The Internet of Bio-Nano Things is a promising technology to reduce turnover and increase resolution of medical data collection. Therefore, tiny devices, so-called Nano Machines (NMs) are placed inside the human body, where the collect, process and transmit information to devices outside the human body. Interfaces between in-body and the external electrical domain are crucial to the realization of this technology. In this paper, we propose sweat glands, a pre-existing infrastructure, as a novel interface through which NMs can transmit information from the inside to the outside of the human body. We provide a detailed mathematical model of the envisioned communication system and evaluate its communication performance using different types of detectors (e.g., Neural Network based detector).

The Internet of Bio-Nano Things (IoBNT) is an envisioned extension of the Internet of Things (IoT), which aims to connect natural and synthetic biological systems and networks to the Internet. Due to the access to new domains (e.g., human body) this concept may help to enable transformative applications in healthcare and nanomedicine. However, it also faces several challenges, such as suitable interfaces and appropriate communication methods. Synthetic Molecular Communications (MC), a molecule-based bio-compatible communication concept, is among the most promising solution, which also defines the requirements for the respective interfaces. Typically, MC systems require a digital representation of the information to be transmitted and, thus, the development of devices for the conversion of analog biological signals to digital signals is crucial, but not well investigated. Thus, in this paper we propose a novel Molecular Analog-to-Digital converter (MADC). The MADC is based on a new neural network representation of the electronic flash ADC concept. This representation enables the implementation of the MADC using the recently proposed Molecular Nano Neural Networks (M3N). In particular, the proposed MADC consists of two matrix multiplication layers that are connected via a ReLU and threshold layer. We derive general design guidelines for the MADC and successfully validate it through computer simulations.

Intelligent behavior is an emergent phenomenon observed in biological organisms across all scales. It describes the cooperative behavior of low complexity entities to accomplish complex tasks, which exceed their individual capabilities. This property is particularly important for the Internet of Bio-Nano Things (IoBNT), which consists of Bio-Nano Things (BNTs) used in the human body, where they face many restrictions, such as bio-compatibility and size constraints. In this paper, we present a novel BNT-architecture, called Molecular Nano Neural Networks (M3N), which allows the implementation of intelligence on the micro-/nano-scale. The proposed structure consists of compartments (low complexity entities) that are connected to each other to form a network. Based on reaction and diffusion of molecules in and between connected compartments, this network mimics an artificial neural network, which is an important step towards artificial intelligence in the IoBNT. We provide design guidelines for the proposed M3N and successfully validate it by applying a regression and classification task.

In this work, we propose a novel nano-scale architecture that performs matrix multiplications. Matrix multiplications are the basic operations of machine learning (ML) algorithms and, thus, the presented approach enables their application at the nano-scale, for example inside the human body in the Internet of Bio-Nano-Things (IoBNT). It is based on the molecule exchange between connected compartments and introducing chemical reactions in some of them. The matrix entries are solely defined by the volumes of the compartment. We provide a detailed mathematical description of the stochastic and dynamic behavior of the system. Moreover, we derive design guidelines for the proposed architecture. Finally, we validated the proposed approach through particle-based simulations.

Droplet-based microfluidic networks interconnect multiple microfluidic modules which allow to process (e.g., mix, sort, heat, incubate) so-called payload droplets (i.e., droplets containing a biological sample) on a single microfluidic chip. Inside such networks the path of a droplet and, thus, the module which processes it, can be controlled by microfluidic switches. Thus far, these switches are realized by injecting additional control droplets into the network which allow to trigger the switching mechanism by solely exploiting passive hydrodynamic effects. While this eliminates the need of expensive components such as valves, this droplet-controlled switching concept is very sensitive and already slight deviations, e.g., in the control droplet injection could lead to incorrectly triggered switches. In this work, we address this issue by proposing a new concept of pressure-controlled networks which omit the control droplets (and their drawbacks) and, instead, use a single pump in order to drive the switches. Using design automation expertise together with established models, we derive a corresponding blueprint which realizes this idea for a specific network architecture. Simulations based on established methods and design tools confirmed the suitability of the proposed pressure-controlled networks.

