR FF-BF filters are sufficiently long.

This paper proposes a strategy that combines user pairing and spectrum allocation for a virtual multiple-input multiple-output (V-MIMO) single-carrier frequency-division multiple access (SC-FDMA) transmission over intersymbol interference (ISI) channels. The users are equipped with a single antenna each, where always two users, composing one pair, transmit their data in the same time slot and frequency band. The receiver at the base station (BS) is assumed to have multiple antennas and separates the users of each pair with a multiuser equalizer. For this, linear minimum mean-squared error (MMSE) equalization and successive interference cancellation (SIC), respectively, are chosen. A solution for the combined optimization of user pairing and spectrum allocation is proposed and evaluated by simulations for a transmission in the uplink of E-UTRA Long Term Evolution (LTE). The simulation results show that the proposed algorithm yields significant gains compared to random user pairing and spectrum allocation. It is also shown that, despite its considerably lower complexity, the performance of the novel algorithm is very close to that of the optimum solution that can only be found by a very complex full search.

Different approaches for single antenna interference cancellation (SAIC) in GSM/EDGE have been proposed. Most of them work best in synchronous interference environments. We propose three different modifications of the conventional EDGE receiver, which target the harmful effects of asynchronous co-channel interference (ACCI). All modifications are applicable to all linear modulation alphabets and do not require any modification of the GSM/EDGE air interface. We model the effect of ACCI as impulsive Generalized Gaussian noise. The proposed methods include a modification of the conventional path metrics for the reduced-state equalizer and the decoder, respectively, as well as a Viterbi algorithm, which performs erasure decoding. Based on simulation results, the performances of the proposed schemes are compared with that of a standard GSM/EDGE receiver. We show that all three proposed schemes outperform the standard receiver in ACCI environments and discuss their complexity.

Voice over Adaptive Multi-user Channels on One Slot (VAMOS) is an extension of the Global System for Mobile Communications (GSM) standard, where two overlaid Gaussian minimum-shift keying (GMSK) signals are transmitted in the same time slot and on the same frequency. The overlaid signals are usually assigned different powers to combat slow fading and propagation loss. For channel estimation, specific training sequences are used for both signals. In the downlink, at each mobile station the channel coefficients and the sub-channel power imbalance ratio (SCPIR) have to be estimated. In this paper, the Cramer-Rao lower bound (CRB) for the training sequence based joint estimation of the SCPIR and the channel coefficients at the mobile station is derived and compared with the CRB for a conventional GSM transmission. The results are compared with the performance of channel estimation algorithms designed for VAMOS. It turns out that these algorithms perform very close to the CRB.

In this paper, we evaluate a multilevel coding (MLC) scheme with multistage decoding (MSD) designed for satellite broadcasting communications. The impact of three different satellite system impairments on the decoding performance is analyzed. First, the influence of errors introduced by the channel estimation is discussed, assuming a typical data-aided (DA) channel estimator with different pilot lengths. Second, the impact of the residual phase noise present after the phase recovery is investigated using a model based on a normal distribution. Finally, the degradation introduced by the non-linearities of the satellite power amplifiers is also analyzed. The impact of these effects is investigated via the mutual information. Besides, bit error rate (BER) simulations are performed for each impairment effect. The considered MLC scheme is compared to a classical bit-interleaved coded modulation (BICM) scheme, showing that the MLC scheme provides different grades of robustness for each level.

In this work basic co-channel interference suppression techniques for SC-FDMA transmission are evaluated. As there seems to be only limited prior work on this topic, this work aims to provide a reference for more sophisticated suppression approaches still to be developed. Hereby, for the filtering the minimum mean-squared error (MMSE) criterion is considered. For the case of incomplete knowledge of the statistics of the interferers, a simple modified MMSE approach is evaluated, where these interferers are modeled as additive white Gaussian noise (AWGN). However, simulation results show that assuming co-channel interference as AWGN leads to a severe performance degradation. As an alternative an adaptive approach is presented, using the method of least squares (LS). Without any knowledge of the co-channel interferers the filter coefficients are adapted to current interference conditions exploiting a known reference signal. A refinement of the filter can be achieved by a feedback of already detected data in adaption, which further improves the performance. Although frequency domain equalization is common in SC-FDMA systems, the design of the LS filter is done in time domain, as for reduced filter length this cannot be done in frequency domain. Therefore, some general considerations which are important for time domain filtering in SC-FDMA systems are pointed out.

In order to reduce the computational complexity of maximum-likelihood symbol estimation (MLSE) demapping of multilevel codes which is based on block partitioning and which produces soft input for a multistage decoding (MSD) process, three different demapping algorithms are proposed. It is theoretically proven that the proposed algorithms can reduce exponentially increasing computational complexity of the MLSE demapping algorithm to a constant complexity (neglecting comparisons). It is shown by extensive simulations for AWGN and Rayleigh fading channels that the proposed low complexity demapping algorithms can achieve near MLSE performance.

Most of the existing literature on cooperative relay networks has focused on frequency-nonselective channels or frequency-selective channels with multi-carrier transmission. However, several practical systems employ single-carrier transmission over frequency-selective channels and the design of corresponding relaying schemes is a largely under-explored topic. In this paper, we investigate filter-and-forward beamforming (FF-BF) for relay networks employing single-carrier transmission over frequency-selective channels. In contrast to prior work, we assume that the destination node is equipped with a simple linear or decision feedback equalizer. The FF-BF filters at the relays are optimized for maximization of the signal-to-noise ratio at the equalizer output under a joint relay power constraint. For infinite impulse response (IIR) FF-BF filters, we derive a unified expression for the filter frequency response valid for linear equalization, decision feedback equalization, and an idealized matched filter receiver. A numerical algorithm with guaranteed convergence is developed for optimization of the power allocation factor included in the expression for the IIR FF-BF filter frequency response. We also provide an efficient gradient algorithm for recursive calculation of near-optimal finite impulse response (FIR) FF-BF filters. Simulation results show that, in general, short FIR FF-BF filters are sufficient to closely approach the performance of IIR FF-BF filters even in severely frequency-selective channels and that the proposed FF-BF scheme with equalization at the destination achieves substantial performance gains compared to a previously proposed FF-BF scheme without equalization.

In this paper, we consider filter-and-forward beamforming (FF-BF) for two-way relay networks employing single-carrier transmission over frequency-selective channels. In FF-BF, the relay nodes filter the received signal using finite impulse response (FIR) or infinite impulse response (IIR) filters. For the processing at the transceivers, we investigate two different cases: (1) simple slicing without equalization and (2) linear equalization (LE) or decision-feedback equalization (DFE). For the first case, we optimize FIR FF-BF filters, respectively, for maximization of the minimum transceiver signal-to-interference-plus-noise ratio (SINR) subject to a relay transmit power constraint and for minimization of the total relay transmit power subject to two quality of service (QoS) constraints. We show that both problems can be transformed into a convex second-order cone programming (SOCP) problem, which can be efficiently solved using standard tools. For the second case, we optimize IIR and FIR FF-BF filters for max-min optimization of the SINR, and for transceivers with zero-forcing LE, also for minimization of the sum mean-squared error (MSE) at the equalizer outputs of both transceivers. Leveraging results from FF-BF for one-way relaying, we establish an upper and an achievable lower bound for the max-min problem and an exact solution for the sum MSE problem. Since the gap between the upper and the lower bound for the max-min problem is small, a close-to-optimal solution is obtained. Our simulation results reveal that the performance of FF-BF without equalization at the transceivers crucially depends on the slicer decision delay and transceivers with slicers can closely approach the performance of transceivers with equalizers provided that the FF-BF filters are sufficiently long and a sufficient number of relays is deployed.

