A novel approach to exponential stability of continuous-time Roesser systems with directional time-varying delays

This paper addresses the problem of exponential stability analysis of two-dimensional (2D) linearcontinuous-time systems with directional time-varying delays. An abstract Lyapunov-like theorem whichensures that a 2D linear system with delays is exponentially stable for a prescribed decay rate is exploitedfor the first time. In light of the abstract theorem, and by utilizing new 2D weighted integral inequalitiesproposed in this paper, new delay-dependent exponential stability conditions are derived in terms oftractable matrix inequalities which can be solved by various computational tools to obtain maximumallowable bound of delays and exponential decay rate. Two numerical examples are given to illustrate theeffectiveness of the obtained results.

On global exponential stability of positive neural networks with time-varying delay

This paper presents a new result on the existence, uniqueness and global exponential stability of a positive equilibrium of positiveneural networks in the presence of bounded time-varying delay. Based on some novel comparison techniques, a testable conditionis derived to ensure that all the state trajectories of the system converge exponentially to a unique positive equilibrium. Theeffectiveness of the obtained results is illustrated by a numerical example.

Model-based pilot and data power adaptation in psam with periodic delayed feedback

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.

Joint space-time trellis decoding and channel estimation in correlated fading channels

This letter addresses the issue of joint space-time trellis decoding and channel estimation in time-varying fading channels that are spatially and temporally correlated. A recursive space-time receiver which incorporates per-survivor processing (PSP) and Kalman filtering into the Viterbi algorithm is proposed. This approach generalizes existing work to the correlated fading channel case. The channel time-evolution is modeled by a multichannel autoregressive process, and a bank of Kalman filters is used to track the channel variations. Computer simulation results show that a performance close to the maximum likelihood receiver with perfect channel state information (CSI) can be obtained. The effects of the spatial correlation on the performance of a receiver that assumes independent fading channels are examined.

Time-frequency characterization of a sound propagation channel as an educational tool

This paper discusses the use of sound waves to illustrate multipath radio propagation concepts. Specifically, a procedure is presented to measure the time-varying frequency response of the channel. This helps demonstrate how a propagation channel can be characterized in time and frequency, and provides visualizations of the concepts of coherence time and coherence bandwidth. The measurements are very simple to carry out, and the required equipment is easily available. The proposed method can be useful for wireless or mobile communication courses.

Effect of pedestrian movement on MIMO-OFDM channel capacity in an indoor environment

The rapid growth of mobile telephone use, satellite services, and now the wireless Internet and WLANs are generating tremendous changes in telecommunication and networking. As indoor wireless communications become more prevalent, modeling indoor radio wave propagation in populated environments is a topic of significant interest. Wireless MIMO communication exploits phenomena such as multipath propagation to increase data throughput and range, or reduce bit error rates, rather than attempting to eliminate effects of multipath propagation as traditional SISO communication systems seek to do. The MIMO approach can yield significant gains for both link and network capacities, with no additional transmitting power or bandwidth consumption when compared to conventional single-array diversity methods. When MIMO and OFDM systems are combined and deployed in a suitable rich scattering environment such as indoors, a significant capacity gain can be observed due to the assurance of multipath propagation. Channel variations can occur as a result of movement of personnel, industrial machinery, vehicles and other equipment moving within the indoor environment. The time-varying effects on the propagation channel in populated indoor environments depend on the different pedestrian traffic conditions and the particular type of environment considered. A systematic measurement campaign to study pedestrian movement effects in indoor MIMO-OFDM channels has not yet been fully undertaken. Measuring channel variations caused by the relative positioning of pedestrians is essential in the study of indoor MIMO-OFDM broadband wireless networks. Theoretically, due to high multipath scattering, an increase in MIMO-OFDM channel capacity is expected when pedestrians are present. However, measurements indicate that some reductions in channel capacity could be observed as the number of pedestrians approaches 10 due to a reduction in multipath conditions as more human bodies absorb the wireless signals. This dissertation presents a systematic characterization of the effects of pedestrians in indoor MIMO-OFDM channels. Measurement results, using the MIMO-OFDM channel sounder developed at the CSIRO ICT Centre, have been validated by a customized Geometric Optics-based ray tracing simulation. Based on measured and simulated MIMO-OFDM channel capacity and MIMO-OFDM capacity dynamic range, an improved deterministic model for MIMO-OFDM channels in indoor populated environments is presented. The model can be used for the design and analysis of future WLAN to be deployed in indoor environments. The results obtained show that, in both Fixed SNR and Fixed Tx for deterministic condition, the channel capacity dynamic range rose with the number of pedestrians as well as with the number of antenna combinations. In random scenarios with 10 pedestrians, an increment in channel capacity of up to 0.89 bits/sec/Hz in Fixed SNR and up to 1.52 bits/sec/Hz in Fixed Tx has been recorded compared to the one pedestrian scenario. In addition, from the results a maximum increase in average channel capacity of 49% has been measured while 4 antenna elements are used, compared with 2 antenna elements. The highest measured average capacity, 11.75 bits/sec/Hz, corresponds to the 4x4 array with 10 pedestrians moving randomly. Moreover, Additionally, the spread between the highest and lowest value of the the dynamic range is larger for Fixed Tx, predicted 5.5 bits/sec/Hz and measured 1.5 bits/sec/Hz, in comparison with Fixed SNR criteria, predicted 1.5 bits/sec/Hz and measured 0.7 bits/sec/Hz. This has been confirmed by both measurements and simulations ranging from 1 to 5, 7 and 10 pedestrians.