Sample preparation refers to the task of generating fluids with a specified target concentration. Generally, this is achieved by performing a set of mixing operations between biochemical fluids with a given volumetric ratio. Sample preparation plays a crucial role in several medical applications. Microfluidic devices or labs-on-chips (LoCs) got established as a suitable solution to realize this task in a miniaturized, integrated, and automatic fashion. Over the years, a variety of different microfluidic platforms emerged, which all have their respective pros and cons. Accordingly, numerous approaches aiming at the sample preparation problem have been proposed—each specialized on a single platform only. More precisely, sample preparation methods introduced thus far provide solutions for a particular platform only, i.e., they are platform specific. In this work, we propose a generic approach that generalizes the constraints of the different microfluidic platforms and, by this, provides a platform-independent sample preparation method. This allows designers to quickly check what existing platform is most suitable for the considered task and to easily support upcoming and future microfluidic platforms as well. We evaluated the performance of the proposed method with a wide range of test cases and concluded (from the evaluations) that the proposed generic approach is capable of efficiently generating results for various platforms with a quality that is close to results from dedicated approaches presented thus far.

Microfluidics is a prospective field which provides technological advances to the life sciences. However, the design process for microfluidic devices is still in its infancy and frequently results in a "trial-and-error" scheme. In order to overcome this problem, simulation methods provide a powerful solution---allowing for deriving a design, validating its functionality, or exploring alternatives without the need of an actual fabricated and costly prototype. To this end, several physical models are available such as Computational Fluid Dynamics (CFD) or the 1-dimensional analysis model. However, while CFD-simulations have high accuracy, they also have high costs with respect to setup and simulation time. On the other hand, the 1D-analysis model is very efficient but lacks in accuracy when it comes to certain phenomena. In this work, we present ideas to combine these two models and, thus, to provide an accurate and efficient simulation approach for microfluidic networks. A case study confirms the general suitability of the proposed approach.

Sample preparation plays a crucial role in several medical applications. Microfluidic devices or Labs-on-Chips (LoCs) got established as a suitable solution to realize this task in a miniaturized, integrated, and automatic fashion. Over the years,$a$variety of different microfluidic platforms emerged, which all have their respective pros and cons. Accordingly, numerous approaches for sample preparation have been proposed-each specialized on a single platform only. In this work, we propose an idea towards a generic sample preparation approach which will generalize the constraints of the different microfluidic platforms and, by this, will provide a platform-independent sample preparation method. This will allow designers to quickly check what existing platform is most suitable for the considered task and to easily support upcoming and future microfluidic platforms as well. We illustrate the applicability of the proposed method with examples for various platforms.

Molecular communications is a promising frame-work for the design of controlled-release drug delivery systems. In this framework, drug carriers are modeled as transmitters, the diseased cells as absorbing receivers, and the channel between transmitter and receiver as diffusive channel. However, existing works on drug delivery systems consider only simple drug carrier models, which limits their practical applicability. In this paper, we investigate diffusion-based spherical matrix-type drug carriers, which are employed in practice. In a matrix carrier, the drug molecules are dispersed in the matrix and diffuse from the inner to the outer layers of the carrier once immersed in a dissolution medium. We derive the channel response of the matrix carrier transmitter for an absorbing receiver and validate the results through particle-based simulations. Moreover, we show that a transparent spherical transmitter, with the drug molecules uniformly distributed over the entire volume, is as special case of the considered matrix system. For this case, we provide an analytical expression for the channel response. Finally, we compare the channel response of the matrix transmitter with those of point and transparent spherical transmitters to reveal the necessity of considering practical models.

Droplet-based microfluidic networks allow to process biological or medical samples by standard unit operations, such as mixing, incubating, sorting, or sensing. However, many of these networks usually perform such operations in a predefined way and, thus, lack in their flexibility. To overcome this problem, ring networks are used, since they allow to execute multiple operations in a row. But while several concepts and also prototypical implementations exist that realize such ring networks, the design process for them is still mainly conducted manually thus far. This is a severe drawback since various aspects, such as the dimensions of the channels, the effects of droplets, the used fluids, the volumetric flow rates inside the channels, etc., have to be considered for this purpose. In this article, we propose design automation methods which address this problem. The proposed solution will automatically generate a proper design as well as correspondingly needed droplet sequences. A case study demonstrates the applicability of the resulting methods and simulations confirms the validity of the proposed approach.