We consider transmit beamforming for single-carrier frequency-division multiple access (SC-FDMA) transmission over frequency-selective multiple-input multiple-output (MIMO) channels. The beamforming filter is optimized for minimization of the sum of the mean-squared errors (MMSEs) of the transmitted data streams after equalization. Here, MIMO minimum mean-squared error linear equalization (MMSE-LE) is considered. We prove that for SC-FDMA transmission eigen-beamforming diagonalizing the overall channel together with a nonuniform power distribution is the optimum beamforming strategy in the MMSE sense. The derived optimum power allocation is similar in spirit to classical results for the optimum continuous-time transmit filter for linear modulation formats obtained by Berger/Tufts and Yang/Roy. Moreover, we present a modification of the beamformer design to mitigate an increase of the peak-to-average power ratio (PAPR) which is in general associated with beamforming. Simulation results demonstrate the excellent performance of the proposed beamforming algorithm.

In this paper, we investigate filter-and-forward beamforming (FF-BF) for relay networks employing single-carrier transmission over frequency-selective channels. In contrast to prior work, we assume that the destination node is equipped with a simple linear equalizer. The FF-BF filters at the relays are optimized for maximization of the signal-to-noise ratio at the equalizer output under a joint relay power constraint. For infinite impulse response (IIR) FF-BF filters, we derive a closed-form expression for the filter frequency response, and a numerical algorithm with guaranteed convergence is developed for optimization of the power allocation factor included in the expression. We also provide an efficient gradient algorithm for recursive calculation of near-optimal finite impulse response (FIR) FF-BF filters. Simulation results show that, in general, short FIR FF-BF filters are sufficient to closely approach the performance of IIR FF-BF filters even in severely frequency-selective channels and that the proposed FF-BF scheme with equalization at the destination achieves substantial performance gains compared to a previously proposed FF-BF scheme without equalization.

For pilot-aided channel estimation in orthogonal frequency-division multiplexing (OFDM) systems, adaptive algorithms enable tracking of a time-varying channel. Using several subcarriers for the update step in the adaptive algorithm, i.e., performing block processing, improves the robustness and convergence speed of the adaptation. To decrease the complexity of these algorithms, simplified versions are deduced as well as versions with real-valued filters. In the latter case the number of required arithmetic operations can be reduced significantly not only in the adaptive algorithm but also in the filtering process itself, which represents a dominating part regarding the complexity due to the huge amount of subcarriers of practical OFDM transmission schemes. Simulation results show that gains w.r.t. the convergence behavior can be achieved by real-valued filters compared to complex-valued filters - even in scenarios where real-valued filters are theoretically suboptimum.

We introduce the use of multidimensional (MD) constellations in a multilevel coding (MLC) scheme with multi-stage decoding (MSD) designed for broadcasting communications, where services with different quality of service (QoS) are desirable. We show that the number of different protection levels increases when using MD constellations. Besides, the appropriate block labeling (BL) partitioning for an MD constellation is found by applying the binary switching algorithm (BSA) as an efficient search algorithm. Two cost functions for the BSA are proposed based on the BL criterion. Some methods to construct an appropriate MD constellation are presented, including the use of uniform and non-uniform component constellations. The viability of the proposed MD constellations approach for broadcasting with different protection levels is evaluated analyzing the mutual information (MI) of each level. Finally, we present a comparison between a unidimensional and a multidimensional scheme which again demonstrates the benefits of the proposed scheme.

In this paper, we evaluate a multilevel coding (MLC) scheme with multi-stage decoding (MSD) designed for satellite broadcasting communications, where services with different quality of service (QoS) are desirable. A simple Land-Mobile-Satellite (LMS) channel model is presented, based on channel states and their transitions. The instantaneous channel capacity for suburban and forest scenarios is calculated using the mutual information concept. An established transmission scheme for satellite broadcasting, which uses bit-interleaved coded modulation (BICM) with Gray mapping, is compared with the MLC scheme in terms of channel capacity and outage probability. The effect of removing a long channel interleaver in the physical layer is analyzed, showing that the MLC scheme is a good alternative to BICM for satellite broadcasting.

In this paper, we introduce robust cyclic space- frequency (CSF) filtering for systems combining bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM). The proposed robust CSF filtering scheme exploits outdated channel state information at the transmitter (CSIT) and takes into account the reliability of the CSIT via a Bayesian model. Based on an upper bound on the worst-case pairwise error probability we formulate the optimization problem for the robust CSF filters which can be solved exactly for certain special cases. For the general case, we obtain an approximate solution by solving a related problem with additional constraints. This approximate solution can be further improved with a gradient algorithm for the original problem. Simulation results confirm the excellent performance of BICM-OFDM with robust CSF filtering.

In this paper, we propose a multilevel coding (MLC) scheme suitable for satellite communications, where different QoS levels are required. We introduce a novel characterization of schemes based on mutual information, called multi-stage decoder (MSD) charts, to aid in the design and evaluation of multilevel coding systems. This characterization method finds the optimal set of code rates for the MLC scheme for a set of required SNR operation points. On the other hand, fixing the code rates to find out the operation points of SNR is also possible. Performance of common mapping strategies used in MLC schemes, as block labeling and Ungerbock labeling, are evaluated using MSD charts. An established transmission scheme for satellite broadcasting, European Satellite Digital Radio (ESDR) standard, is compared with the proposed MLC scheme, showing that the MLC scheme is able to work in lower SNR regions and provides more flexibility in designing the levels.

Currently, Multiple Users Reusing One Slot (MUROS) is discussed in 3GPP GERAN as an extension of the GSM standard. In MUROS, two overlaid GMSK signals are transmitted in the same time slot and at the same frequency resource. By this, capacity of existing GSM networks in principle can be doubled and up to four half rate voice users can share one time slot. In this paper, channel estimation and detection is investigated for the MUROS downlink. Two novel channel estimation algorithms are presented, taking into account the specifics of the MUROS downlink. For detection, a joint MLSE of both user signals can be applied in case of noise limited scenarios. For interference limited environments, it turns out that approaches based on the mono interference cancellation (MIC) algorithm for single antenna interference cancellation (SAIC) are more favorable. It is shown that the standard MIC algorithm performs sufficiently well and could be used for a fast introduction of MUROS in existing GSM networks. For enhanced performance, a novel algorithm based on MIC along with successive interference cancellation is proposed. The presented results demonstrate that an even better performance as the GSM reference performance before introduction of SAIC can be obtained for MUROS if well-designed receivers are used.

Transmit beamforming (BF) and receive combining are simple and popular methods for performance enhancement in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In this paper, we propose a novel single-data stream, time-domain BF (TD-BF) scheme for MIMO-OFDM systems which uses cyclic BF filters (C- BFFs). Assuming perfect channel state information (CSI) at the transmitter, the C-BFFs are optimized for two different criteria, namely, maximum average mutual information (AMI) per sub-carrier and minimum average uncoded bit error rate (BER). If the C-BFF lengthLgis equal to the number of sub-carriers Nc,closed-form solutions to both optimization problems exist. For the practically relevant caseLg< Ncwe present numerical methods for calculation of the optimum C-BFFs for both criteria. Using a global vector quantization (GVQ) approach the C-BFFs are quantized for practical finite-rate feedback channels. Simulation results for typical IEEE 802.11 n channels confirm the excellent performance of the proposed scheme and show that TD-BF has a more favorable performance/feedback rate trade-off than previously proposed frequency-domain BF (FD-BF) schemes.

