Resource Allocation (RA) is one of the crucial elements in Long Term Evolution (LTE) networks, which has a tremendous impact on the network performance. However, complexity of the RA problem increases when both Carrier Aggregation (CA) and Multiple-Input Multiple-Output (MIMO) are enabled in the network. CA is a key functionality for LTE- Advanced (LTE-A) that enables Mobile Networ Operators (MNOs) to create large virtual carrier bandwidths for data hungry applications by aggregating available Component Carriers (CCs). In this paper, Resource Block (RB), Secondary CC (SCC), MIMO Transmission Mode (TM), and Modulation and Coding Scheme (MCS) selection is formulated jointly as an Integer Linear Programming (ILP). We prove that intractable integrality of the optimisation problem can be removed and relaxed to the Linear Programming (LP). Solving the LP obtains an optimal solution for the original ILP.

Mathematical analysis, supported by computer simulations is used to find theoretically the maximum number of co-channel interfering tiers that becomes active at microwave carrier frequencies greater than 2 GHz and Millimeter Wave (mmWave). Information capacity measure based on the uplink of the cellular wireless system is used for the analysis. Results show that at microwave carrier frequencies greater than 2GHz, the first 10 co-channel interfering tiers becomes active. Whereas for mmWave carrier frequencies more than 10 co-channel interfering tiers becomes active. Our results show that the number of active co-channel interfering tiers depends on the carrier frequency and the cell size radius.

Carrier Aggregation (CA) is a key functionality for next generation wireless communication systems that enables operators to create larger virtual carrier bandwidths for data hungry applications by aggregating available Component Carriers (CCs). In this paper, the Resource Allocation (RA) optimisation problem in Orthogonal Frequency Division Multiplexing (OFDM) based wireless systems is studied with CA. We show that RA with CA can be formulated as a complex mixed integer programming optimisation problem under the practical constraints of transmit power and limited number of CCs available for each user. A two-step solution is proposed, Step 1: CC allocation is performed to satisfy users CA constraints and remove the intractable integer constraints of the optimisation problem, Step 2: power and SubCarrier (SC) allocation algorithm assigns resources among users sub-optimally. We propose two efficient methods based on our two-step solution with reasonably low complexity refer as Two-Step RA Algorithm (TSRAA) and Efficient RA Algorithm (ERAA). The performance of TSRAA and ERAA are compared with the highly complex Combinatorial Algorithm (COA) introduced as an optimal solution. Simulation results verify that TSRAA reaches to optimum solution and exploits the multi-CC diversity of wireless systems. Furthermore, numerical results show that ERAA achieves satisfying performance with very low complexity.

In this paper a new method is proposed for classifying randomly deployed sensor nodes over an area of interest into distinctive categories. The problem of event region and event boundary detection in Wireless Sensor Networks (WSNs) is addressed. Particularly, analysis is provided for a scenario whereby an area of interest featuring two distinctive phenomena is being monitored with a randomly deployed network of wirelessly connected sensor nodes. Each sensor node in the network is asked to acknowledge whether or not it classifies itself as an event-region node based only on its own environment reading. The key decision factor employed in this approach is the statistical attributes of received signal distribution at each sensor node. Applying this algorithm results in reducing the required bandwidth for transmitting the environmental reading to the base station to be proportional to the size of the event-region. This is opposed to other approaches where the required bandwidth is proportional to the size of the entire network.

In the research of time varying communication channels, signal amplitude attenuation is considered to be the main cause of channel varying memories. The Gilbert-Elliot channel has been used to model this kind of memories for a long time with a decision-feedback decoder or an equivalent genie-aided decoder being its optimal decoders. In the first part of this paper, we show that the decoders have not performed optimal signal detection for the channel. The expression of the achievable mutual information rate by the genie-aided decoder has been revisited and updated recently. The non-optimality of the decoders can be confirmed by showing the maximum value of the updated expression is smaller than the channel's information capacity, which is obtained by a system-model-independent algorithm. In the second part of this paper, we argue that the phase change of transmitted signal caused by the Doppler effect plays a much more important role in determining the channel varying memory than their amplitude fading does. The Gilbert-Elliot channel, capturing amplitude fading, is therefore not ideal for modelling the memory. And finding its optimal decoder, which is shown to be very difficult, may have little physical significance.

The paper describes a digital smear-desmear technique (SDT) based on polyphase sequences with good autocorrelation properties. These sequences are applied to the design of digital smear/desmear filters and combined with Trellis-coded modulation (TCM) codes. The scheme has been investigated for 16-QAM and 64-QAM modulation. The impulse noise is modeled as a sequence of Poisson arriving delta functions with gaussian amplitudes. The impulse noise parameters are computed from experimental data. Results shows that the SDT filter design method yields a significant improvement in bit error rates as when subject to impulse noise, relative to systems with no SDT.