The derivation of suitable analytical models is an important step for the design and analysis of molecular communication systems. However, many existing models have limited applicability in practical scenarios due to various simplifications (e.g., assumption of an unbounded environment). In this paper, we develop a realistic model for particle diffusion in a bounded sphere and particle transport through a semi-permeable boundary. This model can be used for various applications, such as modeling of inter-/intra-cell communication or the release process of drug carriers. The proposed analytical model is based on a transfer function approach, which allows for fast numerical evaluation and provides insights into the impact of the relevant molecular communication system parameters. The proposed solution of the bounded spherical diffusion problem is formulated in terms of a state-space description and the semi-permeable boundary is accounted for by a feedback loop. Particle-based simulations verify the proposed modeling approach.

Communication systems have to cope with a variety of non-idealities. Among those, carrier frequency offset (CFO) induced impairments denote a challenging task for any system, but especially for multi-carrier schemes such as orthogonal frequency division multiplexing (OFDM). Unique Word-OFDM (UW-OFDM) is a known variant, which provides various performance benefits over conventional cyclic prefix (CP) based OFDM. In particular, UW-OFDM features excellent spectral sidelobe suppression properties and an outstanding bit error ratio performance. In this work, we expand the investigations to CFO impairments, analyze the varying effects experienced by different UW-OFDM realizations and compare it against conventional OFDM as well as single-carrier systems. Through an error analysis it is shown that compared to CP-OFDM, UW-OFDM has a significantly higher robustness against CFO. This advantage becomes even more dominant with an increasing CFO.

In stochastic computing (SC), a real-valued number is represented by a stochastic bit stream, encoding its value in the probability of obtaining a one. This leads to a significantly lower hardware effort for various functions and provides a higher tolerance to errors (e.g., bit flips) compared to binary radix representation. The implementation of a stochastic max/min function is important for many areas where SC has been successfully applied, such as image processing or machine learning (e.g., max pooling in neural networks). In this work, we propose a novel shift-register-based architecture for a stochastic max/min function. We show that the proposed circuit has significantly higher accuracy than state-of-the-art architectures for uncorrelated bit streams at comparable hardware costs. Moreover, we analytically proof the correctness of the proposed circuit and provide a new error analysis, based on the individual bits of the stochastic streams. Interestingly, the analysis reveals that for a certain practical bit stream length a finite optimal shift register length exists and it allows to determine the optimal length.

Microfluidic networks can be applied to droplet-based Lab-on-a-Chip devices, where droplets are used to confine samples which flow through closed microchannels along different paths in order to execute (bio-)chemical experiments. In order to allow this routing of droplets, the design of the microfluidic network has to be precisely defined and afterward fabricated. However, neither the fabrication process nor the applied materials and components are perfect and, therefore, the fabricated microfluidic device frequently contains defects (produced by fabrication tolerances, properties of the used material, or fluctuation of supply pumps). Those may have a severe impact on the behavior of the microfluidic network and can even render the network useless. Furthermore, these defects complicate the design process, which eventually results in a “trial-and-error”-approach causing high costs with respect to time and money. Consequently, designers want to anticipate how robust their design is against those defects. This article, for the first time, describes how these defects can be abstracted, which eventually allows to evaluate the robustness already in the design process. We additionally introduce models considering single and multiple defects as well as corresponding methods for their analysis. Evaluations on a microfluidic network which is used to screen drug compounds confirm that the resulting robustness analysis indeed provides designers with a simple metric to decide how sensitive their design is against defects. The models and methods proposed in this article are grounded on the established 1-D analysis model.

