Polyanskiy [2] proposed a framework for the MAC problem where users employ a common codebook in the finite blocklength regime. In this work, we extend [2] to the case when each user generates a packet independently and asynchronously according to identical renewal processes. Each packet must be decoded with a delay no greater than d, which is a strict delay constraint. We derive a general converse bound for our system model before conducting a comparative analysis of several achievable transmission schemes. First, we consider a transmission scheme where each user transmits a packet as soon as it is generated and suffers interference from other users. We treat interference as noise (TIN). Then, we investigate block transmission where users transmit jointly in fixed slots. Finally, we explore packet splitting, allowing users to divide each packet into two parts with different blocklengths. Using optimized information bit allocation, we discuss two analytical approaches: dependence testing and Gaussian ball. Our numerical results indicate that, when dealing with large delays, TIN performs well compared to block transmission. Conversely, block transmission has the advantage as the system spectral efficiency increases. Packet splitting outperforms other schemes consistently.

This paper analyzes energy consumption in wireless streaming with delay constraints. One fundamental question is whether the transmitter should transmit a steady stream of bits, continuous transmission, or transmit data in bursts and switch off while not transmitting. In the latter case, a question is also for what fraction of time the transmitter should transmit, the duty cycle. In this paper we use traditional and finite blocklength information theory to see whether bursty transmission would be better than transmitting data continuously. When doing this analysis we consider latency and energy efficiency, two fundamental parameters that characterize communication systems. We take into account realistic models of hardware, including overhead power, power amplifier inefficiency and the receiver noise factor. With these models we find that the energy consumption and latency tradeoff behaves quite differently from the ideal case.

Multi-access edge computing (MEC) is seen as a vital component of forthcoming 6G wireless networks, aiming to support emerging applications that demand high service reliability and low latency. However, ensuring the ultra-reliable and low-latency performance of MEC networks poses a significant challenge due to uncertainties associated with wireless links, constraints imposed by communication and computing resources, and the dynamic nature of network traffic. Enabling ultra-reliable and low-latency MEC mandates efficient load balancing jointly with resource allocation. In this paper, we investigate the joint optimization problem of offloading decisions, computation and communication resource allocation to minimize the expected weighted sum of delivery latency and energy consumption in a non-orthogonal multiple access (NOMA)-assisted MEC network. Given the formulated problem is a mixed-integer non-linear programming (MINLP), a new multi-agent federated deep reinforcement learning (FDRL) solution based on double deep Q-network (DDQN) is developed to efficiently optimize the offloading strategies across the MEC network while accelerating the learning process of the Internet-of-Thing (IoT) devices. Simulation results show that the proposed FDRL scheme can effectively reduce the weighted sum of delivery latency and energy consumption of IoT devices in the MEC network and outperform the baseline approaches.

Given a default distribution$P$and a set of test data$x^{M}=\{x_{1},\ x_{2},\ \ldots,\ x_{M}\}$this paper seeks to answer the question if it was likely that$x^{M}$was generated by$P$. For discrete distributions, the definitive answer is in principle given by Kolmogorov-Martin-Lof randomness. In this paper we seek to generalize this to continuous distributions. We consider a set of statistics$T_{1}(x^{M}), T_{2}(x^{M}),\cdots$. To each statistic we associate its maximum entropy distribution and with this a universal source coder. The maximum entropy distributions are subsequently combined to give a total codelength, which is compared with$-\log P(x^{M})$. We show that this approach satisfied a number of theoretical properties. For real world data$P$usually is unknown. We transform data into a standard distribution in the latent space using a bidirectional generate network and use maximum entropy coding there. We compare the resulting method to other methods that also used generative neural networks to detect anomalies. In most cases, our results show better performance.

This paper develops bounds for learning lossless source coding under the PAC (probably approximately correct) framework. The paper considers iid sources with online learning: first the coder learns the data structure from training sequences. When presented with a test sequence for compression, it continues to learn from/adapt to the test sequence. The results show, not unsurprisingly, that there is little gain from online learning when the training sequence length is much longer than the test sequence length. But if the test sequence length is longer than the training sequence, there is a significant gain. Coders for online learning has a somewhat surprising structure: the training sequence is used to estimate a confidence interval for the distribution, and the coding distribution is found through a prior distribution over this interval.