Reducing Impulse noise is a very active research area in communication systems. This paper presents a digital smear-desmear technique (SDT) applied to data transmission over band limited channels. A generalized set of filter design criteria based on minimizing the average bit error probability is introduced. The design criteria were applied to a practical digital filter implementing SDT techniques. The SDT is simulated and combined with coded communication systems for high data transmission rate. Simulation results show that the SDT yields a significant improvement in bit error rates for coded systems subject to impulse noise, relative to the systems with no SDT. The technique also completely removes the error floor caused by the impulse noise.

In this paper, results of mathematical analysis supported by simulation are used to investigate the impact of propagation loss on the performance and information capacity of cellular wireless network at higher microwave carrier frequencies (beyond 2 GHz). At higher microwave carrier frequencies co-channel interfering cells beyond the first six co-channel cell becomes active as the cell size reduces. It is shown that the second tier co-channel interfering cell is more dominant at lower (below 2.5) path loss exponent.

This paper presents an adaptive multiuser receivers scheme for MIMO OFDM over Turbo-Equalization for Single-Carrier Transmission. It involves the joint adaptive minimum mean square error multiuser detection and decoding algorithm with prior information of the channel and interference cancelation in the spatial domain. A partially filtered gradient LMS (Adaptive) algorithm is also applied to improve the convergence speed and tracking ability of the adaptive detectors with slight increase in complexity. The proposed technique is analyzed in a slow and fast Rayleigh fading channels in MIMO OFDM systems. The adaptive multiuser detection for MIMO OFDM (AMUD MIMO OFDM) perform as well as the iterative equalization for single-carrier.

In this paper results of mathematical analysis supported by simulation are used to find a theoretical limit for cell size reduction in mobile communication systems. Information capacity approach is used for the analysis. Attention is given to the active co-channel interfering cells. Because at microwave frequencies beyond 2 GHz, co-channel interfering cells beyond the first tier becomes dominant as the cell size reduces. We show that when the cell size limit is reached any further reduction in cell size will not increase the information capacity of the cellular network. A formula is derived for calculating the number of co-channel cells in subsequent tiers.

This paper Presents information channel capacity as a fundamental measure of estimating the quality of digital communication channel which is possible with vanishingly small probability of bit error. This work presents that the capacity of a stationary channel with intersymbol interference (ISI) is achievable by both the techniques:1) single carrier modulation with ideal MMSE-DFE receiver and 2)multicarrier modulation over narrow subchannels with OFDM receivers. This result implies that both techniques single carrier or multicarrier, apart from different implementations, are essentially identical when it comes to criterion of achievable channel information capacity.

There has been a demand for high data rate for wireless communication system devices, mainly mobile multimedia applications. This paper investigates the suitability of Turbo Equalization as a means of achieving low bit error rate in the future high data communication systems. Turbo equalization is an approach to coded data transmission over channels with Inter-symbol interference (ISI) which can yield additional improvement in bit error rate. The paper demonstrates that at higher modulation scheme using iterative equalization method, high date rate can be achieved. The performance evaluation shows that turbo equalization is beneficial for higher modulation and thereby increase data rate with a reasonable complexity.

The authors explore the potential application of cognitive interrogator network (COIN) in remote monitoring of mobile subjects in domestic environments, where the ultra-wideband radio frequency identification (UWB-RFID) technique is considered for accurate target localisation. The authors first present the COIN architecture in which the central base station (BS) continuously and intelligently customises the illumination modes of the distributed interrogators in response to the system's changing knowledge of the channel condition and subject movement. Subsequently, the analytical results of the locating probability and time-of-arrival (TOA) estimation uncertainty for a large-scale COIN with randomly distributed active sensors are derived based upon the implemented cognitive intelligence. As an important component to facilitate the adaptive illumination of the environment, the sequential-hypothesis-testing framework is proposed to estimate the tag antenna orientation. Finally, numerical examples are used to demonstrate the key effects of the proposed cognitive schemes on the system performance.

A novel channel model for a two-hop decentralized wireless relay network (DWRN) is presented, where the relays operate in a completely distributive fashion. The model is based on an analogous approach to the conventional description methods for wideband directional multipath channels. First, the concept of information azimuth-delay spectrum (IADS) is devised for a simplified DWRN with perfect receiver conditions. The IADS describes the distribution of the incoming information at the destination in the azimuth-delay domain. This definition, parallel to the widely-used power azimuth-delay spectrum (PADS) for physical multipath channels, represents a compact description of virtual DWRN channels. Subsequently, several key quantities derived from the IADS are introduced, which provide an intuitive way for analysis of a DWRN. The proposed approach is then applied to an elliptical random network (ERN) with uniformly distributed relay nodes. Numerical examples are used to demonstrate the usefulness of the suggested channel description method.

In this paper we demonstrate the application of Hidden Markov Models (HMM) for localization and tracking in ultra wide band (UWB) radar networks. To improve localization, a Voronoi region based approach is utilized to form a HMM for detection and tracking of mobile target. The observations used for the HMM localization are obtained from the power delay profile of the received signals. In UWB systems the use of entire power delay profiles instead of the total power only, allows to reach higher localization accuracy. This is due to the power delay profile being a measure of the power as well as the time of arrival. Simulation results suggest a performance gain of 7dB over the maximum likelihood estimation for localization in presence of path loss at intermediate values of signal to noise ratio (SNR).