In this paper, we introduce robust cyclic space-frequency (CSF) filtering for systems combining bit-interleaved coded modulation (BICM) and orthogonal frequency division multiplexing (OFDM). The proposed robust CSF filtering scheme exploits imperfect channel state information at the transmitter (CSIT) and takes into account the reliability of the CSIT via a Bayesian model. To further improve robustness, we combine CSF filtering with orthogonal space-time block coding in the frequency domain and refer to the resulting scheme as SFC-CSF filtering. We also propose a linear prediction method for improving the quality of the CSIT via post-processing. Based on an upper bound on the worst-case pairwise error probability we formulate an optimization problem for the robust CSF filters which can be solved exactly for certain special cases. For the general case, we obtain an approximate solution by solving a related problem with additional constraints. This approximate solution can be further improved with a gradient algorithm for the original problem. Simulation results confirm the excellent performance of BICM-OFDM with robust CSF and SFC-CSF filtering and the effectiveness of the proposed CSIT post-processing method.

In the wireless local area network (WLAN) standard IEEE 802.11b, complementary code keying (CCK) modulation has been adopted for the high data rate transmission mode. In this paper, complexity reduction for block reduced-state sequence estimation (bRSSE), tailored for CCK transmission over frequency-selective channels, is considered. A trellis diagram for the chip phases of the codewords fully describes the CCK code properties. Subset trellises are derived from the full CCK trellis diagram based on set partitioning of the multidimensional CCK code set. The CCK subset trellises connect consecutive bRSSE states forming a compound trellis. The Viterbi algorithm (VA) with per-survivor processing is applied to the compound trellis to take the inter-chip interference into account. Inter-codeword interference is also accounted for by state-dependent decision feedback. The resulting scheme is denoted as bRSSE-pS and has a significantly lower complexity than bRSSE with bruteforce search over the entire CCK code set. By introducing a sphere constraint on the overall decoding trellis (SC-bRSSEpS), the complexity of bRSSE-pS can be further reduced. Omitting states in the CCK subset trellises that violate the sphere constraint, edges that emanate from such states can be pruned, and the average number of metric calculations per CCK trellis segment can be reduced. All presented schemes are also specialized to the case of a single bRSSE trellis state, resulting in block decision-feedback equalization (bDFE) algorithms with per-survivor processing and sphere decoding (bDFE-pS and SC-bDFE-pS, respectively). Simulation results show that the performance of bRSSE-pS (bDFE-pS) and SC-bRSSE-pS (SCbDFE-pS), respectively, is essentially equivalent to that of bRSSE (bDFE) with brute-force search over the entire CCK code set, while complexity is significantly reduced.

For the uplink of the E-UTRA long term evolution (LTE) system, single-carrier frequency-division multiple access (SC-FDMA) transmission has been selected. Frequency-domain linear and decision-feedback equalizers have been already given in the literature for an SC-FDMA transmission over a multiple- input multiple-output (MIMO) intersymbol interference (ISI) channel. In this paper, a soft-output trellis-based equalizer is proposed, taking into account the cyclic ISI structure arising in SC-FDMA, which is especially suited for turbo-encoded transmission over channels with low-to-moderate signal-to-noise ratios (SNRs). A preprocessing stage is necessary for the trellis- based equalizer consisting of a minimum mean-squared error (MMSE) MIMO linear equalizer and a MIMO prediction-error filter, whose design is addressed. Simulation results for an LTE scenario demonstrate that the novel receiver yields significant gains compared to MMSE linear equalization in particular for square MIMO systems.

Transmit beamforming (BF) with reliable channel state information at the transmitter (CSIT) can improve the system performance significantly. On the other hand, for unreliable CSIT pure space-time coding outperforms transmit BF. In this paper, the combination of time-reversal space-time block codes (TR-STBCs) and transmit BF for transmission over frequency-selective fading channels is considered. The proposed scheme takes the reliability of the CSIT into account in an optimum way. For unreliable CSIT, the proposed scheme yields the same diversity gains as a TR-STBC, whereas for reliable CSIT significant BF gains can be obtained in addition to the diversity gain of the TR-STBC.

In this paper, we consider minimum bit error rate (BER) beamforming (BF) for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In particular, assuming perfect channel state information (CSI) at the transmitter we optimize cyclic BF filters (C-BFFs) for minimization of the average BER and the maximum sub-carrier BER, respectively. If the C-BFF length Lgis equal to the number of sub-carriers Nc, closed-form solutions to both optimization problems exist. For the practically relevant case Lgcwe present numerical methods for calculation of the optimum C-BFFs. For finite-rate feedback channels we provide a global vector quantization (GVQ) scheme for codebook design. Simulation results for typical IEEE 802.11n channels confirm the excellent performance of the proposed minimum BER BF schemes for both uncoded and coded MIMO-OFDM systems.

Bit interleaved coded space-time modulation (BIC- STM) is an attractive strategy to achieve high power- and bandwidth-efficiency over multiple-input and multiple-output (MIMO) fading channels due to coding and diversity gains promised by the serial concatenation of an outer convolutional code, a bitwise interleaver and an inner high order space-time modulation (STM). In this paper, BICSTM with iterative decoding (BICSTM-ID) including non-iterative decoding as a special case is considered. For designing the inner STM, two parameters based on bitwise pairwise error probabilities (b- PEP) for the cases with and without a-priori knowledge are proposed as new measures for designing labeling rules for the STM codewords set suited to iterative decoding. On the other hand, the BICSTM-ID scheme is analyzed from an information theoretical aspect, and Information Processing Characteristic (IPC) analysis is developed for MIMO systems in order to fully characterize the BICSTM-ID scheme based on a new equivalent model of combined binary input channels. The analysis results show that the IPC analysis offers a unified perspective for BICSTM and BICSTM-ID from an information theoretical point of view and provides a comprehensive insight into the whole BICSTM-ID scheme as well. Additionally, IPC based upper and lower bounds on bit error ratio (BER) performance are extended to BICSTM-ID, and are confirmed by simulations. These bounds are of significant practical interest for estimating the BER performance of the bit-interleaved concatenated schemes with general nonlinear inner systems.

Reliable channel state information at the transmitter (CSIT) can improve the throughput of wireless networks significantly. In a realistic scenario, there is a mismatch between the CSIT and the true channel. The CSIT may be outdated due to channel variations or could be erroneous due to quantization effects. In this paper, robust transmit beamforming (rBF) and the combination of time-reversal space-time block codes (TR-STBCs) with rBF, both optimized for transmission over frequency-selective fading channels with decision-feedback equalization (DFE), are considered. The reliability of the CSIT is taken into account for the design of channel state dependent transmit signal processing algorithms to achieve robustness against imperfect CSIT. Noisy (quantized) CSIT, outdated CSIT, and the combination of both cases are considered. We show that the performance of the rBF scheme is upper and lower bounded by that of transmit BF with perfect CSIT and optimized delay diversity (ODD) without instantaneous CSIT, respectively. The combination of a TR-STBC with rBF yields significant BF gains in addition to the diversity gains of the TR-STBC for reliable CSIT, whereas for unreliable CSIT, the performance is identical to that of the TR-STBC.