Droplet-based microfluidic devices are a well-established and highly potential Labs-on-Chip technology as droplets are especially suited to encapsulate biological samples like cells, proteins, or DNA. These droplets are injected in a continuous phase and flow through closed microchannels to modules executing operations on the droplets-eventually realizing a (bio-)chemical experiment. Moreover, this technology even allows for the realization of multiple experiments on a single device by letting droplets take different paths through the microfluidic network. This requires, however, a mechanism to route the droplets along these paths. To this end, the concept of passive droplet routing has been suggested which entirely avoids complex valves or switches and, instead, realizes the routing by exploiting the hydrodynamic effect that a droplet will always flow along the path with the highest volumetric flow rate. Since droplets themselves affect the volumetric flow rate, a dedicated sequence of droplets can define what path is taken and, hence, what experiment is executed. However, determining such a droplet sequence is a nontrivial task, as it is nonobvious how much droplets are needed, when to inject them, and how they are interacting. In this paper, we are addressing this issue by providing, for the first time, an automatic method for the generation of droplet sequences realizing the desired experiments on a given network. Evaluations confirm the practicability of the proposed solution. Moreover, the suitability of the obtained droplet sequences is additionally validated through simulations on the 1D analysis model.

In this work, we consider a bit-interleaved coded modulation unique word (UW) OFDM system with turbo equalization. For such a system, only a limited performance improvement over the turbo iterations was observed in a previous work. This is because interleaving was limited to one UW-OFDM symbol, which causes dependencies between the log-likelihood ratios exchanged between the equalizer and the decoder. To overcome this issue, we propose interleaving beyond one UW-OFDM symbol. Moreover, through bit error ratio simulations we determine the minimum number of UW-OFDM symbols across which interleaving should be performed to reach the matched filter bound (MFB).

The complexity of droplet microfluidics grows with the implementation of parallel processes and multiple functionalities on a single device. This poses a severe challenge to the engineer designing the corresponding microfluidic networks. In today’s design processes, the engineer relies on calculations, assumptions, simplifications, as well as his/her experiences and intuitions. To validate the obtained specification of the microfluidic network, usually a prototype is fabricated and physical experiments are conducted thus far. In case the design does not implement the desired functionality, this prototyping iteration is repeated—obviously resulting in an expensive and time-consuming design process. To avoid unnecessary debugging loops involving fabrication and testing, simulation methods could help to initially validate the specification of the microfluidic network before any prototype is fabricated. However, state-of-the-art simulation tools come with severe limitations, which prevent their utilization for practically relevant applications. More precisely, they are often not dedicated to droplet microfluidics, cannot handle the required physical phenomena, are not publicly available, and can hardly be extended. In this work, we present an advanced simulation approach for droplet microfluidics that addresses these shortcomings and, eventually, allows simulating practically relevant applications. To this end, we propose a simulation framework at the one-dimensional analysis model, which directly works on the specification of the design, supports essential physical phenomena, is publicly available, and is easy to extend. Evaluations and case studies demonstrate the benefits of the proposed simulator: While current state-of-the-art tools were not applicable for practically relevant microfluidic networks, the proposed simulator allows reducing the design time and costs, e.g., of a drug screening device from one person month and USD 1200, respectively, to just a fraction of that.

Two-phase flow microfluidics is a sophisticated and frequently applied Labs-on-Chip (LoC) technology as they allow to automatically conduct medical/biochemical experiments. In this technology, small volumes of reagents, so-called droplets, flow in an immiscible continuous flow inside closed channels making it particularly biocompatible. In the recent past, this technology was extended by a concept allowing to passively navigate droplets through the system-leading to so-called Networked Labs-on-Chips (NLoCs). After the design of an NLoC architecture which defines the comprising connectivity between components and, by this, how the considered medical/biochemical experiments are supposed to be realized, the question remains how to properly dimension the used components, i.e. especially how to dimension the used channels. However, this is a challenging task which is conducted manually thus far and frequently leads to specifications that do not work as intended. In this paper, we are addressing this issue by providing the designer with methods that allow to 1) automatically validate whether a chosen specification of an NLoC indeed works as intended and 2) automatically dimension NLoCs. Case studies demonstrate the importance and usefulness of the proposed methods for determining proper specifications of NLoCs.