In this paper, the performance of vector precoding in multiple input multiple output broadcast channels (MIMO BC) is investigated and compared with other channel decomposition techniques utilized for implementing zero forcing (ZF) preceding. It is a known result that ZF precoding requires pseudo inversion of the channel matrix, where this operation is only optimum when the transmitter power is unconstrained. The problem when the transmitter is subject to average or maximum power constraints is well known, where results published have indicated that ZF precoding approaches the maximum capacity bound if the dimensionality of the system is greater than the number of transmitter antennas. A vector precoding technique for MIMO BC channels is investigated in this paper where pseudo inversion is circumvented by employing joint co-operation between transmitter and receiver for all users. This technique adopts a time scheduling approach to service the users which facilitates decentralized multi-user detection at the receiver. This approach yields an improvement to the bit error rate probability by approximately an order of magnitude as compared to the ZF approach utilizing other channel decomposition techniques. The scheme also enables an increase in the capacity of the MIMO BC, with less computational complexity as compared to the techniques employing Moore-Penrose pseudo inverse.

The diversity gains achievable in the generalised distributed antenna system with cooperative users (GDAS-CU) are considered. A GDAS-CU is comprised of M largely separated access points (APs) at one side of the link, and N geographically closed user terminals (UTs) at the other side. The UTs are collaborating together to enhance the system performance, where an idealised message sharing among the UTs is assumed. First, geometry-based network models are proposed to describe the topology of a GDAS-CU. The mean cross-correlation coefficients of signals received from non-collocated APs and UTs are calculated based on the network topology and the correlation models derived from the empirical data. The analysis is also extendable to more general scenarios where the APs are placed in a clustered form due to the constraints of street layout or building structure. Subsequently, a generalised signal attenuation model derived from several stochastic ray-tracing-based pathloss models is applied to describe the power-decaying pattern in urban built-up areas, where the GDAS-CU may be deployed. Armed with the cross-correlation and pathloss model preliminaries, an intrinsic measure of cooperative diversity obtainable from a GDAS-CU is then derived, which is the number of independent fading channels that can be averaged over to detect symbols. The proposed analytical framework would provide critical insight into the degree of possible performance improvement when combining multiple copies of the received signal in such systems.

In this paper, we explore the application of cooperative communications in ultra-wideband (UWB) wireless body area networks (BANs), where a group of on-body devices may collaborate together to communicate with other groups of on-body equipment. Firstly, time-domain UWB channel measurements are presented to characterize the body-centric multipath channel and to facilitate the diversity analysis in a cooperative BAN (CoBAN). We focus on the system deployment scenario when the human subject is in the sitting posture. Important channel parameters such as the pathloss, power variation, power delay profile (PDP), and effective received power (ERP) crosscorrelation are investigated and statistically analyzed. Provided with the model preliminaries, a detailed analysis on the diversity level in a CoBAN is provided. Specifically, an intuitive measure is proposed to quantify the diversity gains in a single-hop cooperative network, which is defined as the number of independent multipaths that can be averaged over to detect symbols. As this measure provides the largest number of redundant copies of transmitted information through the body-centric channel, it can be used as a benchmark to access the performance bound of various diversity-based cooperative schemes in futuristic body sensor systems.

We consider the optimum design of pilot-symbol-assisted modulation (PSAM) schemes with feedback. The received signal is periodically fed back to the transmitter through a noiseless delayed link and the time-varying channel is modeled as a Gauss-Markov process. We optimize a lower bound on the channel capacity which incorporates the PSAM parameters and Kalman-based channel estimation and prediction. The parameters available for the capacity optimization are the data power adaptation strategy, pilot spacing and pilot power ratio, subject to an average power constraint. Compared to the optimized open-loop PSAM (i.e., the case where no feedback is provided from the receiver), our results show that even in the presence of feedback delay, the optimized power adaptation provides higher information rates at low signal-to-noise ratios (SNR) in medium-rate fading channels. However, in fast fading channels, even the presence of modest feedback delay dissipates the advantages of power adaptation.

This paper provides mutual information performance analysis of multiple-symbol differential MPSK (M-phase shift keying) over time-correlated, time-varying flat-fading communication channels. A state space approach is used to model time correlation of time varying channel phase. This approach captures the dynamics of time correlated, time-varying channels and enables exploitation of the forward-backward algorithm for mutual information performance analysis. It is shown that the differential decoding implicitly uses a sequence of innovations of the channel process time correlation and this sequence is essentially uncorrelated. It enables utilization of multiple-symbol differential detection, as a form of block-by-block maximum likelihood sequence detection for capacity achieving mutual information performance. It is shown that multiple-symbol differential ML detection of BPSK and QPSK practically achieves the channel information capacity with observation times only on the order of a few symbol intervals.

