Multi-access Edge Computing (MEC) has become inevitable in future communication networks due to its pivotal role in latency critical and distributed services. New emerging services seek to further push the concept of MEC to user environments for localized, private and even quicker processing of information. Micro MEC (µMEC) fulfills such needs of emerging services. In this article, we provide an overview of the security landscape of the µMEC philosophy and technologies. Security challenges, potential solutions for those challenges are discussed and the missing gaps are highlighted to stir further research in this direction.

For the analysis of complex multi-layered networks, often the evaluation of simulation tools is needed. Different simulation tools provide different Open Systems Interconnection (OSI) layer capabilities. Some of the tools might be integrated with others for detailed system analysis. In this work, we first provide a comprehensive review of some important tools for the satellite-terrestrial networks. We highlight the benefits and disadvantages of each simulation tool. Moreover, we provide a use case scenario that is analysed using a combination of tools. Conclusions on how the combined simulation tools can be treated in the contexts of multi-layered networks are provided.

Small cells are regarded as a promising solution for improving the coverage and spectral efficiency of the fifth generation (5G) cellular networks. Small cell networks utilize low transmission power levels and thus, are appropriate for deploying full-duplex (FD) technology. In this paper, a hybrid full/half duplex (FD/HD) small cell network exploiting fractional frequency reuse (FFR) and fractional power control (FPC) for interference mitigation is considered. Theoretical expressions for the coverage probability and the average sum-rate in uplink and downlink directions are derived for systems that make use of conventional frequency reuse, hard FFR, and soft FFR. The impact of various system parameters, such as power control parameter, small base station (SBS) density, and self-interference cancellation (SIC) parameters on system performance are investigated. The numerical experiments show that SBSs tend to operate in FD mode for large values of SBS density and SIC level. Also, interestingly, the optimal sum-rate is achieved when all SBSs operate either in FD or in HD mode. Finally, it is observed that exploiting FFR techniques in FD networks making use of FPC, significantly improves downlink coverage probability.

The integration of satellite-terrestrial networks is beneficial in terms of the increase of the network capacity and coverage. In such a heterogeneous network, highly efficient spectrum utilization is extremely important. This could be achieved by the single frequency reuse which allows increasing the capacity at the cost of increased interference. Interference is one of the main parameters that limits the link-level performance in such a network. In this paper, we examine the frequency reuse scenario by analyzing the impact of terrestrial interference to the uplink of a low Earth orbiting (LEO) satellite constellation in the high International Mobile Telecommunications (IMT) frequency bands. To this end, we propose a novel stochastic geometry based analytical framework that is able to accommodate various aspects of realistic satellite networks. The accuracy of the analysis is verified by using advanced simulation tools.

In this paper, a new approach, based on the generalized triangular decomposition (GTD), is proposed for simultaneous wireless information and power transfer (SWIPT) in the spatial domain for a point-to-point multiple-input-multiple-output (MIMO) system. The approach exploits the structure of the GTD to allow the transmitter to use each of the strongest subchannels jointly for energy harvesting and information exchange while the receiver has the ability to harvest energy or decode information from each different received subchannel. An optimal solution is developed in the paper for SWIPT based on GTD that jointly obtains the optimal subchannels assignment and minimizes the total transmitted power for given information rate and energy harvesting constraints under a limited available power per each subchannel at the transmitter. Both the theoretical and numerical results presented in this paper show that our proposed approach significantly outperforms the state of the art spatial domain SWIPT systems based on the singular value decomposition (SVD).