In this paper, we consider diffusion-based molecular communication with and without drift between two static nano-machines. We employ type-based information encoding, releasing a single molecule per information bit. At the receiver, we consider an asynchronous detection algorithm which exploits the arrival order of the molecules. In such systems, transposition errors fundamentally undermine reliability and capacity. Thus, in this paper, we study the impact of transpositions on the system performance. Toward this, we present an analytical expression for the exact bit error probability (BEP) caused by transpositions and derive computationally tractable approximations of the BEP for diffusion-based channels with and without drift. Based on these results, we analyze the BEP when background is not negligible and derive the optimal bit interval that minimizes the BEP. Simulation results confirm the theoretical results and show the error and goodput performance for different parameters such as block size or noise generation rate.

Motivated by the numerous healthcare applications of molecular communication inside blood vessels of the human body, this paper considers multiple relay/cooperative nanomachine (CN)-assisted molecular communication between a source nanomachine (SN) and a destination nanomachine (DN) where each nanomachine is mobile in a diffusion-advection flow channel. Using the first hitting time model, the impact of the intermediate CNs on the performance of the aforementioned system with fully absorbing receivers is comprehensively analyzed taking into account the presence of various degrading factors, such as inter-symbol interference, multi-source interference, and counting errors. For this purpose, the optimal decision rules are derived for symbol detection at each of the CNs and the DN. Furthermore, closed-form expressions are derived for the probabilities of detection and false alarm at each CN and DN, along with the overall end-to-end probability of error and channel achievable rate for communication between the SN and DN. Simulation results are presented to corroborate the theoretical results derived and also to yield insights into the system performance under various mobility conditions.

Labs-on-Chips (LoCs) implement laboratory procedures on a single chip and are successfully used for chemical and biomedical applications. A promising and emerging realization of such chips are Networked LoCs (NLoCs) in which small volumes of fluids, so-called droplets, flow in closed channels of submillimeter diameters. NLoCs allow for an incubation and storage of assays over a long period of time and, hence, avoid evaporation and unwanted reactions. To increase the flexibility, effectiveness, and reusability, network functionalities allow to passively route droplets in channels and, hence, to dynamically select operations depending on the executed experiment. However, only manually designed architectures are considered for NLoCs thus far. They frequently suffer from large execution times and/or a high contamination of channels. To overcome these drawbacks, we propose the consideration of application-specific architectures for NLoCs. To this end, an automatic design method is proposed which, for a given set of experiments as well as constraints and objectives from the designer, is able to generate an optimized NLoC architecture realizing these experiments. Evaluations and case studies demonstrate the potential of the proposed solution for design exploration. Moreover, we are able to show that application-specific architectures are capable of realizing experiments in just a fraction of the time needed by architectures used thus far as well as with a substantially reduced contamination.

Labs-on-Chip integrate and minimize the functionality of complete conventional laboratories on a single chip. An upcoming and especially biocompatible realization are Networked Labs-on-Chips (NLoCs). In NLoCs, small volumes of reagents, so-called droplets, flow in an immiscible fluid in closed channels. An external pump applies a force to this immiscible fluid driving the droplets through the channels of the NLoC. However, the exact flow behavior of droplets in NLoCs physically depends on many factors and interdependencies. This makes it cumbersome to manually determine the taken path of a droplet and the time it needs to pass the NLoC. For the same reason, also almost no automated design solutions exist for NLoCs yet. In this work, we present a discrete model enabling designers and design automation tools to efficiently determine the droplets' path and positions. The precision of the proposed model is evaluated by a systematic examination for basic building blocks of NLoCs as well as for a complete architecture. The resulting model can be used for manual inspections of the droplets' behavior in an NLoC and, additionally, provides the basis for automated design solutions.