We explore the potential application of Cognitive Interrogator Network (CIN) in remote monitoring of mobile subjects in domestic environments, where the ultra-wideband radio frequency identification (UWB-RFID) technique is considered for accurate source localization. We first present the CIN architecture in which the central base station (BS) continuously and intelligently customizes the illumination modes of the distributed transceivers in response to the system’s changing knowledge of the channel conditions and subject movements. Subsequently, the analytical results of the locating probability and time-of-arrival (TOA) estimation uncertainty for a large-scale CIN with randomly distributed interrogators are derived based upon the implemented cognitive intelligences. Finally, numerical examples are used to demonstrate the key effects of the proposed cognitions on the system performance.

A communication system model for mutual information performance analysis of multiple-symbol differential M-phase shift keying over time-correlated, time-varying flat-fading communication channels is developed. This model is a finite-state Markov (FSM) equivalent channel representing the cascade of the differential encoder, FSM channel model and differential decoder. A state-space approach is used to model channel phase time correlations. The equivalent model falls in a class that facilitates the use of the forward–backward algorithm, enabling the important information theoretic results to be evaluated. Using such a model, one is able to calculate mutual information for differential detection over time-varying fading channels with an essentially finite time set of correlations, including the Clarke fading channel. Using the equivalent channel, it is proved and corroborated by simulations that multiple-symbol differential detection preserves the channel information capacity when the observation interval approaches infinity.

We study information rates of time-varying flat-fading channels (FFC) modeled as finite-state Markov channels (FSMC). FSMCs have two main applications for FFCs: modeling channel error bursts and decoding at the receiver. Our main finding in the first application is that receiver observation noise can more adversely affect higher-order FSMCs than lower-order FSMCs, resulting in lower capacities. This is despite the fact that the underlying higher-order FFC and its corresponding FSMC are more predictable. Numerical analysis shows that at low to medium SNR conditions (SNR lsim 12 dB) and at medium to fast normalized fading rates (0.01 lsim fDT lsim 0.10), FSMC information rates are non-increasing functions of memory order. We conclude that BERs obtained by low-order FSMC modeling can provide optimistic results. To explain the capacity behavior, we present a methodology that enables analytical comparison of FSMC capacities with different memory orders. We establish sufficient conditions that predict higher/lower capacity of a reduced-order FSMC, compared to its original high-order FSMC counterpart. Finally, we investigate the achievable information rates in FSMC-based receivers for FFCs. We observe that high-order FSMC modeling at the receiver side results in a negligible information rate increase for normalized fading rates fDT lsim 0.01.

A method for selecting the order in which the users are detected in communication systems employing adaptive successive decision feedback multiuser detection is proposed. Systems employing channel coding without the assumption of perfect decision feedback are analyzed. The method is based on the mean squared error (MSE) measurements during a training period for each user. The analysis' shows that the method delivers BER performance improvement relative to other previously proposed ordering methods.

We investigate the antenna impedance mismatching effect on thermal noise power, in addition to the mutual coupling effect on signal and noise. We provide an analysis of the channel capacity of multiple antennas terminated by three most common matching decoupling networks. We confirm that the achievable information rate of the multiple antennas terminated by self- and characteristic impedance matching network is below the information throughput that could be achieved by the multiple antennas terminated by the multi-port conjugate match. Yet the actual information rate degradation due to the imperfect matching network is lower than the case when the noise coupling is omitted from the analysis. The result indicates on the potential of the transit output power savings. Therefore, we suggest that the design of the matching decoupling networks should be based on the maximum signal-to-noise ratio criteria rather than maximum achievable signal power as both signal and noise powers are affected by the mutual coupling effect and consequently by antenna mismatching impedance.

This paper presents a Godard-Kalman approach with adaptive model parameters identification for model-based adaptive filtering over time-varying communication channels. The presented approach enables model-based adaptive channel equalization without prior channel estimation. An adaptive identification of autoregressive (AR) model coefficients is performed to overcome the issue of determining model coefficients which capture the dynamics of unknown time-varying channels. Experimental MSE performance of the adaptive algorithms are simulated in a multi-user environment, assuming a vector AR(1) model for the optimal filter weighs. Superior performance of the Godard-Kalman algorithm with adaptive model identification is demonstrated, comparing to the same algorithm with fixed model coefficients and to standard observation-only-based LMS and RLS adaptive algorithms.

This paper looks at capacity achieving detection strategies for information transfer over time-varying channels. The time-varying binary symmetric channel (TV-BSC) is identified as the basic binary state-space model. Separation of entropies principles and the TV-BSC model-based state-space approach are used to determine the performance bounds for coherent and non-coherent detection over time-varying communication channels. The mutual information rate over the TV-BSC, assuming channel estimation in the presence of channel noise, is shown to be below the channel information capacity because of lack of perfect channel knowledge. Furthermore, it is shown that TV-BSC model-based differential detection has a fundamental advantage over the channel estimation based detection since it theoretically preserves the TV-BSC information capacity when the observation interval approaches infinity. Simulation analysis corroborates the theoretical results, showing that multiple-symbol differential detection practically achieves the TV-BSC capacity in just a few symbol observation times.