A classic application of description length is for model selection with the minimum description length (MDL) principle. The focus of this paper is to extend description length for data analysis beyond simple model selection and sequences of scalars. More specifically, we extend the description length for data analysis in Gaussian graphical models. These are powerful tools to model interactions among variables in a sequence of i.i.d Gaussian data in the form of a graph. Our method uses universal graph coding methods to accurately account for model complexity, and therefore provide a more rigorous approach for graph model selection. The developed method is tested with synthetic and electrocardiogram (ECG) data to find the graph model and anomaly in Gaussian graphical models. The experiments show that our method gives better performance compared to commonly used methods.

This paper asks a basic question: how much training is required to beat a universal source coder? Traditionally, there have been two types of source coders: fixed, optimum coders such as Huffman coders; and universal source coders, such as Lempel-Ziv. The paper considers a third type of source coders: learned coders. These are coders that are trained on data of a particular type, and then used to encode new data of that type. This is a type of coder that has recently become popular for (lossy) image and video coding. The paper evaluates two criteria for performance of learned coders: the average performance over training data, and a guaranteed performance for all training except for some error probability Pe, which is PAC learning. In both cases the coders are evaluated with respect to redundancy. The paper considers the independent identically distributed (IID) binary case and binary Markov chains. In both cases it is shown that the amount of training data required is very moderate: to code sequences of length$l$the amount of training data required to beat a universal source coder is$m=K\frac{l}{\log l}$, where the constant$K$depends on the case considered.

This paper analyzes the performance of discrete input distributions (coded modulation) in interference channels. This approach is motivated in part by the necessity of using coded modulation in practical systems, and in part by the potential of discrete distributions for interference alignment as well as the importance demonstrated by Dytso et al. of discrete input distributions for transmission over the 2 × 2 interference channel when treating interference as noise. The contribution of this work includes the establishment of achievable rates subject to discrete modulations. In the process, new bounds involving the minimum distance of the sum of discrete modulations have been developed that are useful for facilitating further work in this area. These bounds are then used for finding achievable rates for 3 × 3 interference channels. It is also shown that interference alignment can be efficiently employed with discrete modulations at finite SNR even with imperfect channel state information at the transmitter.

This paper investigates basic trade-offs between energy and delay in wireless communication systems using finite blocklength theory. We first assume that data arrive in constant stream of bits, which are put into packets and transmitted over a communications link. Our results show that depending on exactly how energy is measured, in general energy depends √d-1or √Vd-1log d, where d is the delay. This means on that the energy decreases quite slowly with increasing delay. Furthermore, to approach the absolute minimum of -1.59 dB on energy, bandwidth has to increase very rapidly, much more than what is predicted by infinite blocklength theory. We then consider the scenario when data arrive stochastically in packets and can be queued. We devise a scheduling algorithm based on finite blocklength theory and develop bounds for the energy-delay performance. Our results again show that the energy decreases quite slowly with increasing delay.

In[1], the affiliation of A. Høst-Madsen was incorrect. The affiliation should read: A. Høst-Madsen is with the Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, HI 96822 USA (e-mail: [email protected]).

We investigate the performance of discrete (coded) modulations in the full-duplex compress-and-forward relay channel using multilevel coding. We numerically analyze the rates assigned to component binary codes of all levels. LDPC codes are used as the component binary codes to provide error protection. The compression at the relay is done via a scalar quantizer whose output is mapped to a codeword through LDPC codes. A compound Tanner graphical model and information exchange algorithm are described for the joint decoding of both messages sent from the source and relay. Simulation results show that the performance of the proposed system based on multilevel coding is better than that based on BICM, and is separated from the SNR threshold of the known compress-and-forward achievable rate by two factors consisting approximately of the sum of the shaping gain (due to the scalar quantization) and the separation of the LDPC code implementation from AWGN capacity.

A central question in the era of big data is what to do with the enormous amount of information. One possibility is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand the general structure of the overall data. The perspective in this paper is the opposite, namely that most of the value in the information in some applications is in the parts that deviate from the average, that are unusual, atypical. We define what we mean by atypical in an axiomatic way as data that can be encoded with fewer bits in itself rather than using the code for the typical data. We show that this definition has good theoretical properties. We then develop an implementation based on universal source coding, and apply this to a number of real world data sets.