An optimized online secondary path modeling method for single-channel feedback ANC systems

This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the proposed system there is no need for using offline estimation. The proposed method consists of two steps: a noise controller which is based on an FxLMS algorithm, and a variable step size (VSS) LMS algorithm which is used to adapt the modeling filter with the secondary path. In order to increase performance of the algorithm in a faster convergence and accurate performance, we stop the VSS-LMS algorithm at the optimum point. The results of computer simulation shown in this paper indicate effectiveness of the proposed method.

Novel channel tracking and equalization methods in MU-MIMO-OFDM systems

The methodology undertaken, the channel model and the system model created for developing a novel adaptive equalization method and a novel channel tracking method for uplink of MU-MIMO-OFDM systems is presented in this paper. The results show that the channel tracking method works with 97% accuracy, while the training-based initial channel estimation method shows poor performance in estimating the actual channel comparatively.

Optimal Weighted Antenna Selection For Imperfect Channel Knowledge From Training

Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the ``best'' antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.

Tracking analysis of an LMS decision feedback equalizer for a wireless channel

We consider a time varying wireless fading channel, equalized by an LMS Decision Feedback equalizer (DFE). We study how well this equalizer tracks the optimal MMSEDFE (Wiener) equalizer. We model the channel by an Autoregressive (AR) process. Then the LMS equalizer and the AR process are jointly approximated by the solution of a system of ODEs (ordinary differential equations). Using these ODEs, we show via some examples that the LMS equalizer moves close to the instantaneous Wiener filter after initial transience. We also compare the LMS equalizer with the instantaneous optimal DFE (the commonly used Wiener filter) designed assuming perfect previous decisions and computed using perfect channel estimate (we will call it as IDFE). We show that the LMS equalizer outperforms the IDFE almost all the time after initial transience.

Channel training signal design for reciprocal multiple antenna systems with beamforming

Fast and efficient channel estimation is key to achieving high data rate performance in mobile and vehicular communication systems, where the channel is fast time-varying. To this end, this work proposes and optimizes channel-dependent training schemes for reciprocal Multiple-Input Multiple-Output (MIMO) channels with beamforming (BF) at the transmitter and receiver. First, assuming that Channel State Information (CSI) is available at the receiver, a channel-dependent Reverse Channel Training (RCT) signal is proposed that enables efficient estimation of the BF vector at the transmitter with a minimum training duration of only one symbol. In contrast, conventional orthogonal training requires a minimum training duration equal to the number of receive antennas. A tight approximation to the capacity lower bound on the system is derived, which is used as a performance metric to optimize the parameters of the RCT. Next, assuming that CSI is available at the transmitter, a channel-dependent forward-link training signal is proposed and its power and duration are optimized with respect to an approximate capacity lower bound. Monte Carlo simulations illustrate the significant performance improvement offered by the proposed channel-dependent training schemes over the existing channel-agnostic orthogonal training schemes.

Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning

It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.

Wireless scheduling with partial channel state information: large deviations and optimality

We consider a server serving a time-slotted queued system of multiple packet-based flows, where not more than one flow can be serviced in a single time slot. The flows have exogenous packet arrivals and time-varying service rates. At each time, the server can observe instantaneous service rates for only a subset of flows ( selected from a fixed collection of observable subsets) before scheduling a flow in the subset for service. We are interested in queue length aware scheduling to keep the queues short. The limited availability of instantaneous service rate information requires the scheduler to make a careful choice of which subset of service rates to sample. We develop scheduling algorithms that use only partial service rate information from subsets of channels, and that minimize the likelihood of queue overflow in the system. Specifically, we present a new joint subset-sampling and scheduling algorithm called Max-Exp that uses only the current queue lengths to pick a subset of flows, and subsequently schedules a flow using the Exponential rule. When the collection of observable subsets is disjoint, we show that Max-Exp achieves the best exponential decay rate, among all scheduling algorithms that base their decision on the current ( or any finite past history of) system state, of the tail of the longest queue. To accomplish this, we employ novel analytical techniques for studying the performance of scheduling algorithms using partial state, which may be of independent interest. These include new sample-path large deviations results for processes obtained by non-random, predictable sampling of sequences of independent and identically distributed random variables. A consequence of these results is that scheduling with partial state information yields a rate function significantly different from scheduling with full channel information. In the special case when the observable subsets are singleton flows, i.e., when there is effectively no a priori channel state information, Max-Exp reduces to simply serving the flow with the longest queue; thus, our results show that to always serve the longest queue in the absence of any channel state information is large deviations optimal.

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Iterative Multiuser Detection and Decoding for DS-CDMA System With Space-Time Linear Dispersion

This paper considers a Q-ary orthogonal direct-sequence code-division multiple-access (DS-CDMA) system with high-rate space-time linear dispersion codes (LDCs) in time-varying Rayleigh fading multiple-input-multiple-output (MIMO) channels. We propose a joint multiuser detection, LDC decoding, Q-ary demodulation, and channel-decoding algorithm and apply the turbo processing principle to improve system performance in an iterative fashion. The proposed iterative scheme demonstrates faster convergence and superior performance compared with the V-BLAST-based DS-CDMA system and is shown to approach the single-user performance bound. We also show that the CDMA system is able to exploit the time diversity offered by the LDCS in rapid-fading channels.