This paper shows the existence of the optimal training, in terms of achievable mutual information rate, for an output feedback implicit estimator for finite-state Markov communication channels. A proper quantification of source redundancy information, implicitly used for channel estimation, is performed. This enables an optimal training rate to be determined as a tradeoff between input signal entropy rate reduction (source redundancy) and channel process entropy rate reduction (channel estimation). The maximal mutual information rate, assuming the optimal implicit training and the presence of channel noise, is shown to be strictly below the ergodic channel information capacity. It is also shown that this capacity penalty, caused by noisy time-varying channel process estimation, vanishes only if the channel process is known or memoryless (channel estimation cannot improve system performance)

In this paper, we study the effect of channel memory order on the information rate bounds in time-varying flat-fading (FF) channels. We model time variations of the FF channel with autoregressive (AR) processes with varying degrees of model order. We observe that in high SNR conditions (SNR 20 dB), the information rate penalty of not knowing the AR channel is a non-increasing function of the AR model order. This is expected, since the AR channel predictability cannot decrease with increasing its order. However, in low SNR conditions, the information rate penalty in low-order AR channels can be lower than those in high-order AR channels. Likewise, the intuitive and universal monotonic increase of the information rate bounds with the AR model order is only observed in almost noiseless conditions. In the low SNR regime, however, the achievable information rate bounds in low-order AR channels can be higher than those in high-order AR channels.

In this paper we show that an adaptive multiuser Turbo (iterative) asynchronous compared to a synchronous receiver converges to the same bit error rate (BER) performance result of less than 10 3 at signal to interference noise ratio (SINR) greater than 4 dB . The (Turbo) iterative multiuser receiver system considered here consists of a bank of adaptive linear minimum mean square error (MMSE) channel estimator followed by non-linear MMSE detectors and a bank of dedicated log maximum a-posteriori (MAP) decoders. The log-MAP decoders and adaptive non-linear detectors which are separated by de-interleaver and interleaver, exchange imperfect decision information in every Turbo iteration. Asynchronous behavior of a wireless communication multicellular multiuser system (multiple input multiple output, MIMO) system is shown with different spreading gains but, fixed receiver filter tap size.

This paper shows through theory and simulation the superiority of model-based adaptive algorithms relative to observation-only-based adaptive algorithms, such as LMS and RLS, when applied to tracking time-varying channels. The model-based formulation reveals RLS as a degenerate algorithm which does not explicitly recognize the time-varying nature of the channel and consequently is ill-suited to tracking in non-stationary environments. Simulation results for MSE performance of the various adaptive algorithms applied to adaptive MMSE multiuser receiver corroborate the theoretical analysis

In this paper we study the temporal statistics of cellular mobile channel. We propose a scattering model that encloses scatterers in an elliptical scattering disc. We, further, employ this model to derive the probability density function (pdf) of time of arrival (ToA) of the multipath signal for picocell, microcell, and macrocell environments. For macrocell environment, we present generic closed-form formula for the pdf of ToA from which previous models can be easily deduced. Proposed theoretical results can be used to simulate temporal dispersion of the multipath signal in a variety of propagation conditions. The presented results help in the design of efficient equalizers to combat intersymbol interference (ISI) for wideband systems

Propagation models are low-cost and handy means to accurately predict radio wave propagation behavior. In this paper we discuss the change in temporal statistics of the multipath signals when a mobile station (MS) traverses a distance inside the coverage area. The paper emphasizes the delay spread of the multipath signals received at each instant of time during MS motion. We identify two different cases for the change in the scattering environment regarding the terrain and clutter surrounding MS as it moves. The derived formulae, in closed form, can easily be used to simulate time variability of temporal spread as a result of MS motion. Proposed theoretical results can therefore be extended to design efficient equalizers to combat intersymbol interference (ISI) in time varying frequency selective fading channels

In this paper we determine the MIMO channel capacity in the presence of correlated noise. We employ a normalized noise correlation matrix in the channel capacity formula, in order to identify the noise correlation contribution to the MIMO subchannel decorrelation. Then, we analyze the thermal noise correlation due to mutual coupling. We calculate thermal noise correlation matrix in the multi-antenna system with closely spaced antenna by applying the Nyquist's thermal noise theorem. Simulation results shows that mean and outage MIMO channel capacity is underestimated if the noise correlation due to mutual coupling is not accounted for. We present results for cases when the transmitter does and does not know the channel realization

This paper considers precoder designs in downlink MIMO broadcast channels (BC). A preceding scheme that is analog to the MMSE decision feedback equalizer in the uplink MIMO multiple access channels is first introduced. The proposed scheme can asymptotically achieve the sum-capacity of MIMO-BC at high SNRs. At medium SNRs, its achievable sum capacity is very close to that of MIMO-BC and larger than that of ZF-DR. The new scheme can also offer a significant gain over linear pre-equalization techniques in terms of average error probability. Furthermore, by linking the proposed precoder with the sphere encoder, a modified sphere encoder is devised. The modified sphere encoder is shown to be able to achieve the same diversity order as the maximum likelihood (ML) receiver in dual uplink. Its performance is also approaching the corresponding ML receiver.