Labs-on-Chips (LoCs) revolutionize conventional biochemical processes and may even replace laboratories by integrating and minimizing their functionalities on a single chip. In a promising and emerging realization of LoCs, small volumes of reagents, so-called droplets, transport the biological sample and flow in closed channels of sub-millimeter diameters. This realization is called Networked Labs-on-Chips (NLoCs). The architecture of an NLoC defines different paths through which the droplets can flow. These paths are realized by splitting channels into multiple successor channels - so-called bifurcations. However, whether the architecture indeed allows to route droplets along the desired paths and, hence, correctly executes the intended experiment is not guaranteed. In this work, we present the first automatic solution for verifying whether an NLoC architecture allows to correctly route the droplets. Our evaluations demonstrate the applicability and importance of the proposed solution on a set of NLoC architectures.

On a droplet-based Labs-on-Chip (LoC) device, tiny volumes of fluids, so-called droplets, flow in channels of micrometer scale. The droplets contain chemical/biological samples that are processed by different modules on the LoC. In current solutions, an LoC is a single-purpose device that is designed for a specific application, which limits its flexibility. In order to realize a multi-purpose system, different modules are interconnected in a microfluidic network — yielding so-called Networked LoCs (NLoCs). In NLoCs, the droplets are routed to the desired modules by exploiting hydrodynamic forces. A well established topology for NLoCs are ring networks. However, the addressing schemes provided so far in the literature only allow to address multiple modules by re-injecting the droplet at the source every time, which is a very complex task and increases the risk of ruining the sample. In this work, we address this issue by revising the design of the network nodes, which include the modules. A novel configuration allows the droplet to undergo processing several times in cascade by different modules with a single injection. Simulating the trajectory of the droplets across the network confirmed the validity of our approach.

In this letter, we investigate the extrinsic log-likelihood ratio (LLR) computation of a soft-input soft-output equalizer used in a turbo equalization system. The optimum LLRs are obtained by a maximum a posteriori -based equalizer, which may be computationally expensive. Thus, several reduced-complexity equalizers have been proposed. The most promising approach first applies linear minimum mean square error filtering to the channel output and then computes the LLRs based on a scalar Gaussian approximation of the filter output. The resulting LLRs can be viewed as an approximation of the optimum LLRs. In order to improve the approximation, we investigate the computation of the LLRs based on a vector Gaussian approximation of the filter output, which incorporates the correlation between the estimated symbols after filtering. Surprisingly, it turns out that both approaches, although their derivation is different, give the same LLRs. We verify this remarkable result through an analytical proof and bit error ratio simulations.

Timestamp free clock synchronization in a master-slave network, i.e., synchronization where no timestamps are exchanged between the nodes, is considered. For highly accurate synchronization, a novel discrete-valued clock model is introduced. It is based on the observation that clocks are discrete counters in digital wireless radios. Considering this model, it is shown that the round-trip time (RTT) measurements follow specific pulse or step shaped functions. The estimated parameters of these RTT functions are used to determine the clock parameters (clock skew and phase) and the propagation delay. Numerical analysis illustrate that when RTT measurements are collected using discrete-valued clocks, the proposed estimation schemes outperform estimators derived from the continuous clock model, which is used in the state-of-the-art methods. Moreover, the presented scheme performs similar to recently presented discrete-valued clock approaches with more stringent hardware assumption. The correctness of the proposed models is validated through hardware experiments.

In this paper we investigate the hardware implementation of the subspace marginalization with interference suppression (SUMIS) detector using high-level synthesis (HLS). SUMIS is a promising detection approach for multiple-input multiple-output (MIMO) systems, due to its fixed computational complexity and well-defined tradeoff between complexity and performance. Based on a SystemC implementation, the Xilinx Vivado HLS tool is used to implement the SUMIS algorithm on a Virtex-7 field programmable gate array (FPGA). By defining different macro- and micro-architectures we propose three hardware designs of the SUMIS algorithm and compare them in terms of area, speed, and energy (design space exploration (DSE)). Our investigations reveal that hardware design using HLS is a viable approach for rapid prototyping and DSE.