This paper studies the low-SNR regime performance of a scalar complex K-user interference channel with the Gaussian noise. The finite bandwidth case is considered, where the low-SNR regime is approached by letting the input power go to zero, while the bandwidth is small and fixed. We show that for all δ > 0, there exists a set of channel coefficients with non-zero measure (probability), in which the wideband slope per user satisfies S0<; 2/K +δ. This is quite contrary to the large bandwidth case, where a slope of 1 per user is achievable with probability 1. We also develop an interference alignment scheme for the finite bandwidth case that shows some gain in wideband slope.

This paper has dual aims. First is to develop practical universal coding methods for unlabeled graphs. Second is to use these for graph anomaly detection. The paper develops two coding methods for unlabeled graphs: one based on the degree distribution, the second based on the triangle distribution. It is shown that these are efficient for different types of random graphs, and on real-world graphs. These coding methods is then used for detecting anomalous graphs, based on structure alone. It is shown that anomalous graphs can be detected with high probability.

We pursue interference mitigation via the integration of two key ideas. First, understanding the behavior of available rates under discrete signaling which has recently been shown to be promising in the interference channel. This part calls for calculation of good bounds on post-interference mutual information under discrete signaling, as a function of the forward and cross channel gains. Second, the capacity of the interference channel is known to be very irregular, so for any target capacity there are “outage” sets that we aim to avoid by using reconfigurable antennas. For the first component, we report an analytical lower bound on the mutual information and establish a constant gap O(logγ) to capacity (excepting an outage set) that is derived using a purely discrete signaling. This result outperforms the gap reported by Dytso et al that was generated via a mixed discrete-continuous input strategy. In the second part of this work, we propose to use a reconfigurable antenna technology involving liquid metal antennas that can steer the channel away from the outage scenarios and therefore facilitate higher values of coded rates for the two-user symmetric Gaussian interference channel. The viability of the proposed technique is studied via simulations.

This paper analyzes the performance of discrete input distributions (coded modulation) in certain 3 user interference channels. This approach is motivated in part by the necessity of using coded modulation in practical systems, and in part by the potential of discrete distributions for interference alignment as well as the demonstrated importance of discrete input distributions for transmission over the 2 × 2 interference channel when treating interference as noise. The contribution of this work includes the establishment of achievable rates subject to discrete (PAM) modulations. In the process, new bounds involving the minimum distance of the sum of discrete modulations have been developed that are useful for facilitating further work in this area.

With the enormous amount of data generated through the internet and sensors, Internet of Things, it becomes too overwhelming for humans to examine it all. One solution is to reduce the data to a set of statistics. The perspective in this paper is the opposite, namely that most of this data is just background noise, and the interesting parts are those that deviate from background noise, the parts that are atypical. In order to find such “interesting” parts of data, universal approaches are required, since it is not known in advance what we are looking for. Our approach is to use Rissanen's minimum description length (MDL) as a tool for that. We would like to be able to find both short and long atypical sequences of data, and we therefore need accurate expressions of MDL, without prior assumptions. In this paper we develop a modified predictive MDL method that works better for short sequences.

With the enormous amount of data generated through the internet and sensors, Internet of Things, it becomes too overwhelming for humans to examine it all. One solution is to reduce the data to a set of statistics. The perspective in this paper is the opposite, namely that most of this data is just background noise, and the interesting parts are those that deviate from background noise, the parts that are atypical. In order to find such “interesting” parts of data, universal approaches are required, since it is not known in advance what we are looking for. The paper develops new algorithms for detecting atypical data based on information theory concepts. These are applied to a number of real-world data.

With the exponential growth in information, “Big Data,” a key question is what to do with this information. A classic possibility is to characterize it through statistics. The perspective in this paper is the opposite, namely that most of the value in the information is in the parts that deviates from the average, that are unusual, atypical. Think of art: The valuable paintings or writings are those that deviate from the norms, that are atypical. With the same perspective think of social networks: Flickr is an example that has a rating for images based on how interesting they are. In previous works, researchers introduced algorithms to find the interesting images based on human psychological taste; but in this paper, we are proposing a new method to rate an image based on their level of interestingness using atypicality, in which interestingness is equivalent to having atypical structure. The paper first discusses what exactly should be understood by “atypical.” It has to be a well defined theoretical concept corresponding to some intuitive idea of atypicality, which when applied gives useful results. This is followed by applying the notion of atypicality to the general discrete scenario with finite alphabet and then putting it in to the framework of the quantized wavelet image coding. Finally our proposed algorithm is applied on Labeled Faces in the Wild (LFW) database with 13,233 images in order to find the interesting ones.