In this paper, we investigate the downlink throughput performance of a massive multiple-input multiple-output system that employs superimposed pilots for channel estimation. The component of downlink (DL) interference that results from transmitting data alongside pilots in the uplink (UL) is shown to decrease at a rate proportional to the square root of the number of antennas at the BS when the least-squares channel estimate is employed in a matched-filter precoder. The normalized mean-squared error (NMSE) of the channel estimate is compared with the Bayesian Cramér-Rao lower bound that is derived for the system, and the former is also shown to diminish with increasing number of antennas at the base station. Furthermore, we show that staggered pilots are a particular case of superimposed pilots and offer the downlink throughput of superimposed pilots while retaining the UL spectral and energy efficiency of regular pilots. We also extend the framework for designing a hybrid system, consisting of users that transmit either regular or superimposed pilots, to minimize both the UL and DL interference. The improved NMSE and DL rates of the channel estimator based on superimposed pilots are demonstrated by means of simulations.

In massive multiple-input multiple-output (MIMO) systems, superimposed (SP) and time-multiplexed (TM) pilots exhibit a complementary behavior, with the former and latter schemes offering a higher throughput in high and low inter-cell interference scenarios, respectively. Based on this observation, in this paper, we propose an algorithm for partitioning users into two disjoint sets comprising users that transmit TM and SP pilots. This selection of user sets is accomplished by minimizing the total inter-cell and intra-cell interference, and since this problem is found to be non-convex, a greedy approach is proposed to perform the partitioning. Based on simulations, it is shown that the proposed method is versatile and offers an improved performance in both high and low-interference scenarios.

Superimposed pilots are proposed as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output systems. Provided that the uplink duration is larger than the total number of users in the system, superimposed pilots enable each user to be assigned a unique pilot sequence, thereby allowing for a significant reduction in pilot contamination. Channel estimation performance in the uplink is further improved using an iterative data-aided algorithm. Based on approximate expressions for the uplink signal-to-interference-plus-noise ratio, it is shown that superimposed pilots provide a better performance when compared with methods that use time-multiplexed data and pilots. Numerical simulations are used to validate the approximations and the improved performance of the proposed method.

This paper studies the asymptotic performance of multi-hop amplify-and-forward relay multiple-antenna communication channels. Each multi-antenna relay terminal in the considered network amplifies the received signal, sent by a source, and retransmits it upstream toward a destination. Achievable ergodic rates of the relay channel with both jointly optimal detection and decoding and practical separate-decoding receiver architectures for arbitrary signaling schemes, along with average bit error rates for various types of detectors are derived in the regime where the number of antennas at each terminal grows large without a bound. To overcome the difficulty of averaging over channel realizations, we apply a large-system analysis based on the replica method from statistical physics. The validity of the large-system analysis is further verified through Monte Carlo simulations of realistic finite-sized systems.

The performance of regularized least-squares estimation in noisy compressed sensing is analyzed in the limit when the dimensions of the measurement matrix grow large. The sensing matrix is considered to be from a class of random ensembles that encloses as special cases standard Gaussian, row-orthogonal, geometric, and so-called T-orthogonal constructions. Source vectors that have non-uniform sparsity are included in the system model. Regularization based on ℓ-norm and leading to LASSO estimation, or basis pursuit denoising, is given the main emphasis in the analysis. Extensions to ℓ-norm and zero-norm regularization are also briefly discussed. The analysis is carried out using the replica method in conjunction with some novel matrix integration results. Numerical experiments for LASSO are provided to verify the accuracy of the analytical results. The numerical experiments show that for noisy compressed sensing, the standard Gaussian ensemble is a suboptimal choice for the measurement matrix. Orthogonal constructions provide a superior performance in all considered scenarios and are easier to implement in practical applications. It is also discovered that for non-uniform sparsity patterns, the T-orthogonal matrices can further improve the mean square error behavior of the reconstruction when the noise level is not too high. However, as the additive noise becomes more prominent in the system, the simple row-orthogonal measurement matrix appears to be the best choice out of the considered ensembles.