In this paper we consider Generalized Pre-coding aided Spatial Modulation (GPSM), which was recently proposed as a promising alternative to conventional Multiple Input Multiple Output (MIMO) transmission schemes. In GPSM only a part of the receive antennas is activated with the aid of pre- coding at the transmitter. Hence, information bits are mapped to a spatial symbol, corresponding to a particular activation pattern, and to modulation symbols. Optimal performance is achieved with a Maximum Likelihood (ML) detector, but its exhaustive search leads to an intractable complexity. In this paper we present a novel detector, referred to as Soft MMSE with Exhaustive Search (SOMES) detector, that computes soft information for each symbol by employing a soft- output Minimum Mean Square Error (MMSE) detector. The soft information is used to determine the activation pattern using a small exhaustive search and to obtain the symbols in the particular activation pattern. Link level simulations show that the proposed algorithm possesses the near- optimal Bit Error Rate (BER) performance while achieving a remarkable reduction in complexity.

In this paper we consider a unique word OFDM (UW-OFDM) system with iterative detection, for which we explore two soft-input soft-output (SISO) detection algorithms: A LMMSE detector and a detector based on the generalized approximate message passing (GAMP) algorithm. For the GAMP based detector we propose additional simplifications suitable for UW-OFDM to further reduce the complexity while exhibiting only a small BER performance loss. As verified by computer simulations, both algorithms show a significant BER performance improvement over the iterations. Moreover, the GAMP based detector with the proposed simplifications outperforms the computational more complex LMMSE detector.

Accurate estimation of the doubly selective mobile radio channels is essential to achieve satisfactory performance in wideband wireless communication systems like 3GPP LTE. Pilot Assisted Channel Estimation (PACE) techniques like Linear Minimum Mean Squared Error (LMMSE), Least Square (LS) etc. are used to estimate the channel states at a certain time-frequency grid specified by the pilot symbols. A model like Basis Expansion Model (BEM) is used to track the doubly selective channel in time between the pilot symbols. BEM performance is sensitive to the PACE results. Imperfections in the PACE results are inevitable due to Inter Carrier Interference (ICI) at high speeds, changing signal to noise ratio (SNR) of the system and mismatch in the parameter values used in PACE calculations. We are able to reduce the effect of such imperfections by shaping the noise at the high frequencies in extended Fixed CE-BEM. Simulations show a decrease in MSE by a factor of ≈2 when the LMMSE results, obtained at 0dB SNR, are used to track the doubly selective channel at 350 km/h.

In this paper, we consider a bit-interleaved coded spatial multiplexing MIMO communication system over a frequency-selective MIMO channel. We present a factor-graph-based derivation of two different equalization algorithms. To this end, we propose a cycle-free factor graph representation of the equalizer, to which we apply the sum-product algorithm (SPA). By using different message representations in the SPA, it is shown that the resulting equalization algorithms correspond to the optimal MAP equalizer and the low-complexity LMMSE equalizer, respectively. Both algorithms can be used in turbo processing and we demonstrate that after 3 iterations the BER performance of the LMMSE equalizer is similar to that of the MAP equalizer.

The main targets for the next generation of mobile communication systems, with LTE as a main candidate, are increased data rates and improved spectrum efficiency. The transmission scheme for the LTE downlink is OFDM which enables the design of low complexity and high performance receivers, if the channel is time-invariant. However in high-mobility scenarios, where the channel is time-variant, the receiver design is more challenging. Therefore in this paper the turbo equalizer presented in [1] is extended to 16-QAM and 64-QAM to compensate the effect of the time-varying channel. Furthermore an iterative pilot-assisted channel estimator based on DPS-BEM is presented. BER performance of the LTE downlink for the ITU Vehicular A channel shows that the improvement due to iterative processing is substantial.

In future wireless communication systems like LTE, wide bandwidths for the transmission of high data rates result in frequency selective mobile radio channels. If data transmission takes place at high mobile speeds the mobile radio channel becomes also time selective. In this paper we propose for the estimation of such double selective channels a modification to the well known complex exponential basis expansion model, which is based on the assumed knowledge of the maximum Doppler frequency at the mobile user. We are able to reduce the estimation error by an order of magnitude up to very high mobile speeds for the OFDMA-based LTE downlink.