In this paper, robust transmit beamforming (BF) for transmission over frequency-selective fading channels, taking into account the availability of imperfect channel state information at the transmitter (CSIT), is considered. In a realistic scenario, the CSIT differs from the true channel. The CSIT may be outdated due to channel variations or could be erroneous due to quantization effects and/or transmission errors. Transmit BF with reliable CSIT can improve the system performance significantly. On the other hand, pure space-time coding outperforms transmit BF if the CSIT is unreliable. The optimization criterion of the proposed robust BF scheme takes the reliability of the available CSIT in an optimum way into account. We show that the performance of the proposed robust BF scheme is upper and lower bounded by that of transmit BF with perfect CSIT and generalized delay diversity coding without instantaneous CSIT, respectively.

Transmit beamforming (BF) and receive combining are simple and popular methods for performance enhancement in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In this paper, we propose a novel time-domain BF (TD-BF) scheme for MIMO-OFDM systems which uses cyclic BF filters (C-BFFs). Assuming perfect channel state information (CSI) at the transmitter, the C- BFFs are optimized for maximization of the average mutual information per sub-carrier. Using a global vector quantization approach the C-BFFs are quantized for practical finite-rate feedback channels. Simulation and numerical results for typical IEEE 802.11n channels confirm the excellent performance of the proposed scheme and show that TD-BF has a more favorable performance/feedback rate trade-off than previously proposed frequency-domain BF (FD-BF) schemes.

In recent years, single antenna interference cancellation (SAIC) has evolved as an attractive method to increase the capacity of single carrier mobile radio networks like GSM/EDGE (enhanced data rates for GSM evolution). Different algorithms have been developed to improve the performance of the GSM system in severely interference limited scenarios exploiting the structure of the real-valued signal constellation adopted in GSM. For general complex-valued modulation, most known procedures are not applicable, as for a single antenna receiver the system gets overloaded and signals are not separable by e.g. linear operations. In this paper, cochannel interference mitigation with a reduced-complexity equalizer for intersymbol interference (ISI) channels in combination with an appropriate prefilter is considered. In order to enable SAIC for complex-valued modulation schemes, joint (multi-user) turbo equalization is applied. Performance is shown for different variants of the proposed basic scheme, where the GSM/EDGE system serves as an application example. One of the proposed variants is fully compatible to the packet radio transmission schemes of the GSM/EDGE standard and outperforms previously proposed SAIC algorithms for 8PSK.

In this paper, we propose beamforming schemes for frequency-selective channels with decision-feedback equalization (DFE) at the receiver and with, respectively, perfect and quantized channel state information (CSI) at the transmitter. For beamforming with perfect CSI and infinite impulse response (IIR) beamforming Alters (BFFs) we provide a closed-form expression for the optimum BFFs. We also devise two efficient numerical methods for recursive calculation of the optimum finite impulse response (FIR) BFFs with perfect CSI. For beamforming with quantized CSI and finite-rate feedback channel, we propose a global vector quantization (GVQ) algorithm for codebook design. This algorithm is deterministic and independent of initial conditions and does not impose any constraints on the number of transmit and receive antennas, the antenna correlation, or the fading statistics. Our simulation results for typical GSM/EDGE channels show that in general short FIR BFFs are sufficient to closely approach the performance of IIR BFFs even in severely frequency-selective channels. Furthermore, finite-rate feedback beamforming with only a few feedback bits achieves significant performance gains over single-antenna transmission, transmit antenna selection, and optimized delay diversity in frequency-selective fading.

In this paper, we consider beamforming with finite-rate feedback for frequency-selective channels with decision-feedback equalization (DFE) at the receiver. Using average bit error rate as performance measure, we propose a global vector quantization (GVQ) algorithm for codebook design. This algorithm is deterministic and independent of initial conditions. The proposed design method does not impose any constraints on the number of transmit and receive antennas, the antenna correlation, or the fading statistics. Simulation results for typical GSM/EDGE channels show that finite-rate feedback beamforming with one or two feedback bits achieves significant performance gains over single-antenna transmission, transmit antenna selection, and optimized delay diversity. While beamforming filters of length one are preferable if only few feedback bits can be afforded, longer filters can improve performance if a sufficiently large number of feedback bits is available.

In this paper, we propose beamforming schemes for frequency-selective channels with decision-feedback equalization (DFE) at the receiver. We consider both finite impulse response (FIR) and infinite impulse response (IIR) beamforming filters (BFFs). In case of IIR beamforming, we are able to derive closed-form expressions for the optimum BFFs. In addition, we provide an efficient numerical method for recursive calculation of the optimum FIR BFFs. Simulation and numerical results for typical GSM/EDGE channels confirm the significant performance gains achievable with beamforming compared to single-antenna transmission and optimized delay diversity.

State-of-the-art wireless local area network (WLAN) IEEE 802.11b terminals employ complementary code keying (CCK) as modulation format. In this paper, receiver concepts tailored for CCK transmission over frequency-selective fading channels are presented in a unified and systematic framework. First, optimum maximum-likelihood (ML) detection for CCK signaling is considered. Second, for complexity reduction, minimum mean-squared error block decision-feedback equalization (MMSE-DFE) is investigated and reduced-state sequence estimation (RSSE) is considered on the basis of an Ungerbock-like set partitioning of the multidimensional CCK code wordset. In order to improve the reliability of CCK transmission over fading channels, time-reversal space-time block codes (TR-STBCs) combined with receive diversity are applied. Simulation results of the considered suboptimum receivers are compared with a performance approximation for optimum detection. Our results demonstrate the excellent performance of the advocated equalization schemes and the significant gains that can be achieved with TR-STBCs and receive diversity in typical WLAN environments

In mobile communications networks, system capacity is often limited by cochannel interference. Therefore, receiver algorithms for cancellation of cochannel interference have recently attracted much interest. At the mobile terminal, algorithms can usually rely only on one received signal delivered by a single receive antenna. In this letter, a low-complexity single antenna interference cancellation (SAIC) algorithm for real-valued modulation formats referred to as mono interference cancellation (MIC) is introduced which is well suited for practical applications. Field trials in commercial GSM networks using prototype terminals with the proposed MIC algorithm have demonstrated that the novel concept may yield capacity improvements of up to 80%. The underlying principle is also beneficial for adjacent channel interference and receivers with multiple antennas. Furthermore, in coverage-limited scenarios, there is no performance degradation compared with conventional receivers

In this paper, we investigate time-reversal space-time block codes (TR-STBCs) and receive diversity for complementary code keying (CCK) modulation, which is employed in the wireless local area network (WLAN) standard IEEE 802.11b. The construction of TR-STBCs from orthogonal space-time block codes designed for flat fading channels for an arbitrary number of transmit antennas is discussed. Furthermore, an equivalent single-input single-output (SISO) transmission model for CCK is derived, which combines the effects of space-time coding and multiple receive antennas. For indoor multipath fading channels numerical results are provided for TR-STBCs with two and three transmit antennas, which demonstrate that significant gains can be obtained compared to a SISO transmission. For detection, decision-feedback equalization (DFE) and reduced-state sequence estimation (RSSE) especially tailored for CCK transmission are considered.