Hardware non-idealities in wireless transmitter electronics cause distortion that is not captured by conventional linear channel models; in fact, error-vector magnitude (EVM) measurements in conformance testing conceptually reduce their collective effect to an additive noise component at each subcarrier. Motivated by the EVM, the present paper considers a `binoisy' multiple-input multiple-output (MIMO) channel model where the additional non-idealities manifest themselves as an additive distortion noise term at the transmit side. Through this extended MIMO relation, the effects of hardware impairments on the achievable rates of different digital modulation schemes are studied via large system analysis. The numerical results illustrate how tolerable EVM levels depend non-trivially on various factors, including the signal-to-noise ratio, modulation order and the level of asymmetry in antenna array configurations.

Hardware impairments in radio-frequency components of a wireless system cause unavoidable distortions to transmission that are not captured by the conventional linear channel model. In this paper, a “binoisy” single-user multiple-input multiple-output (SU-MIMO) relation is considered where the additional distortions are modeled via an additive noise term at the transmit side. Through this extended SU-MIMO channel model, the effects of transceiver hardware impairments on the achievable rate of multi-antenna point-to-point systems are studied. Channel input distributions encompassing practical discrete modulation schemes, such as, QAM and PSK, as well as Gaussian signaling are covered. In addition, the impact of mismatched detection and decoding when the receiver has insufficient information about the non-idealities is investigated. The numerical results show that for realistic system parameters, the effects of transmit-side noise and mismatched decoding become significant only at high modulation orders.

The present paper studies the asymptotic performance of dual-hop amplify-and-forward multiple-input multiple-output relay communication systems. In the corresponding setup, a relay amplifies the signal received from a source, retransmitting it towards a destination, while the direct source-destination link is absent. Ergodic achievable rates under separate decoding, along with the average bit error rate under various detection schemes are derived in the regime where the number of antennas at each terminal grows without bound. To overcome the mathematical difficulty of averaging over both channel realizations and input signals we apply large-system analysis based on the replica method from statistical physics. The validity of the large-system analysis is further verified through Monte Carlo simulations, providing particularly good accuracy at low SNR.

A signal recovery scheme is developed for linear observation systems based on expectation consistent (EC) mean field approximation. Approximate message passing (AMP) is known to be consistent with the results obtained using the replica theory, which is supposed to be exact in the large system limit, when each entry of the observation matrix is independently generated from an identical distribution. However, this is not necessarily the case for general matrices. We show that EC recovery exhibits consistency with the replica theory for a wider class of random observation matrices. This is numerically confirmed by experiments for the Bayesian optimal signal recovery of compressed sensing using random row-orthogonal matrices.

Performance of regularized least-squares estimation in noisy compressed sensing is studied in the limit when the problem dimensions grow large. The sensing matrix is sampled from the rotationally invariant ensemble that encloses as special cases the standard IID and row-orthogonal constructions. The analysis is carried out using the replica method in conjunction with some novel matrix integration results. The numerical experiments show that for noisy compressed sensing, the standard IID ensemble is a suboptimal choice for the measurement matrix. Orthogonal constructions provide a superior performance in all considered scenarios and are easier to implement in practice.

MOTIVATION: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. As the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically time-consuming in a desktop computing environment. RESULTS: Using sparsity enforcing methods from the general sparse signal processing field (such as compressed sensing), we derive a solution to the community composition estimation problem by a simultaneous assignment of all sample reads to a pre-processed reference database. A general statistical model based on kernel density estimation techniques is introduced for the assignment task, and the model solution is obtained using convex optimization tools. Further, we design a greedy algorithm solution for a fast solution. Our approach offers a reasonably fast community composition estimation method, which is shown to be more robust to input data variation than a recently introduced related method. AVAILABILITY AND IMPLEMENTATION: A platform-independent Matlab implementation of the method is freely available at http://www.ee.kth.se/ctsoftware; source code that does not require access to Matlab is currently being tested and will be made available later through the above Web site.