Atypical sequences are subsequences of long sequences that deviates from the ‘normal’ data. In a previous paper we have developed an information theory approach to such sequences for discrete data. In the current paper we extend this principle to real-valued data, whereby it is possible to use signal processing tools to search for atypical data. The application of this principle is to extract a few interesting sets of information from ‘big data’ sets. We include a simple application to stock market data.

The deterministic-coding capacity of a network is the capacity of a network when nodes are restricted to transmitting reliable information, that is, (asymptotically) deterministic functions of the source messages. This is a generalization of network coding to wireless networks. The main contribution of this paper is to develop new outer bounds on the deterministic-coding capacity in the low-power regime,$ \mathrm {SNR}\to 0$. This is accomplished by developing a technique for finding the limits of single letter bounds for$ \mathrm {SNR}\to 0$even when these cannot be evaluated for finite SNR. It is shown that these outer bounds are tight in a number of cases, including the butterfly network of Ahlswedeet al.in a wireless setting.

Welcome to Paradise. It is our utmost pleasure to welcome you to the city of Honolulu, Hawaii and to the 2014 IEEE International Symposium on Information Theory. We hope that you will find the Symposium technically rewarding and the Hawaiian nature/ambiance equally interesting.

In this paper, we investigate the minimum energy of transmitting correlated sources over the Gaussian multiple-access channel. Compared to other works on joint source-channel coding, we consider the fundamental problem of the minimum transmission energy, where the source and channel bandwidths are not naturally matched. Different models of correlated sources are studied. We first treat lossy transmission of Gaussian sources, including multiterminal sources and CEO sources. We then consider lossless transmission of correlated binary sources. In all cases, we lower bound the minimum energy using a cut-set argument that couples transmission energy and the distortions for the Gaussian cases (or source entropy for the discrete case). For the achievable schemes, separate source and channel coding and uncoded transmission are studied as benchmarks. In addition, we show that hybrid digital/analog transmission achieves the best known energy efficiency.

We study the minimum energy of sending Gaussian multiterminal sources over the Gaussian multiple access channel (MAC). Distributed transmitters observe Gaussian multiterminal sources and describe their observations to a central decoder, which desires to reconstruct the sources under MSE constraints. We first lower bound the minimum energy by a cut-set argument which couples the transmitted signals and reconstruction errors. For achievability, separate source-channel coding is first studied as a benchmark. We then find out the minimum energy that can be achieved uncoded transmission. A hybrid digital/analog scheme is proposed to achieve the best known energy performance.

One characteristic of the information age is the exponential growth of information, and the ready availability of this information through networks, including the internet - “Big Data.” The question is what to do with this enormous amount of information. One possibility is to characterize it through statistics - think averages. The perspective in this paper is the opposite, namely that most of the value in the information is in the parts that deviate from the average, that are unusual, atypical. Think of art: the valuable paintings or writings are those that deviate from the norms, that are atypical. The same could be true for venture development and scientific research. The paper first discusses what exactly should be understood by “atypical.” This is by no means straightforward. It has to be a well defined theoretical concept corresponding to some intuitive idea of atypicality, which when applied gives useful results. This is followed by a simple example of iid binary sequences. This example is simple enough that complete algorithms can be developed and analyzed, which give insights into atypicality. We finally develop a more general algorithm based on the Context Tree Weighing algorithm and apply that to heart rate variability.

This paper develops a methodology for finding minimum energy per bit in networks with correlated information, without at need for finding bounds on capacity. This is used to derive the exact minimum energy per bit for some broadcast and interference channels with common and correlated messages, and bounds on the minimum energy per bit for some other channels.