We optimize delay diversity (DD) for the case when simple linear equalization (LE) is used at the receiver. We consider the general case of transmission over a correlated multiple-input multiple-output (MIMO) frequency-selective fading channel. The proposed optimization requires the knowledge of the statistical properties of the wireless channel at the transmitter, but channel state information is only required at the receiver side. Based on an approximation of the bit error rate for LE, we derive a stochastic gradient algorithm that allows us to optimize the DD transmit filters. Simulation results for the GSM/EDGE system show significant performance gains for the proposed optimized DD scheme over DD schemes reported previously (Gore, D. et al., Proc. IEEE Int. Conf. Commun., ICC, p.1949-53, 2002; Schober, R. et al., IEEE Trans. Wireless Commun., 2004), if LE is used at the receiver.

This paper proposes an optimized delay diversity (ODD) scheme for frequency-selective fading channels. The novel scheme requires knowledge of the channel impulse response (CIR) autocorrelation matrix at the transmitter, but the CIRs themselves have to be available only at the receiver side. A cost function for optimization of the ODD transmit filters is derived and a steepest descent algorithm for iterative calculation of the filter coefficients is provided. In addition, an upper bound on the cost function is derived and employed to prove the asymptotic optimality of the generalized DD (GDD) scheme in [Proc. IEEE Int. Conf. Communications (ICC), (2002) p. 1949] for very high signal-to-noise ratios (SNRs) and transmit filters of maximum length. However, for SNRs of practical interest and reasonable filter lengths, the novel ODD scheme significantly outperforms GDD for both optimum and suboptimum equalizations. It is also shown that, in contrast to the frequency-nonselective case, for frequency-selective channels, transmit diversity schemes designed under the high SNR assumption may perform poorly for practically relevant SNRs.

We consider a joint detection approach for cancellation of co-channel interference in time-division multiple access (TDMA) mobile communications systems like GSM/EDGE (enhanced data rates for GSM evolution). Concepts from reduced-state equalization of frequency-selective multiple-input multiple-output (MIMO) channels are applied together with prefiltering. A novel efficient prefilter computation algorithm is presented. Simulation results demonstrate the high performance of the proposed receiver for GSM/EDGE applications.

In this paper, complementary code keying (CCK) transmission over frequency-selective fading channels is investigated. Since CCK can be interpreted as a block code, an appropriate vector signal model is introduced. Based on pairwise error probabilities, we derive a performance approximation for optimum detection in general frequency-selective Ricean fading channels and compare the theoretical results with simulations. Because optimum maximum-likelihood sequence estimation (MLSE) is not practical for large channel delay spreads, minimum mean-squared error decision-feedback equalization (MMSE-DFE) is employed. Two different DFE approaches are considered here: (a) the conventional scalar DFE and (b) a block-based DFE optimized for the underlying vector signal model. Furthermore, a reduced-state sequence estimation (RSSE) algorithm is designed for CCK. Numerical results are given for fixed channels and common indoor fading channel profiles.

In this paper, we propose an optimized delay diversity (ODD) scheme for fading intersymbol interference (ISI) channels. The novel scheme requires knowledge of the channel impulse response (CIR) autocorrelation matrix at the transmitter, but the CIRs themselves have to be available only at the receiver side. We derive a cost function for optimization of the ODD transmit filters and we provide a steepest descent algorithm for iterative calculation of the filter coefficients. In addition, an upper bound on the cost function is derived and employed to prove the asymptotic optimality of the generalized DD (GDD) scheme in [1] for very high signal-to-noise (SNR) ratios and transmit filters of maximum length. However, for SNRs of practical interest and reasonable filter lengths the novel ODD scheme significantly outperforms GDD for both optimum and suboptimum equalization.

In this paper, reduced-state sequence estimation (RSSE) designed for complementary code keying (CCK) transmission over frequency-selective fading channels is investigated. An Ungerboeck-like set partitioning of the multidimensional CCK symbol constellation is given. For the proposed RSSE algorithm based on this partitioning, numerical results are provided for a typical indoor wireless channel. A comparison is made with optimum detection and suboptimum decision-feedback equalization (DFE), respectively.

We optimize delay diversity (DD) for the case when computationally efficient decision-feedback equalization (DFE) is used at the receiver. We consider the general case of transmission over a correlated multiple-input multiple-output (MIMO) frequency-selective fading channel. The proposed optimization requires knowledge of the statistical properties of the wireless channel at the transmitter, but channel state information is only required at the receiver side. Based on an approximation of the pairwise error probability for DFE, we derive a stochastic gradient algorithm that allows us to optimize the DD transmit filters. Simulation results for the GSM/EDGE system show significant performance gains of the proposed optimized DD scheme over the DD schemes reported by D. Gore et al. (see Proc. IEEE Int. Conf. Commun., p.1949-53, 2002) and R. Schober et al. (see Proc. IEEE ICC, 2004), respectively, if DFE is used at the receiver.

We present three different decision-feedback sequence estimation (DFSE) schemes for time-reversal space-time block coding (TR-STBC). The first scheme is called unwhitened DFSE (U-DFSE) and performs reduced-state sequence estimation based on the output of the spatio-temporal matched filter (MF) typically employed in TR-STBC. The second approach improves upon U-DFSE by subtracting a bias term caused by anti-causal interference from the U-DFSE metric. In the third scheme, the noise component in the output of the spatio-temporal MF is first whitened using a prediction-error filter that can he efficiently computed using the Levinson-Durbin algorithm, and subsequently whitened DFSE (W-DFSE) is performed. As relevant example, all three DFSE schemes are compared for the GSM/EDGE system and typical channel profiles such as typical urban (TU) and hilly terrain (HT). Our results show that for binary modulation (as used in GSM) U-DFSE and its improved version can approach the performance of W-DFSE for the full range of delay spreads relevant for GSM and EDGE. On the other hand, for high-level modulation (as used in EDGE) only W-DFSE gives a satisfactory performance, if a low trellis complexity is desired.

In this paper, we develop receiver concepts for transmission with space-time block codes (STBCs) over frequency-selective fading channels. The focus lies on Alamouti's space-time block-coding scheme, but the results may be generalized to other STBCs as well. We show that a straightforward combination of conventional equalizers and a space-time block decoder is only possible if at least as many receive antennas as transmit antennas are employed, but not for the practically interesting case of pure transmit diversity, for which space-time coding had been originally developed. This restriction is circumvented by our approach. Here, the structural properties of the transmit signal of space-time block coding, which is shown to be improper (rotationally variant), are fully used. For this, equalizers with widely linear (WL) processing are designed, such as a WL equalizer, a decision-feedback equalizer with WL feedforward and feedback filtering, and a delayed decision-feedback sequence estimator with WL prefiltering. Simulation results demonstrate that the proposed concepts may be successfully employed in an enhanced data rates for GSM evolution (EDGE) receiver, especially for pure transmit diversity. Here, significant gains can be observed, compared with a conventional single-input single-output transmission.

The performance loss due to separation of detection and decoding on the binary-input additive white Gaussian noise (AWGN) channel is quantified in terms of mutual information. Results are reported for both the code-division multiple-access (CDMA) channel in the large system limit and the intersymbol interference (ISI) channel. The results for CDMA rely on the replica method developed in statistical mechanics. It is shown that a previous result of Shamai and Verdu found for Gaussian input alphabet holds also for binary input alphabets. For the ISI channel, the performance loss is calculated via the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. Comparisons are made to the capacity of separate detection and decoding using suboptimum detectors such as a decision-feedback equalizer.