Presence of multiple antennas on both sides of a communication channel promises significant improvements in system throughput and power efficiency. In effect, a new class of large multiple-input multiple-output (MIMO) communication systems has recently emerged and attracted both scientific and industrial attention. To analyze these systems in realistic scenarios one has to include such aspects as co-channel interference, multiple access and spatial correlation. In this paper, we study the properties of correlated MIMO multiple-access channels in the presence of external interference. Using the replica method from statistical physics, we derive the ergodic sum-rate of the communication for arbitrary signal constellations when the numbers of antennas at both ends of the channel grow large. Based on these asymptotic expressions, we also address the problem of sum-rate maximization using statistical channel state information and linear precoding. The numerical results demonstrate that when the interfering terminals use discrete constellations, the resulting interference becomes easier to handle compared to Gaussian signals. Thus, it may be possible to accommodate more interfering transmitter-receiver pairs within the same area as compared to the case of Gaussian signals. In addition, we demonstrate numerically for the Gaussian and QPSK signaling schemes that it is possible to design precoder matrices that significantly improve the achievable rates at low-to-mid range of signal-to-noise ratios when compared to isotropic precoding.

The present paper investigates the achievable data rates of multi-hop amplify-and-forward multi-antenna relay channels with arbitrary number of hops K. Each multi-antenna terminal in the system amplifies the received signal and retransmits it upstream. To analyze the ergodic end-to-end mutual information of the system, one has to perform averaging over the fading coefficients. To overcome this difficulty we apply large-system analysis, based on the assumption that the number of antennas grows without bound at every terminal. Using the replica method, we derive an explicit asymptotic expression for the ergodic mutual information between the input and output of the K-hop channel with no restrictions on the channel inputs. Numerical results support the validity of the replica analysis and show that the result is tight even for small antenna arrays.

Base station cooperation in cellular networks has been recently recognized as a key technology for mitigating interference, providing thus significant improvements in the system performance. In this paper, we consider a simple scenario consisting of two one-dimensional cells, where the base stations have fixed locations, while the user terminals are randomly distributed on a line. Exploiting the replica method from statistical physics, we derive the ergodic sum-rate under arbitrary signaling for both cooperative and non-cooperative scenarios, when the system size grows large. The obtained results are analytically tractable and can be used to optimize the system parameters in a simple manner. The numerical examples show that the analysis provides good approximations for finite-sized systems.

Bidirectional wireless communication between two multi-antenna terminals that operate in a full-duplex mode is considered. The effects of loopback self-interference and transmit-side noise due to radio front-end imperfections are analyzed in the asymptotic regime where the number of antennas at both terminals grows large. The study compares two transparent mitigation schemes by which conventional spatial multiplexing can operate without being aware of the self-interference issue: spatial-domain null-space projection and time-domain subtractive cancellation. Both schemes eliminate the information-bearing part of the self-interference perfectly, but due to transmitter imperfections, the cancellation approach suffers from residual self-interference. The receive-side null-space projection, on the other hand, suppresses also the transmit-side distortion, but at the cost of reduction in the degrees of freedom available for data reception. The results indicate that suppression becomes preferred when the level of transmit-side noise increases and the receiver does not have instantaneous knowledge about the spatial structure of the self-interference. Exhaustive Monte Carlo simulations also show that the asymptotic analytical results are useful for evaluating the performance of practical not-so-large systems.

A subspace method for channel estimation is proposed for asymmetric antenna array systems. The so-called pilot contamination problem reported in [1] is found to be due to the linearity of channel estimation in [2]. We show that it does not occur in cellular systems with power control and power-controlled handoff when the nonlinear channel estimation method proposed in this paper is used. Power-control hand-off is needed to guarantee separability between signal and interference subspaces. We derive the transmission conditions for subspace separability based on free probability and perturbation theory.