In this paper, we study the effect of memory order on the capacity of finite-state Markov channels (FSMC). We analytically compare the capacity of an originally high-order FSMC model with the capacity of its reduced memory order version. We show that the capacity difference is caused by two factors: 1) the channel entropy difference, and 2) the channel observability difference between the two models. While the first factor, alone, results in underestimation of the original FSMC capacity by the reduced-order FSMC model, due to the existence of the second factor, capacity overestimation can also occur. Explicit examples of FSMC models are provided, where the reduced-order FSMC model overestimates the capacity of the original high-order channel. To show the practical significance of the analysis, we model time-varying flat-fading (FF) channels with FSMC models. It is observed that the first-order FSMC models can provide both higher and lower estimates of the FF channel capacity, compared to higher order FSMC models

In this paper we analyze the causes and effects of mutual coupling on MIMO channel capacity. Mutual coupling not only affects the signal through correlation but also the thermal noise-a critical factor omitted from previous work. We study a two-antenna array by applying the Nyquist's thermal noise theorem. Simulation results shows that mean MIMO channel capacity is underestimated if the noise correlation due to mutual coupling effect is not accounted for. We present results for both cases when the transmitter does and does not know the channel realizations

In this letter, we propose a new turbo decision feedback equalizer (DFE) algorithm that takes into account decision feedback error propagation. We derive the exact mathematical expression for the probability density function (pdf) of soft decision feedback symbols, assuming that soft outputs from channel decoder are independent identically distributed Gaussian random variables with known mean and variance. We find a new set of "imperfect" turbo minimum mean-square error DFE coefficients based on the derived pdf. The proposed turbo detector outperforms other turbo equalization algorithms of similar computational complexity in terms of bit-error rate. The achieved improvement is up to 3.8 dB for severe frequency-selective channels.

In this letter, we propose to use repeaters to increase system information capacity in urban areas, where path loss exponents are higher (as much as 3.4 or more). There is at least a 10% increase in coverage area when repeaters are placed in every cell within the network to increase system capacity. The overall system information capacity is doubled at propagation exponent 3.7-3.9.

In this paper, time-varying flat-fading channels are modeled as first-order finite-state Markov channels (FSMC). The effect of this modeling on the channel information capacity is addressed. The approximation accuracy of the first-order memory assumption in the Markov model is validated by comparing the FSMC capacity with the channel capacity assuming perfect state information at the receiver side. The results indicate that the first-order Markovian assumption is accurate for normalized Doppler frequencies f/sub d/T /spl lsim/ 0.01, in amplitude-only quantization of the channel gain for noncoherent binary signaling. In phase-only and joint phase and amplitude quantization of the channel gain for coherent binary signaling, the first-order Markovian assumption is accurate for f/sub d/T /spl lsim/ 0.001. Furthermore, the effect of channel quantization thresholds on the FSMC capacity is studied. In high signal-to-noise ratio (SNR) conditions, nonuniform two-level amplitude quantization scheme outperforms equiprobable quantization method by 0.8-1.5 dB.

This work analyses the time varying nature of the mobile communications channel which affects the channel information capacity. The channel uncertainty is a fundamental issue for the information theory of the time varying mobile communication channels and here we use an information theoretic approach to channel uncertainty modeling. Although we analyse the final state Markov channel (FSMC) models, our approach still enables considerable conceptual insight to be gained. In the first step of our analysis, we define four axioms which qualitatively describe the non-trivial influence of the channel uncertainty on the time-varying channel information capacity. Then, we focus on the FSMC models which capture these axioms. We present the concept of time varying binary symmetric channel (TV-BSC) to further support our analysis. The TV-BSC is the simplest FSMC model that shows a non-trivial influence on capacity due to the channel uncertainty. We provide the information theoretic analysis of the TV-BSC model including the information capacity calculation and the TV-BSC estimation analysis.

The effect of channel gain quantization on the information capacity of unknown time varying flat fading channels is investigated. The phase and/or amplitude of the flat fading channel gain is modelled as a finite state Markov (FSM) process and the information capacity of the FSM channel is calculated numerically as a measure for choosing the number of channel quantization levels, as well as quantization thresholds. The results indicate that for binary signalling, the capacity is saturated beyond 8 to 16 levels of phase and 8 to 16 levels of amplitude quantization.

We study the multiuser diversity in both multiple-input multiple output (MIMO) multiple-access channel (MAC) and MIMO broadcast channel (BC). We consider multiuser scheduling with linear receivers for uplink and linear precoders for downlink. In each time slot, a group of users are selected in either transmission or reception. Based on the performance analysis, a criterion of selecting a number of active users to optimize error performance is presented. We show that the linear processing for downlink and uplink achieves the same SNR. This makes it convenient to use a single scheduling algorithm in both downlink and uplink. Furthermore, we present an analysis of the performance of the proposed scheduling algorithm. It is shown that the analysis agrees with the simulation.