In this paper, space-time block-coded transmission over frequency-selective fading channels is investigated. A lower bound for the pairwise error probability for optimum detection is given. Also, an approximation for the bit-error rate is derived and compared with simulation results for maximum-likelihood sequence estimation (MLSE) for the GSM/EDGE (Enhanced Data Rates for GSM Evolution) system. Furthermore, a novel design rule for space-time block codes (STBCs) for frequency-selective fading channels is provided. A corresponding code is designed and shown to yield higher performance than Alamouti's code. It is demonstrated that for fading channels with L independent impulse response coefficients, STBCs designed for the flat fading channel can achieve at most a diversity order of (N/sub T/+L-1)N/sub R/ if N/sub T/ transmit antennas and N/sub R/ receive antennas are used. On the other hand, the maximum diversity order employing the proposed code design rule is LN/sub T/N/sub R/.

In this paper, we propose a widely linear (WL) decision-feedback (DF) minimum mean-squared error (MMSE) receiver for direct-sequence code-division multiple access (DS-CDMA) with binary phase-shift keying (BPSK) modulation. Both theoretical considerations and simulations show that the novel receiver yields significant performance gains over both WL receivers without feedback and conventional DF receivers. In particular, the proposed WL DF-MMSE receiver achieves a high performance even in overloaded systems. For efficient adaptation of the filter coefficients a WL least-mean-square (LMS) algorithm and a WL recursive least-squares (RLS) algorithm are devised.

In this paper, a novel data-aided stochastic gradient algorithm for adjustment of the widely linear (WL) minimum mean-squared error (MMSE) filter for multiple access interference (MAI) suppression for direct-sequence code-division multiple access (DS-CDMA) is introduced and analyzed. We give analytical expressions for the steady-state signal-to-interference-plus-noise ratio (SINR) of the proposed WL least-mean-square (LMS) algorithm, and we also investigate its speed of convergence. Both analytical considerations and simulations show in good agreement the superiority of the novel WL adaptive algorithm. Nevertheless, the computational complexity of the proposed algorithm is lower than that of the linear LMS algorithm.

In this paper, a novel blind minimum-output-energy (MOE) algorithm for adjustment of the unbiased widely linear (WL) minimum mean-squared error (MMSE) filters for multiple access interference (MAI) suppression for direct-sequence code-division multiple access (DS-CDMA) is introduced and analyzed. We analyze the steady-state signal-to-interference-plus-noise ratio (SINR) and the convergence of the proposed blind WL stochastic gradient algorithm. Wherever possible comparisons with the linear blind MOE algorithm are made. Both analytical considerations and simulations show in good agreement the superiority of the novel WL adaptive algorithm. Nevertheless, it requires a lower computational complexity than its linear counterpart.

For many existing blind channel estimation algorithms, accurate estimates of the channel order are a prerequisite. A novel scheme for blind channel order estimation using exclusively second-order statistics of the received signal is proposed, which can be embedded in channel estimation and is based on the examination of an indicator function constructed from initial channel estimates with overestimated order. It is demonstrated that significantly better order estimates can be obtained than by information-theoretic criteria like the minimum description length criterion.

We propose several equalization schemes based on widely linear processing (WLP). The received signal and its complex conjugate are separately filtered and the results are linearly combined. It is shown that WLP yields a gain in performance if the (noiseless) received signal can be interpreted as the convolution of a real-valued data sequence and an equivalent complex-valued intersymbol interference channel impulse response. Such a model applies to, e.g., amplitude-shift keying, offset quadrature amplitude modulation, and binary minimum-shift keying-type modulation. We consider receivers without and with decision feedback. Finite impulse response filters are derived for these structures, which are optimum with respect to the zero-forcing and minimum mean-squared error (MMSE) criteria, respectively. In the MMSE case, adaptive algorithms for filter adjustment are given. Infinite filter orders are investigated in order to obtain analytical performance results. Furthermore, suboptimum trellis-based detection with widely linear preprocessing is briefly discussed. It is demonstrated analytically and by numerical examples that widely linear schemes may outperform conventional schemes significantly, depending on the considered application.

In this article, modulation diversity (MD) for frequency-selective fading channels is proposed. The achievable performance with MD is analyzed and a simple design criterion for MD codes for Rayleigh-fading channels is deduced from an upper bound on the pairwise error probability (PEP) for single-symbol transmission. This design rule is similar to the well-known design rule for MD codes for flat fading and does not depend on the power-delay profile of the fading channel. Several examples for MD codes with prescribed properties are given and compared. Besides the computationally costly optimum receiver, efficient low-complexity linear equalization (LE) and decision-feedback equalization (DFE) schemes for MD codes are also introduced. Simulations for the widely accepted COST fading models show that performance gains of several decibels can be achieved by MD combined with LE or DFE at bit-error rates (BERs) of practical interest. In addition, MD also enables the suppression of cochannel interference.

In this paper, we develop receiver concepts for transmission with space-time block codes (STBC) over frequency-selective fading channels. The focus lies on Alamouti's STBC, but the results may be generalized to related STBC. We show that a straightforward combination of conventional equalizers and a space-time block decoder is only possible if at least as many receive antennas as transmit antennas are employed, but not for the practically interesting case of pure transmit diversity. This restriction is circumvented by our approach. Equalizers with widely linear (WL) processing are designed, utilizing the structural properties of the transmit signal of space-time block coding, which is shown to be improper (rotationally variant). These schemes are especially suited for equalization of high-level modulated signals, which are used in third-generation time-division multiple access mobile communications standards such as EDGE (Enhanced Data Rates for GSM Evolution).

Space-time block-coded transmission over fading intersymbol interference (ISI) channels is investigated. A lower bound on the pairwise error probability for optimum detection is given. Also, an approximation for the bit error rate is derived and compared with simulation results for maximum-likelihood sequence estimation (MLSE) for the GSM/EDGE (enhanced data rates for GSM evolution) system. Furthermore, a novel design rule for space-time block codes (STBC) with arbitrary rate for fading ISI channels is provided. A corresponding full-rate code is designed and shown to yield higher performance than Alamouti's code. It is demonstrated that for fading channels with L independent impulse response coefficients, full-rate STBCs designed for the flat fading channel can achieve at most a diversity order of (N/sub T/+L-1)N/sub R/ if N/sub T/ transmit antennas and N/sub R/ receive antennas are used. On the other hand, the maximum diversity order employing the proposed code design rule is LN/sub T/N/sub R/.

The performance loss due to separation of detection and decoding on the binary-input Gaussian CDMA channel is calculated in the large system limit. It is shown that a previous result found for the Gaussian input alphabet holds also for the binary input alphabet.

A well-known receiver strategy for a linearly modulated signal transmitted over a frequency-selective channel is channel equalization. Recently it was proposed to employ a minimum mean-squared error (MMSE) channel equalizer for the downlink of CDMA. In this paper, we introduce a new receiver concept using MMSE channel equalization as a first stage and MMSE decision-feedback equalization (DFE) utilizing soft feedback from the decoding unit as a second stage. Both schemes are compared for the downlink of CDMA. It turns out, that after channel decoding we gain about 1 dB compared to conventional MMSE channel equalization.

A novel multiuser detector for direct sequence code division multiple access is proposed. The receiver performs iterated soft decision interference cancellation (ISDIC) based on multiuser interference suppression filters designed for minimization of the mean-square error. Assuming a complex modulation format, we show that the multiuser interference becomes rotationally variant in the course of the iterations. Regarding this rotational variance in the design of the multiuser interference suppression filter, the presented iterative multiuser detector achieves significant performance gains compared with conventional ISDIC employing a standard minimum mean-squared error filter which is optimum only for rotationally invariant multiuser interference.