Iterative channel estimation and decoding based on probabilistic bias is investigated. In order to control the occurrence probability of transmitted symbols, biased convolutional codes (CCs) are proposed. A biased CC is obtained by puncturing the parity bit of a conventional (unbiased) CC and by inserting a fixed bit at the punctured position when the state is contained in a certain subset of all possible states. A priori information about the imposed bias is utilized for the initial linear minimum mean-squared error (LMMSE) channel estimation. This paper focuses on biased turbo codes that are constructed as the parallel concatenation of two biased CCs with interleaving, and proposes an iterative LMMSE channel estimation and decoding scheme based on approximate belief propagation. The convergence property of the iterative LMMSE channel estimation and decoding scheme is analyzed via density evolution (DE). The DE analysis allows one to design the magnitude of the bias according to the coherence time, in terms of the decoding threshold. The proposed scheme is numerically shown to outperform conventional pilot-based schemes in the moderate signal-to-noise ratio (SNR) regime, at the expense of a performance degradation in the high SNR regime.

Training-based transmission over Rayleigh block-fading multiple-input multiple-output (MIMO) channels is investigated. As a training method a combination of a pilot-assisted scheme and a biased signaling scheme is considered. The achievable rates of successive decoding (SD) receivers based on the linear minimum mean-squared error (LMMSE) channel estimation are analyzed in the large-system limit, by using the replica method under the assumption of replica symmetry. It is shown that negligible pilot information is best in terms of the achievable rates of the SD receivers in the large-system limit. The obtained analytical formulas of the achievable rates can improve the existing lower bound on the capacity of the MIMO channel with no channel state information (CSI), derived by Hassibi and Hochwald, for all SNRs. The comparison between the obtained bound and a high-SNR approximation of the channel capacity, derived by Zheng and Tse, implies that the high-SNR approximation is unreliable unless quite high SNR is considered. Energy efficiency in the low-SNR regime is also investigated in terms of the power per information bit required for reliable communication. The required minimum power is shown to be achieved at a positive rate for the SD receiver with no CSI, whereas it is achieved in the zero-rate limit for the case of perfect CSI available at the receiver. Moreover, numerical simulations imply that the presented large-system analysis can provide a good approximation for not so large systems. The results in this paper imply that SD schemes can provide a significant performance gain in the low-to-moderate SNR regimes, compared to conventional receivers based on one-shot channel estimation.

In this paper we consider a scenario, where several mobile multi-antenna terminals communicate with a multi-antenna base station within a cellular communication system over the flat Rayleigh fading channel. In addition, several terminals from the neighboring cell cause interference. For such a scenario, we derive, using the replica method, the asymptotic sum-rate of the communication in the large-system limit for arbitrary signal constellations. Moreover, we show via numerical results that when the interfering terminals use a QPSK constellation, the resulting interference becomes easier to handle. In effect, we may be able to accumulate more interfering transmitter-receiver pairs within the same area as compared to the case of Gaussian signals. Monte-Carlo simulations show that the derived asymptotic expression matches well with the simulated values even for small numbers of antennas.

The sparse representation problem of recovering an N dimensional sparse vector x from M1-norm of x under the constraint y = Dx. In this paper, the performance of l1-reconstruction is analyzed, when the dictionary is bi-orthogonal D = [O1O2], where O1, O2are independent and drawn uniformly according to the Haar measure on the group of orthogonal M × M matrices. By an application of the replica method, we obtain the critical conditions under which perfect l1-recovery is possible with bi-orthogonal dictionaries.

This paper studies the asymptotic sum-rate of the primary network within the relay-assisted multi-antenna cognitive radio system performing amplify-and-forward relaying. The achievable sum-rates are derived in the large-system limit by means of the replica method. A closed-form expression for the sum-rate of the primary network is obtained for large antenna arrays as a function of parameters obtained by solving a set of fixed-point equations. Numerical simulations confirm the validity of the results even for systems with only a few antennas at each terminal.