It is well known that minimum received energy per bitEb/N0|minin the interference channel is -1.59 dB as if there were no interference. Thus, the best way to mitigate interference is to operate the interference channel in the low-signal-to-noise-ratio ( SNR) regime. However, when the SNR is small but nonzero,Eb/N0|minalone does not characterize performance. Verdu introduced the wideband slopeS0to characterize the performance in this regime. We show that a wideband slope ofS0/S0, no interference= 1/2 is achievable. This result is similar to recent results on degrees of freedom in the high-SNR regime, and we use a type of interference alignment using delays to obtain the result. We also show that in many cases, the wideband slope is upper bounded byS0/S0, no interference≤ 1/2 for large number of usersK.

A network code is said to be reliable when all transmissions in the network are (deterministic) functions of the source messages; well-known examples include decode-forward for relay networks. It is said to be unreliable when transmissions depend on the noise realization at nodes; examples include compress-forward and amplify-forward. The deterministic capacity of a network is defined as the supremum of the rates achievable by reliable codes. In this paper we derive the deterministic capacity of some relay networks in the low power regime. The resulting energy per bit is then compared with the one achievable by arbitrary transmission.

We consider a cooperative wireless multicast channel with no channel state information available at the transmitters, and where all wireless links undergo independent identically distributed quasi-static Rayleigh fading. For cooperation, we propose a rateless coded approach and identify its benefits over a conventional fixed-rate two-hop strategy. In addition to providing robust communications under fading conditions, rateless coding is able to exploit the cooperation benefits to a greater degree. A hybrid rateless coded cooperation scheme is proposed which employs amplify-and-forward (AF) in conjunction with decode-and-forward (DF) strategy. Results based on Monte-Carlo simulations indicate that the rateless coded hybrid scheme is able to significantly outperform two-hop cooperation, as well as the individual AF and DF strategies.

This paper derives a formula for the wideband slope for outage capacity and consider its application to some specific wireless channels. For the broadcast channel, the formula indicates that superposition is always superior to time-division multiple access (TDMA). On the other hand, for the interference channel, we show that TDMA is better than superposition for most realistic situations.

In this paper we investigate the wideband slope of a K-user interference channel. We show that delay-based inteference alignment can achieve a wideband slope of S0over S0, no interference = 1 over 2 independent of the number of users K, when the delays are known exactly. This result is similar to recent results on degrees of freedom in the high SNR regime. We also show that in many cases the wideband slope is upper bounded by S0over S0, no interference ≤ 1 over 2 for large number of users K. We then consider delay uncertainty at the transmitter side. We present a modified delay-based interference alignment scheme. Its achievable wideband slope can get arbitrarily close S0over S0, no interference = 1 over 2 -the best achievable wideband slope for the channel without delay uncertainty‥

This paper develops a methodology for finding minimum energy per bit in networks with correlated information, without needing for find bounds on capacity. This is used to derive the minimum energy per bit for certain non-cooperative networks with common messages, and bounds on the minimum energy per bit in a MIMO broadcast channel with correlated information.

We consider outage capacity in the broadcast channel when the base-station has none or little channel knowledge. We find the minimum energy per bit needed to achieve a certain outage probability and the corresponding wideband slope.

This paper considers cooperation in the low power/SNR regime for the multiple access channel. We assume that transmitters have no channel state information, and consider the outage capacity. We further assume that the nodes operate in half duplex. The nodes are multiplexed by assigning to each node a unique frequency band via frequency division multiplexing (FDM). A node transmits only on this band while listening on the others. We perform outage wideband analysis on FDM based cooperation, and show perhaps surprisingly that FDM loses very little in performance compared to the idealized situation where nodes can operate in full duplex. We then develop practical rateless coding methods for FDM using Raptor codes.

In this paper, the achievable rate of a K-user interference channel with single antenna at individual node is studied. Interference alignment and zero forcing are applied at the transmitter and receiver, respectively. It is well known from the paper [1] of Cadambe and Jafar that a multiplexing gain (degrees of freedom) of K/2 is achievable. We propose two new pre-coding methods and study how they enhance the achievable rate in terms of the high SNR offset, while maintain the optimality of degree of freedom the Cadambe-Jafar scheme achieved.

The combined use of a Doppler radar with digital signal processing technique gives an effective non-invasive remote sensing of heart beat signals. Initial results have showed that the proposed technique is very promising in successfully isolating desired heart beat signals from other mobile objects and other distortion effects characterizing the wireless channel. We concentrate in this paper on the harder problem of separating at low SNR the signals from two subjects in the same room. We show that preliminary results obtained by the real analytical constant modulus algorithm (RACMA) and the independent component analysis (ICA) on experimental data are very promising into this goal.