We consider the density of the zeros of random polynomials with nonzero mean correlated Gaussian coefficients. We show that for most communication problems the original result by Hammersley (1956) can be expressed in a simpler form. In order to illustrate the usefulness of the presented theory, two applications are considered. First, the zeros of frequency-selective Ricean fading channels are investigated. Second, the zeros of the transfer function corresponding to channel impulse response estimates obtained by a least-squares approach are calculated. For both applications implications for equalizer design are discussed.

A novel robust noncoherent receiver for minimum mean-squared error (MMSE) interference suppression for direct-sequence code-division multiple access (DS-CDMA) is proposed. The receiver consists of a linear MMSE filter and a decision-feedback differential detector (DF-DD). The performance of the proposed scheme is investigated analytically and by computer simulations. It is shown that the loss compared to coherent MMSE interference suppression is limited and can be made arbitrarily small by increasing the observation window used for calculation of the reference symbol of the DF-DD. Hence, the regarded noncoherent receiver is near-far resistant. For adjustment of the MMSE filter coefficients three noncoherent adaptive algorithms are proposed. In contrast to coherent adaptive algorithms, these noncoherent algorithms have the important advantage that they also converge if the channel phase is time-variant.

In advanced time-division multiple-access (TDMA) mobile communications systems, reduced-state equalization algorithms have to be employed because high-level modulation is used in order to improve spectral efficiency. Reduced-state equalizers yield only high performance, if the overall discrete-time system to be equalized is minimum-phase. Therefore, in general, a discrete-time prefilter has to be inserted in front of equalization. For prefilter computation, several approaches are investigated in this paper. For the finite impulse response (FIR) prefilter case, which seems to be more relevant for practical applications than the in finite impulse response case, we discuss a method based on minimum mean-squared error decision-feedback equalization and a novel approach based on linear prediction (LP). The LP method seems to be very robust and requires an only moderate amount of computational complexity. Here, the prefilter consists of the cascade of a channel-matched filter and a prediction-error filter, which may be viewed as a finite-length approximation to the noise whitening part of the ideal prefilter transfer function. A key observation of the paper is that the proposed cascaded structure enables a very efficient prefilter computation because a prediction-error filter can be calculated via the Levinson-Durbin algorithm. Simulation results are given, which demonstrate that the performance of reduced-state equalization with proper FIR prefiltering is close to that of equalization combined with ideal all-pass prefiltering. Furthermore, it is shown that high performance can be obtained for TDMA mobile communications systems, if the LP scheme is employed for prefiltering.

An equalization concept for the novel radio access scheme Enhanced Data rates for GSM Evolution (EDGE) is proposed by which high performance can be obtained at moderate computational complexity. Because high-level modulation is employed in EDGE, optimum equalization as usually performed in Global System for Mobile Communications (GSM) receivers is too complex and suboptimum schemes have to be considered. It is shown that delayed decision-feedback sequence estimation (DDFSE) and reduced-state sequence estimation (RSSE) are promising candidates. For various channel profiles, approximations for the bit error rate of these suboptimum equalization techniques are given and compared with simulation results for DDFSE. It turns out that a discrete-time prefilter creating a minimum-phase overall impulse response is indispensable for a favorable tradeoff between performance and complexity. Additionally, the influence of channel estimation and of the receiver input filter is investigated and the reasons for performance degradation compared to the additive white Gaussian noise channel are indicated. Finally, the overall system performance attainable with the proposed equalization concept is determined for transmission with channel coding.

In this paper, the distribution of zeros of mobile channels is investigated and the results obtained are applied to the channel models standardized for the GSM (Global System for Mobile Communications)/EDGE (Enhanced Data Rates for GSM Evolution) system. The taps of the discrete-time overall impulse response can be modeled as correlated complex Gaussian random variables with zero or nonzero mean, where the correlations depend on the transmit filter, the power delay profile of the channel, and the receiver input filter. For calculation of the density of zeros of the overall transfer function results from the mathematical literature on the zeros of random polynomials are used. From this density two cumulative distributions which are relevant for the design of suboptimum receivers for intersymbol interference (ISI) channels are derived for the case of uncorrelated taps with exponential distribution of the tap variances. Finally, practical equalizer design rules for the GSM/EDGE system are deduced from the calculated statistical distributions.

In this paper, a novel robust noncoherent receiver for MMSE interference suppression for DS-CDMA is proposed. The receiver consists of an MMSE filter and a decision-feedback differential detector (DF-DD). The performance of the proposed scheme is investigated analytically and by computer simulations. It is shown that the loss compared to coherent MMSE interference suppression is limited and can be made arbitrarily small by increasing the observation window used for calculation of the reference symbol of the DF-DD. For adjustment of the MMSE filter coefficients a noncoherent normalized least-mean-square (NC-NLMS) algorithm is proposed which is robust against channel phase variations.

The distribution of zeros of mobile channels is investigated and the results obtained are applied to the GSM/EDGE system. The taps of the discrete-time overall impulse response can be modeled as correlated complex Gaussian random variables, where the correlations depend on the transmit filter, the power delay profile of the channel, and the receiver input filter. For calculation of the density of zeros of the overall transfer function, a result from the mathematical literature is used. From this density, two cumulative distributions which are relevant for the design of suboptimum receivers are derived. Our investigations show that for the power delay profiles specified for GSM/EDGE, an allpass prefilter which transforms the impulse response in its minimum phase equivalent should be employed if decision-feedback equalization (DFE) or reduced-state sequence estimation (RSSE) are used at the receiver. On the other hand, if impulse response truncation using a linear prefilter is applied, the truncated impulse response should have a length of three as shown.

In this letter, two novel noncoherent adaptive algorithms for channel identification are introduced. The proposed noncoherent least-mean-square (LMS) and noncoherent recursive least squares (RLS) algorithms can be combined easily with noncoherent sequence estimation (NSE) for M-ary differential phase-shift keying signals transmitted over intersymbol interference (ISI) channels. It is shown that the resulting adaptive noncoherent receivers are very robust against carrier phase variations. For zero frequency offset, the convergence speed and the steady-state error of the noncoherent adaptive algorithms are similar to those of conventional LMS and RLS algorithms. However, the conventional algorithms diverge even for relatively small frequency offsets, whereas the proposed noncoherent algorithms converge for relatively large frequency offsets. Simulations confirm the good performance of NSE combined with noncoherent adaptive channel estimation in time-variant (fading) ISI channels.

In this article, prediction-based decision-feedback differential detection (DF-DD) for 16-level differentially encoded amplitude/phase-shift keying is proposed. Unlike previously reported DF-DD schemes, this scheme provides a performance gain over conventional differential detection under general Ricean fading conditions. A further important advantage of the novel scheme is that it is able to compensate a small carrier frequency offset. The linear predictor coefficients may be updated using the recursive least-squares algorithm, which can start blind, i.e., without a priori knowledge about the channel statistics and without a training sequence. This makes the scheme attractive for application in mobile communications since the statistics of a nonstationary mobile channel can be tracked.

A novel noncoherent adaptive algorithm for channel estimation is introduced. The proposed noncoherent least-mean-square (NC-LMS) algorithm can be combined easily with noncoherent sequence estimation (NSE) for M-ary differential phase-shift keying (MDPSK) signals transmitted over intersymbol interference (ISI) channels. For zero frequency offset the convergence speed and the steady-state error of the novel noncoherent adaptive algorithm are similar to those of a conventional (coherent) LMS algorithm. However, the conventional LMS algorithm diverges even for relatively small frequency offsets, whereas the proposed NC-LMS algorithm also converges for relatively large frequency offsets. Simulations confirm the good performance of NSE combined with noncoherent adaptive channel estimation in time-variant (fading) ISI channels.