This paper presents a large-system analysis of the performance of joint channel estimation, multiuser detection, and per-user decoding (CE-MUDD) for randomly-spread multiple-input multiple-output (MIMO) direct-sequence code-division multiple-access (DS-CDMA) systems. A suboptimal receiver based on successive decoding in conjunction with linear minimum mean-squared error (LMMSE) channel estimation is investigated. The replica method, developed in statistical mechanics, is used to evaluate the performance in the large-system limit, where the number of users and the spreading factor tend to infinity while their ratio and the number of transmit and receive antennas are kept constant. The performance of the joint CE-MUDD based on LMMSE channel estimation is compared to the spectral efficiencies of several receivers based on one-shot LMMSE channel estimation, in which the decoded data symbols are not utilized to refine the initial channel estimates. The results imply that the use of joint CE-MUDD significantly reduces rate loss due to transmission of pilot signals, especially for multiple-antenna systems. As a result, joint CE-MUDD can provide significant performance gains, compared to the receivers based on one-shot channel estimation.

A novel signaling scheme for iterative channel estimation and data decoding is proposed. In the proposed scheme, the occurrence probability of transmitted symbols is biased. A priori information about the bias is utilized for the initial channel estimation. The proposed scheme is based on parallel concatenation of two biased convolutional codes (BCCs), which are constructed as systematic recursive convolutional codes with state-dependent puncturing. The BCCs can be regarded as a joint coding scheme that determines the insertion positions of pilot symbols according to information bits. The proposed scheme is numerically shown to outperform conventional pilot-based schemes in the waterfall region, while it is inferior to the conventional schemes in the error-floor region.

The maximum traffic arrival rate at the network for a given delay guarantee (delay constrained throughput) has been well studied for wired channels. However, few results are available for wireless channels, especially when multiple antennas are employed at the transmitter and receiver. In this work, we analyze the network delay constrained throughput of a multiple input multiple output (MIMO) wireless channel with time-varying spatial correlation. The MIMO channel is modeled via its virtual representation, where the individual spatial paths between the antenna pairs are Gilbert-Elliot channels. The whole system is then described by a K-State Markov chain, where K depends upon the degree of freedom (DOF) of the channel. We prove that the DOF based modeling is indeed accurate. Furthermore, we study the impact of the delay requirements at the network layer, violation probability and the number of antennas on the throughput under different fading speeds and signal strength.

Minimum mean square error (MMSE) estimation of block sparse signals from noisy linear measurements is considered. Unlike in the standard compressive sensing setup where the non-zero entries of the signal are independently and uniformly distributed across the vector of interest, the information bearing components appear here in large mutually dependent clusters. Using the replica method from statistical physics, we derive a simple closed-form solution for the MMSE obtained by the optimum estimator. We show that the MMSE is a version of the Tse-Hanly formula with system load and MSE scaled by a parameter that depends on the sparsity pattern of the source. It turns out that this is equal to the MSE obtained by a genie-aided MMSE estimator which is informed in advance about the exact locations of the non-zero blocks. The asymptotic results obtained by the non-rigorous replica method are found to have an excellent agreement with finite sized numerical simulations.

The large system analysis of randomly spread MIMO DS-CDMA systems is provided. Correlated Rayleigh fading MIMO channels are assumed for all users. Linear multiuser detection with separate decoding and pilot-aided channel estimation are used. The results imply that with channel estimation, the performance can improve significantly as the correlation between the transmit antennas increases. No channel information at the transmitter is required, but the channel estimator needs knowlegde of the long term transmit correlation in advance. The numerical results demonstrate that in a 4 × 4 MIMO DS-CDMA system with two users per chip, high antenna correlation at the transmitter can double the ergodic spectral efficiency compared to the case of uncorrelated transmit antennas.

A Rayleigh block-fading multiple-input multiple-output (MIMO) channel with channel state information (CSI) available neither to the transmitter nor to the receiver is considered. A lower bound on the capacity is formulated based on a successive decoding (SD) scheme. An analytical expression of the lower bound is derived in the large-system limit, by using the replica method. Furthermore, the achievable rate of the linear minimum mean-squared error (LMMSE) receiver with SD is also evaluated in the large-system limit. The lower bound is superior to the lower bound derived by Hassibi and Hochwald for all signal-to-noise ratios (SNRs).