In this paper we develop practical rateless coded cooperation strategies for a two-user multiple access channel. At the heart of our practical strategies lie Raptor codes concatenated with a space-time code, and an iterative decoding procedure to jointly recover the two users' messages at the base station. Since cooperation in multiple access channels is known to be particularly beneficial when considering the outage probability at low SNRs, or equivalently at low transmission rates, we simulate our strategies at a transmission rate of 0.25 bits/sample. Experiments indicate that our schemes perform very close to the theoretical limit, with the performance gap being less than 0.55 dB.

Nonlinear precoding schemes for downlink time-division duplex–CDMA systems over multipath fading channels, are considered. First, the capacity results of a downlink CDMA system with either multiuser detection or precoding, were obtained and compared. It is seen that the two schemes exhibit similar capacity regions for both sum rate and maximum equal rate, which motivates the development of efficient nonlinear transmitter precoding techniques to reduce the receiver complexity at the mobile units without degrading the system performance. We then develop both bit-wise and chip-wise Tomlinson–Harashima (TH) multiuser precoding methods for downlink CDMA with multipath, to remove multi-user interference, inter-chip interference and inter-symbol interference. Efficient algorithms for multiuser power loading and ordering are also developed. Implementation of the proposed TH-precoding schemes in time-varying channels based on channel prediction is addressed as well. Simulations results are provided to demonstrate the effectiveness of the proposed techniques in suppressing interference in downlink CDMA.

Abstract — In this paper, we compare two classes of linear interference suppression techniques for downlink TDD-CDMA systems, namely, linear multiuser detection methods (receiver processing) and linear precoding methods (transmitter processing). For the linear precoding schemes, we assume that the channel state information (CSI) is available only at the transmitter but not at the receiver (i.e., ultra simple receivers). We propose several precoding techniques and the corresponding power control algorithms. The performance metric used in the comparisons is the total power required at the transmitter to achieve a target SINR at the receiver. Our results reveal that in general multiuser detection and precoding offer similar performance; but in certain scenarios (e.g, low BER requirements or use of random spreading sequences), precoding can bring a substantial performance improvement. These results motivate the use of precoding techniques to reduce the complexity of the system and the mobile terminals (only a matched-filter to the own spreading sequence is required without CSI). Moreover, it is shown that the proposed chip-wise linear MMSE precoding method is optimal in the sense that it requires the minimum total transmitted power to meet a certain receiver SINR performance. Index Terms — Downlink CDMA, linear multiuser detection, linear precoding, power control. I.

Doppler radar life sensing has shown promise in medical and security applications. The current paper considers the problem of determining the number of persons in a given area (e.g., a room) using the Doppler shift due to heartbeat. The signal is weak and time-varying, and therefore poses a complicated signal processing problem. We develop a generalized likelihood ratio test (GLRT) based on a model of the heartbeat, and show that this can be used to distinguish between the presence of 2, 1, or 0 subjects, even with a single antenna. We further extend this to N antennas. The results show that one can expect to detect up to 2N-1 subjects using this technique

Current research on cooperation in wireless communications has mainly focused on ergodic capacity in high SNR (or at least finite SNR) case. We show that cooperation is more efficient in the low power regime when considering outage capacity. We develop cooperation methods that are particularly efficient in the low power regime. They can be easily scaled to large networks when combined with rateless codes. The results show that there is a large gain in Eb/N0|minfrom cooperation when considering outage capacity in the low power regime. This is in contrast to the fact that in a Gaussian channel, we can at most change the slope of Eb/N0

In a cooperative diversity network, users cooperate to transmit each others' messages; to some extent nodes therefore collectively act as an antenna array and create a virtual or distributed multiple-input multiple-output (MIMO) system. In this paper, upper and lower bounds for the information-theoretic capacity of four-node ad hoc networks with two transmitters and two receivers using cooperative diversity are derived. One of the gains in a true MIMO system is a multiplexing gain in the high signal-to-noise ratio (SNR) regime, an extra factor in front of the log in the capacity expression. It is shown that cooperative diversity gives no such multiplexing gain, but it does give a high SNR additive gain, which is characterized in the paper