A novel noncoherent linear equalization scheme is introduced and analyzed. The proposed scheme is not only applicable for M-ary differential phase-shift keying (MDPSK) but also for M-ary differential amplitude/phase-shift keying (MDAPSK) and minimizes the variance of intersymbol interference (ISI) in the equalizer output signal. The optimum equalizer coefficients may be calculated directly from an eigenvalue problem. For an efficient recursive adaptation of the equalizer coefficients, a modified least-mean-square (LMS) algorithm is proposed. Simulations confirm the good performance of the considered noncoherent equalization scheme and its robustness against frequency offset.

In advanced TDMA mobile communications systems, reduced-state equalization algorithms have to be employed because a high-level modulation is used in order to improve the spectral efficiency. Such equalizers only have a high performance, if the overall discrete-time system to be equalized is minimum-phase. Therefore, in general, a discrete-time prefilter has to be inserted in front of equalization. In the literature, several approaches have been proposed for computation of a suitable FIR or IIR prefilter. We present an approach for FIR prefilter computation, which is quite robust and requires an only moderate computational complexity. The prefilter consists of the cascade of a channel-matched filter and a prediction-error filter, which can be calculated via the Levinson-Durbin algorithm. Simulation results are given, which demonstrate that the performance of the proposed approach is essentially equivalent to the case of reduced-state equalization combined with ideal allpass prefiltering.

The distribution of zeros of mobile channels is investigated and the results obtained are applied to the GSM (Global System for Mobile Communications)/EDGE (Enhanced Data Rates for GSM Evolution) system. The taps of the discrete-time overall impulse response can be modeled as correlated complex Gaussian random variables, where the correlations depend on the transmit filter, the power delay profile of the channel, and the receiver input filter. For calculation of the density of zeros of the overall transfer function a result from the mathematical literature on the zeros of polynomials with correlated Gaussian coefficients is used. From this density two cumulative distributions which are relevant for the design of suboptimum receivers are derived and specialized to the case of uncorrelated taps. Finally, practical equalizer design rules for the GSM/EDGE system are deduced from the calculated statistical distributions.

In this paper, linear prediction-based decision-feedback differential detection (DF-DD) for M-ary differential phase-shift keying (MDPSK) signals transmitted over Ricean fading channels is proposed. This scheme can improve conventional DD significantly for a multitude of frequency-nonselective channels, as shown analytically and by computer simulations. Prediction-based DF-DD is particularly well suited for application in mobile communications since the predictor coefficients may be updated regularly using the recursive least squares (RLS) algorithm. Here, adaptation can start blind, i.e., no training sequence and no a prior knowledge about the channel statistics are required. A further important characteristic of the proposed detection scheme is that no degradation occurs under frequency offset. The bit error rate (BER) performance of QDPSK with genie-aided prediction-based DF-DD is analyzed, and it is shown under which conditions the irreducible error floor of conventional DD can be removed entirely. In addition, the influence of Doppler shift is discussed. Last, the proposed scheme is compared with a second DF-DD scheme, which is based on multiple-symbol detection.

In this letter, a novel equalization algorithm applying soft-decision feedback and designed for binary transmission is introduced. In contrast to conventional decision-feedback equalization (DFE), iterations are necessary, because a simple matched filter serves as feedforward filter, which collects signal energy, but creates noncausal intersymbol interference. The rule for generating soft decisions is adapted continuously to the current state of the algorithm. In most cases, standard DFE methods are clearly outperformed. For a class of certain channel impulse responses, performance of maximum-likelihood sequence estimation is attained, in principle. The high performance of the scheme is explained using results from neural network theory.

A novel noncoherent decision-feedback equalization (NDFE) scheme for M-ary differential phase shift-keying signals transmitted over intersymbol interference channels is presented. A suboptimum version with lower computational complexity and a noncoherent linear equalizer (NLE) are derived from the original NDFE scheme. Furthermore, the relation of the novel NLE to a previously proposed NLE is investigated. In contrast to known NDFE schemes, the novel scheme can approach the performance of coherent minimum mean-squared error decision-feedback equalization. For adaptation of the feedforward and feedback filters, efficient novel modified least mean-square and recursive least squares algorithms are presented. Finally, it is shown that the proposed adaptive NDFE scheme is robust against frequency offset.

A novel noncoherent receiver for M-ary differential phase-shift keying signals transmitted over intersymbol interference channels is presented. The noncoherent receiver consists of a linear equalizer and a decision-feedback differential detector. A significant performance gain over a previously proposed noncoherent receiver can be observed. For an infinite number of feedback symbols, the optimum equalizer coefficients can be calculated analytically, and the performance of the proposed receiver approaches that of a coherent linear minimum mean-squared-error equalizer. Moreover, a modified least mean square and a modified recursive least squares algorithm for adaptation of the equalizer coefficients are discussed.

In this paper, decision-feedback differential detection (DF-DD) of M-ary differential phase-shift keying (MDPSK) signals, which has been introduced previously for the additive white Gaussian noise (AWGN) channel by Leib et al. (1988) and Edbauer (1992), is extended to flat Rayleigh fading channels. The corresponding DF-DD metric is derived from the multiple-symbol detection (MSD) metric and for genie-aided DF-DD, an exact expression for the bit-error rate (BER) of QDPSK (M=4) is calculated. Furthermore, the dependence of BER on the power spectrum of the fading process is investigated for feedback filters of infinite order. It is shown that in this case, for ideally bandlimited fading processes, the error floor of conventional differential detection (DD) can be removed entirely. Simulation results confirm that both MSD and DF-DD with feedback filters of finite order can reduce the error floor of conventional DD significantly. DF-DD thereby causes considerably less computational load.

A new technique is proposed to improve the performance of differential detection (DD) of M-ary differential phase-shift keying (MDPSK) significantly, applying sequence estimation. In order to obtain an appropriate representation of the received signal, a nonlinear time-variant finite impulse response or infinite impulse response prediction-error filter is used. For both filter structures the optimum coefficients are derived, assuming transmission over an additive white Gaussian noise (AWGN) channel. Delayed decision-feedback sequence estimation (DDFSE) is employed to estimate the transmitted symbol sequence. It is shown by simulations that even for decision-feedback equalization, which is a simple special case of DDFSE, a significant performance improvement of conventional DD under AWGN conditions results. In contrast to other noncoherent low-complexity receivers proposed in literature, this receiver is very robust under flat fading (Rayleigh and Ricean) conditions.

A new combined precoding/shaping technique for fast digital transmission over twisted pair lines is proposed. Major advantages of this "dynamics shaping" are: Dynamics of the signal at the input of the decision device are reduced by a great amount. Thereby, A/D-conversion, adaptive equalization, and symbol timing are rather facilitated. A trade-off between signal dynamics at the transmitter output, decision device input and SNR-gain by noise whitening is offered. For dynamics limitation relevant in practice, gains up to 6 dB are achieved. Additionally, the transmitter can be fixed to a typical application because, in contrast to Tomlinson-Harashima or other precoding techniques, blind adaptive equalization is practicable to remove residual intersymbol interference in the case of a mismatch of precoding and actual cable characteristics. The residual SNR-loss is negligible in most applications. SNR-gains due to noise prediction, channel coding and signal shaping simply can be combined us...