In rapidly time varying channels, the channel impulse response estimation might not be successful by the conventional decision directed scheme following the supervised training. The blind sequence detection would be the only solution in these scenarios. In this paper we proposed a new blind sequence detection algorithm, which has a significant robustness against bit ambiguity and whose performance with uncoded sequence is better than that of some existing blind algorithms with coded systems. Bit error performances have been investigated by computer simulation and compared with coded system. A significant gain has been achieved in SNR as much as 6 dB or more.

In this paper we compare two different methods for receiver training in flat fading channels. The first method is the traditional way in which periodic training sequences are sent to the receiver (explicit training). In the second method, recently proposed, the information source emits bits with unequal probabilities of being '0' and '1'. This method is called implicit training, since the training is implied in the non-symmetrical source structure. BPSK signaling is used as the simplest example of constant-envelope phase modulations. We map the phase component of the flat fading channel response to a simple two-state Markov model. Then joint iterative trellis-based maximum a posteriori probability (MAP) method is used for channel state estimation and decoding. The results of computer simulations for the receiver bit error rate (BER) performance in various channel fading rates and information rates are presented. The results indicate superior performance of implicit training. In slow fading conditions, the gain is 4 dB at information rate of 0.15 bits/channel use and 1.2 dB for information rate of 0.25 bits/channel use.

Multiuser decision-feedback detectors (DFDs) for direct-sequence code-division multiple access, based on the minimum mean-squared error (MMSE) performance criterion, are described. Both successive and parallel feedback (interference cancellation) with hard decisions are considered. An iterative DFD is presented, which consists of cascaded DFDs, each performing successive cancellation. The two-stage DFD achieves the single-user bound in the absence of error propagation, and performs significantly better than an MMSE DFD with parallel feedback. The filter structures are generalized to include finite impulse response feedforward and feedback matrix filters, which account for asynchronous users and intersymbol interference. The effect of error propagation is illustrated through simulation. Both uncoded and coded performance results are presented. Although error propagation can significantly degrade performance, the DFDs still offer a significant performance gain relative to linear MMSE detection.

In multiuser wireless systems using multiple antennas in a multipath environment it has been asserted that whenever users are at least a distance of /spl lambda//2 apart, it is possible to recover their respective signals with appropriate signal-processing (Winters 1984). This paper significantly strengthens this remarkable assertion by: 1) showing explicitly that separability of the users' signals is possible using the simplest class of linear detectors; 2) showing that the /spl lambda//2 rule of thumb can be quite conservative with meaningful signal separability achievable at separations around /spl lambda//10; and 3) identifying critical geometrical properties, such as position of users and multipath geometry that permit the best separability.

The expression for stationary channel information capacity of a mobile communication channel with a large number of receiving antennas in the presence of multipath signal propagation is shown. It has been shown that the capacity is approaching information capacity of the orthogonal Gaussian channel with no interference. The system with adaptive linear MMSE receivers and adaptive interference cancellation is presented as a possible low computational complexity solution achieving the theoretical capacity.

An optimal multiuser detector in the weighted least squares (WLS) sense is derived. This detector, which includes the maximum likelihood multiuser detector as a special case, consists of two parts: a bank of linear fractionally chip spaced minimum mean squared error (MMSE) filters, and a nonlinear WLS metric minimizer. It is shown that the symbol spaced samples at the output of the MMSE filter bank provide a set of sufficient statistics for WLS detection. The relationship between the taps of a centralized decision feedback detector and the MMSE filter bank is derived. It is proven that all the necessary parameters for implementing the WLS detector can be realized by adaptively training a centralized decision feedback detector. Therefore, the WLS detector achieves optimal joint synchronization and data detection even in the presence of colored noise, such as narrowband interference, without any a priori knowledge of the users' signatures, multipath channel taps or statistics of the colored noise. Significant features of the WLS detector are that: (1) the WLS detector is a generalization of the maximum likelihood multiuser detector that employs a bank of matched filters; (2) it is implemented adaptively; and (3) it has structural flexibility in terms of implementation complexity.

In direct-sequence code-division multiple access, a code synchronization must take place before the multiuser detector. As the initial synchronization stage, a code acquisition scheme is used to estimate the relative timing phase for the desired transmission within one chip interval. In this paper, a blind code acquisition scheme using adaptive linear filtering based on a linearly constrained constant modulus algorithm (CMA) is proposed. The uncertainty of a desired user's delay is initially discretized and translated into a number of hypotheses. The lock convergence property of CMA is exploited, where the filter at the steady state can lock onto the desired user while nulling all other interfering users (i.e., a decorrelator). For each delay hypothesis, the filter is initialized as the corresponding shifted spreading sequence of the desired user. It is shown that lock convergence always occurs for the correct hypothesis, while all incorrect hypotheses will be hovered around some saddle regions, given sufficiently small step sizes. Then, the correct hypothesis is the one which has the converged filter to yield the maximum lock onto the desired user, or a maximum output energy.

This paper examines the achievable capacity of a direct-sequence spread-spectrum multiple-access system based on an adaptive minimum mean-square-error (MMSE), single-user receiver over a Gaussian channel. The effect of using both trellis and convolutional codes of varying rates and complexity is investigated in relation to the effect on the maximum number of users and the achievable bit-error rate for a given number of users. A system based on a matched filter receiver is presented for the purposes of comparison. It has been found that, over the band-limited channel considered, the use of convolutional coding reduces the total system capacity for the high efficiency MMSE receiver while still providing a coding gain for a small numbers of users. The use of trellis codes however leads to increased system capacity.

