We provide an overview of the extensive results on the Shannon capacity of single-user and multiuser multiple-input multiple-output (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about the underlying time-varying channel model and how well it can be tracked at the receiver, as well as at the transmitter. More realistic assumptions can dramatically impact the potential capacity gains of MIMO techniques. For time-varying MIMO channels there are multiple Shannon theoretic capacity definitions and, for each definition, different correlation models and channel information assumptions that we consider. We first provide a comprehensive summary of ergodic and capacity versus outage results for single-user MIMO channels. These results indicate that the capacity gain obtained from multiple antennas heavily depends on the available channel information at either the receiver or transmitter, the channel signal-to-noise ratio, and the correlation between the channel gains on each antenna element. We then focus attention on the capacity region of the multiple-access channels (MACs) and the largest known achievable rate region for the broadcast channel. In contrast to single-user MIMO channels, capacity results for these multiuser MIMO channels are quite difficult to obtain, even for constant channels. We summarize results for the MIMO broadcast and MAC for channels that are either constant or fading with perfect instantaneous knowledge of the antenna gains at both transmitter(s) and receiver(s). We show that the capacity region of the MIMO multiple access and the largest known achievable rate region (called the dirty-paper region) for the MIMO broadcast channel are intimately related via a duality transformation. This transformation facilitates finding the transmission strategies that achieve a point on the boundary of the MIMO MAC capacity region in terms of the transmission strategies of the MIMO broadcast dirty-paper region and vice-versa. Finally, we discuss capacity results for multicell MIMO channels with base station cooperation. The base stations then act as a spatially diverse antenna array and transmission strategies that exploit this structure exhibit significant capacity gains. This section also provides a brief discussion of system level issues associated with MIMO cellular. Open problems in this field abound and are discussed throughout the paper.

/pdf/capacity-limits-of-mimo-channels-aigb402z31.pdf

Capacity limits of MIMO channels

Spatial modulation (SM) is a recently developed transmission technique that uses multiple antennas. The basic idea is to map a block of information bits to two information carrying units: 1) a symbol that was chosen from a constellation diagram and 2) a unique transmit antenna number that was chosen from a set of transmit antennas. The use of the transmit antenna number as an information-bearing unit increases the overall spectral efficiency by the base-two logarithm of the number of transmit antennas. At the receiver, a maximum receive ratio combining algorithm is used to retrieve the transmitted block of information bits. Here, we apply SM to orthogonal frequency division multiplexing (OFDM) transmission. We develop an analytical approach for symbol error ratio (SER) analysis of the SM algorithm in independent identically distributed (i.i.d.) Rayleigh channels. The analytical and simulation results closely match. The performance and the receiver complexity of the SM-OFDM technique are compared to those of the vertical Bell Labs layered space-time (V-BLAST-OFDM) and Alamouti-OFDM algorithms. V-BLAST uses minimum mean square error (MMSE) detection with ordered successive interference cancellation. The combined effect of spatial correlation, mutual antenna coupling, and Rician fading on both coded and uncoded systems are presented. It is shown that, for the same spectral efficiency, SM results in a reduction of around 90% in receiver complexity as compared to V-BLAST and nearly the same receiver complexity as Alamouti. In addition, we show that SM achieves better performance in all studied channel conditions, as compared with other techniques. It is also shown to efficiently work for any configuration of transmit and receive antennas, even for the case of fewer receive antennas than transmit antennas.

Spatial Modulation

We present an experiment-based characterization of passive suppression and active self-interference cancellation mechanisms in full-duplex wireless communication systems. In particular, we consider passive suppression due to antenna separation at the same node, and active cancellation in analog and/or digital domain. First, we show that the average amount of cancellation increases for active cancellation techniques as the received self-interference power increases. Our characterization of the average cancellation as a function of the self-interference power allows us to show that for a constant signal-to-interference ratio at the receiver antenna (before any active cancellation is applied), the rate of a full-duplex link increases as the self-interference power increases. Second, we show that applying digital cancellation after analog cancellation can sometimes increase the self-interference, and thus digital cancellation is more effective when applied selectively based on measured suppression values. Third, we complete our study of the impact of self-interference cancellation mechanisms by characterizing the probability distribution of the self-interference channel before and after cancellation.

/pdf/experiment-driven-characterization-of-full-duplex-wireless-3u33o8w3q1.pdf

Experiment-Driven Characterization of Full-Duplex Wireless Systems

Mathematica--A System for Doing Mathematics by Computer.

The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information and so on, blind identification of the modulation is a difficult task. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels. With this in mind, the authors provide a comprehensive survey of different modulation recognition techniques in a systematic way. A unified notation is used to bring in together, under the same umbrella, the vast amount of results and classifiers, developed for different modulations. The two general classes of automatic modulation identification algorithms are discussed in detail, which rely on the likelihood function and features of the received signal, respectively. The contributions of numerous articles are summarised in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the results reported in the literature have been obtained under different conditions. So, we have also simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Furthermore, new problems that have appeared as a result of emerging wireless technologies are outlined. Finally, open problems and possible directions for future research are briefly discussed.

Survey of automatic modulation classification techniques: classical approaches and new trends

Recent studies have shown that a compact underwater vector sensor receiver can perform as an array of scalar sensors. Here we propose a scheme to transmit data via underwater particle velocity channels using a dipole vector sensor. System equations are derived and simulations are presented. This demonstrates the possibility of digital transmission using a vector sensor.

Digital transmission via underwater acoustic particle velocity channels

The subject matter disclosed herein relates to techniques for estimating a speed of an object that transmits a signal at a carrier frequency.

System and/or Method for Estimating Speed of a Transmitting Object

The present disclosure provides advantageous systems and methods for identifying molecular vulnerabilities in biological pathways and networks The present disclosure generally involves conceptualizing a disease/disorder at the molecular level as a faulty physiological system, wherein one or more molecules in the complex intracellular signaling network are dysfunctional This is accomplished by modeling a given physiological system as a digital logic circuit More particularly, in exemplarily embodiments, binary logic equations are derived by analyzing the interactions between the input and output nodes of a target biological system These equations are then used to produce a digital circuit representation for the system Once a digital circuit representation is created, this circuit may advantageously be analyzed, using fault analysis techniques, in order to determine the vulnerability levels of the molecules of the targeted system

Systems and methods for fault diagnosis in molecular networks

Supplementary Materials for Fault Diagnosis Engineering of Digital Circuits Can Identify Vulnerable Molecules in Complex Cellular Pathways

A new narrowband MIMO channel model for microcells is introduced, and different aspects of the model, such as spatial, spatio-temporal, level crossing rate, and average fade duration are compared with the measured data, to verify the proposed model. The results exhibit the utility of the new parametric model. The proposed model serves as a useful tool for design and simulation of MIMO systems in microcellular environment.

Ali Abdi

Papers

Digital transmission via underwater acoustic particle velocity channels

System and/or Method for Estimating Speed of a Transmitting Object

Systems and methods for fault diagnosis in molecular networks

Supplementary Materials for Fault Diagnosis Engineering of Digital Circuits Can Identify Vulnerable Molecules in Complex Cellular Pathways

A time-varying MIMO channel model: theory and measurements