We consider multi-antenna systems utilizing groupwise layered space-time coding. Simple, yet accurate approximations for the probability density function (PDF) of the signalto- interference-plus-noise ratio (SINR) at the output of a linear zero-forcing (ZF) and minimum mean square error (MMSE) data estimators are presented. It is shown that the derived PDFs are exact in the limit of large number of receive antennas. We also demonstrate via numerical examples that the approximations are accurate already for relatively small numbers of receive antennas. Closed form expressions in terms of some well-known special functions for the uncoded bit error probability as well as frame error probability (FEP) of turbo-like coded system with iterative sum-product based decoder are derived. For comparison, we also give analytical lower bounds for the FEP of a coded multiantenna system utilizing conventional spatial multiplexing and linear ZF or MMSE based estimator at the receiver.

This paper studies large randomly spread direct-sequence code-division multiple-access system operating over a block fading multipath channel. Channel knowledge is obtained by a linear estimator whose initial decisions are iteratively refined by using a soft feedback from the single-user decoders. In addition to the traditional training symbol based signaling scheme, we study a novel method that utilizes a random bias in the symbol probabilities of the transmitted signal to construct the initial channel estimates. The numerical results suggest that in the large system limit, appropriate selection of the channel code and signaling method allows for successful communication with vanishing training overhead in overloaded systems if iterative channel and data estimation is performed at the receiver.

We propose a randomly-biased quadrature phase shift keying (QPSK) signaling scheme for a noncoherent Rayleigh block-fading multiple-input multiple-output (MIMO) channel. In order to optimize a prior of bias, we evaluate a lower bound of the spectral efficiency of the noncoherent MIMO channel with randomly-biased QPSK signaling in the large-system limit, by using the replica method. Our main result is that randomly-biased QPSK signaling with vanishing bias is optimal in the large-system limit for any signal-to-noise ratio.

The large system analysis of a randomly spread direct-sequence code-division multiple-access system operating over a frequency-selective fading channel is considered. Iterative multiuser detection and decoding based on generalized posterior mean estimation and interference cancellation is assumed. The channel is mismatched and provided by a linear estimator whose initial pilot-based decisions are iteratively refined by using a feedback from the single-user decoders. By an application of the replica method, a tool from statistical physics, and density evolution with Gaussian approximation, we show that the performance metrics of the considered multiuser system converge in distribution at the large system limit to that of a simple single-user system operating over a flat fading channel. We also give the exact result of the hard decision feedback based channel estimator analyzed approximately by Li et al. (2007).

We propose a novel signaling scheme for wireless communication systems without channel state information (CSI). In that scheme, a bias of the occurrence probabilities of constellation points is utilized as pilot information known to the receiver, whereas pilot signals known to the receiver are sent in conventional pilot-based approaches. We evaluate the performance of the new scheme and conventional pilot-based schemes for a large direct-sequence code-division multiple-access (DS-CDMA) system, by using the replica method. It is shown that the new scheme outperforms the conventional pilot-based scheme when the amount of pilot information is large.

We study the spectral efficiency of large random direct-sequence code-division multiple-access systems utilizing linear minimum mean square error (LMMSE) channel estimation and iterative multiuser detection and decoding (MUDD). Iterative MUDD based on non-linear data estimation and single-user decoding is considered as a benchmark for the more practical iterative LMMSE data estimator with soft parallel interference cancellation. The results showed that the channel parameters and the choice of error correction code have a great impact on the achievable spectral efficiency. It was also found that for the considered setups, the iterative LMMSE based channel estimator is near optimal for slowly time-varying multipath fading channels.