A closed-form expression for information capacity of the random signature multiple input multiple-output channel is given. A direct calculation of the capacity is provided additional to the proof involving the capacity of minimum mean-square-error systems provided by Verdu and Shamai (see IEEE Trans. Inform. Theory, vol.45, p.622-40, 1999).

The well-known code division multiple access maximum likelihood receiver (MF-ML) uses a bank of matched filters as a generator of sufficient statistics for maximum likelihood detection of users transmitted symbols. In this paper, the bank of matched filters is replaced by a bank of adaptive minimum mean squared error (MMSE) filters as the generator of sufficient statistics. This formal replacement of the MF bank by the adaptive MMSE filter bank has significant conceptual consequences and provides improvement by several performance measures. The adaptive MMSE-ML receiver's digital implementation is significantly computationally simplified. The advantages of the proposed adaptive MMSE-ML receiver over the MF-ML receiver are: (1) ability to perform joint synchronization, channel parameter estimation, and signal detection where the signal is sent over an unknown, slowly time-varying, frequency-selective multipath fading channel; (2) increased information capacity in a multicellular environment; and (3) significantly improved bit error rate (BER) performance in a multicellular mobile communications environment. The information capacity and the BER of the proposed MMSE-ML receiver are analyzed. Numerical results showing the BER performance of the MMSE-ML receiver in a multipath channel environment are presented.

This paper studies the effect of arrival of new users to the synchronous blind multiuser receivers namely one based on the minimum output energy (MOE) criterion and one based on the constant modulus algorithm (CMA). The perturbation caused by this sudden birth of interferers is examined from the viewpoint of the change of the cost function under the noiseless assumption. The CMA and MOE detectors are compared with the training-based minimum mean-squared-error (MMSE) detector. It is shown that in the vicinity of lock convergence, the CMA minimum stays in the neighborhood of the MMSE minimum if the perturbation is small enough. The changes in the MMSE and MOE costs are the same if all users are independent. It is observed that the transient behavior of the CMA detector deteriorates the fastest as the existing number of users approaches the processing gain.

The performance of the adaptive MMSE maximum likelihood CDMA receiver is analysed. Instead of the matched filter (MF) bank as the generator of sufficient statistics for maximum likelihood detection (MF-ML), a bank of minimum mean squared error (MMSE) filters is used. It is shown that the proposed MMSE-ML receiver achieves increased information capacity in a multicellular environment. Numerical results showing the performance of the MMSE-ML receiver in a multipath channel environment are presented.

Code acquisition in direct-sequence code division multiple access (DS-CDMA) must occur before any multiuser detection is used. In this paper, a blind code acquisition algorithm is proposed using adaptive filtering based on the constant modulus algorithm (CMA). In the same manner as the subspace-based channel estimation, the task of delay estimation problem is to minimize the Euclidean norm of the projection of the linear filter onto the subspace spanned by interfering users in the noiseless case. We exploited the lock convergence property of CMA to give a higher acquisition-based capacity and near-far resistance compared to the conventional sliding correlator. We analyzed the mean acquisition time, detection and false alarm probabilities in terms of probability density function of the decision statistics.

Issues relating to the choice of adaptive algorithms for use in single user minimum mean square error (MMSE) direct sequence (DS) code division multiple access (CDMA) receivers are discussed. Such receivers have the potential to dramatically increase capacity of CDMA systems compared to systems with conventional correlator receivers, without the high complexity associated with many proposed multi-user receivers. To be practical for many applications, the convergence rate of the adaptive algorithms must be improved, while maintaining low residual mean-squared error (MSE), robustness to noise and realisable complexity. The slow convergence of the least mean squares (LMS) algorithm is highlighted for this application. Recursive least squares (RLS) algorithms are shown to give much quicker convergence, but have much higher sensitivity to thermal noise. Algorithms normally termed "Fast-RLS", are shown to more complex than the conventional RLS algorithm when used in a CDMA receiver.

Adaptive linear and decision feedback receiver structures for coherent demodulation in asynchronous code division multiple access (CDMA) systems are considered. It is assumed that the adaptive receiver has no knowledge of the signature waveforms and timing of other users. The receiver is trained by a known training sequence prior to data transmission and continuously adjusted by an adaptive algorithm during data transmission. The proposed linear receiver is as simple as a standard single-user detector receiver consisting of a matched filter with constant coefficients, but achieves essential advantages with respect to timing recovery, multiple access interference elimination, near/far effect, narrowband and frequency-selective fading interference suppression, and user privacy. An adaptive centralized decision feedback receiver has the same advantages of the linear receiver but, in addition, achieves a further improvement in multiple access interference cancellation at the expense of higher complexity. The proposed receiver structures are tested by simulation over a channel with multipath propagation, multiple access interference, narrowband interference, and additive white Gaussian noise.>