We consider impacts of channel estimation errors on performance of general multiuser detectors in MIMO DS-CDMA channels. We evaluate their performance in terms of asymptotic spectral efficiency, which is obtained via decoupling structure, by using the replica method. Numerical results imply that the performance of LMMSE detection is very close to that of MMSE detection for small system loads. Furthermore, we find that the spectral efficiency of MMSE detection grows discontinuously with the length of pilot sequences for large system loads, and that the critical length is close to the optimal length. While it is indistinguishable from that of LMMSE detection for short pilot sequences, the gap between the two is significantly large if the length of pilot sequences is longer than the critical length.

We present an asymptotically (in number of receive antennas) exact probability density function for the signal-to-noise-plus-interference ratio (SINR) of the output of a zero-forcing (ZF) estimator in a multi-antenna system utilizing groupwise layered space-time (GLST) transmission. We show that the approximation is extremely accurate also for finite and relatively small numbers of receive antennas. Closed form expression for the uncoded bit error probability (BEP) is given in the case of binary or quadrature phase shift keying (BPSK/QPSK). Frame error probability (FEP) lower bound for turbo-like codes and iterative sum-product based decoder is presented.

In this paper we propose an information theoretic framework for studying coding and throughput optimization for multi-layered packet transmission systems. Our approach assumes that the system is divided into two separate layers: One code word forms a packet at the physical layer and the code at the network layer spans over these packets. At the receiver, the network layer assumes that the decoded packets arriving from the physical layer either have no errors or are marked as deleted. Thus, albeit the packet loss may be caused, for example, by decoding error, congestion or channel conditions, the network layer treats all decoding errors as erasures regardless of the cause. This allows us to view the system at the network layer as transmission over memoryless erasure channel. We study the throughput optimization and code design across the layers under a total code length constraint while taking also into account the network layer imperfections in the transmission. We use random coding error exponents to achieve results that do not depend on specific coding scheme used. The proposed scheme provides also means for investigating important physical layer phenomena, such as, channel model and lower layer error correction coding in the packet erasure models. Our approach extends to fading channels and networks of multiple nodes and by viewing the two layers of coding as a concatenated coding scheme, a comparison between layer-by-layer and joint cross-layer rate optimization can be made, as outlined in this paper

For the single-user communications pragmatic yet powerful methods known as layered space-time (LST) architectures provide means to increase the user data rate of a multiple-input multiple-output (MIMO) antenna system dramatically. To achieve better error rate performance, the LST transmission can be further accompanied with forward error correction coding. Future wireless communication systems, however, require high data rates also in multiuser environments. For broadband multiuser wireless communications, multicarrier code-division multiple-access (MC-CDMA) has emerged as an attractive technique due to its low equalization complexity and robust performance in multipath fading channels. Utilizing strong channel coding and LST architectures with MC-CDMA, high spectral efficiency and good error rate performance can be obtained in diverse environments. In this paper, single and multi-antenna turbo coded downlink MC-CDMA is combined with the concept of LST architectures. Due to the inevitable complexity restrictions, a suboptimal receiver interface for the underlying system is designed and its performance is evaluated in fading channels. The results demonstrate that significant improvement in both error rate performance and the system throughput can be achieved also with multiuser communications by using these MIMO techniques.

We evaluate the performance of single- and multi-antenna multi-carrier code division multiple access (MC-CDMA) downlink (base station to mobile terminal) systems in multi-cell environments. We first propose a minimum mean square error (MMSE) filter for a single input single output (SISO) MC-CDMA downlink system. Then, we apply it to a SIMO (single input multiple output) system with a conventional turbo coding. Furthermore, we compare the performance of SISO (1 /spl times/ 1) and SIMO (1 /spl times/ 2) MC-CDMA systems with that of a multiple input multiple output (MIMO) (2 /spl times/ 2) system employing space-time turbo coded modulation (STTuCM) in a multi-cell environment with 7 cells by computer simulation. Based on the computer simulation results, it is found that the considered MIMO system can achieve twofold capacity with the same transmission power in the multi-cell environment.