We consider the minimum energy unicast problem in ad-hoc wireless networks. A source node wants to transmit a sequence of messages to a single destination node. Other nodes in the network may operate as relay nodes. The problem is to do this with the minimum overall power consumption in the network. In this paper we investigate the power saving that can be achieved by using advanced relay-channel signaling rather than traditional multi-hop routing. Under the assumptions that carrier level synchronization is not available and every intermediate node uses a decode-forward scheme, we show that any optimal relaying structure can be converted to a sequential path, for which we find a simple optimal power-allocation policy. Finding an optimal sequential path is still complex, and we therefore present two polynomial heuristic algorithms to find good cooperative routes. Our simulation results show that they can achieve average power savings of up to 3 dB for a network size of 60 nodes in low attenuation regimes. For high attenuation regimes, cooperative routing exhibits limited gain and traditional multi-hop operation is a reasonable choice.

At high SNR the capacity of a point-to point MIMO system with NTtransmit antenna and NRreceive antenna is min{NT, NR} log(SNR) + O(1). The factor in front of the log is called the multiplexing gain. In this paper we consider a network with 2N nodes (N source destination pairs) that each have only a single antenna. These single antenna nodes could cooperate to form larger virtual arrays, usually called cooperative diversity, user cooperation, or coded cooperation. The question we ask is: how large a multiplexing gain is possible. We prove that for N = 2 the multiplexing gain is 1, and consider generalizations to larger networks

We consider three-node wireless relay channels in a Rayleigh-fading environment. Assuming transmitter channel state information (CSI), we study upper bounds and lower bounds on the outage capacity and the ergodic capacity. Our studies take into account practical constraints on the transmission/reception duplexing at the relay node and on the synchronization between the source node and the relay node. We also explore power allocation. Compared to the direct transmission and traditional multihop protocols, our results reveal that optimum relay channel signaling can significantly outperform multihop protocols, and that power allocation has a significant impact on the performance.

We study the capacity of multiple-input multiple- output (MIMO) relay channels. We first consider the Gaussian MIMO relay channel with fixed channel conditions, and derive upper bounds and lower bounds that can be obtained numerically by convex programming. We present algorithms to compute the bounds. Next, we generalize the study to the Rayleigh fading case. We find an upper bound and a lower bound on the ergodic capacity. It is somewhat surprising that the upper bound can meet the lower bound under certain regularity conditions (not necessarily degradedness), and therefore the capacity can be characterized exactly; previously this has been proven only for the degraded Gaussian relay channel. We investigate sufficient conditions for achieving the ergodic capacity; and in particular, for the case where all nodes have the same number of antennas, the capacity can be achieved under certain signal-to-noise ratio (SNR) conditions. Numerical results are also provided to illustrate the bounds on the ergodic capacity of the MIMO relay channel over Rayleigh fading. Finally, we present a potential application of the MIMO relay channel for cooperative communications in ad hoc networks.

This paper studies the ergodic capacity and power allocation for a three-node relay channel over Rayleigh fading. Assuming perfect channel side information (CSI) at the transmitters and the receivers, we find the upper bounds and lower bounds on the capacity by solving generalized "water-filling" power allocation problems. Our results reveal that the optimum relay channel signaling can significantly outperform direct transmissions and traditional multi-hop transmissions. We also characterize the power gain of using the optimum signaling in the high SNR regime and find the corresponding power allocation policies.

We derive a new iterative multiuser detection algorithm based on a generalized sidelobe canceller which uses the constant modulus property of digitally modulated signals together with code knowledge given to a receiver in a CDMA system. The proposed algorithm can be seen as an extension of the least squares constant modulus algorithm in a sense that it simultaneously minimizes the constant modulus (CM) and the least square (LS) cost functions iteratively. The proposed detector performs as well as the ideal MMSE detector in the high SNR region, in addition to resolving the interference capture problem existing in most constant modulus algorithms (CMA).

We consider practical two-way communication schemes with blind channel estimation in TDD (time-division duplex) MISO (multi-input single-output) and MIMO (multi-input multi-output) systems. In the MISO system, we show that blind channel estimation only needs to be done at a transceiver equipped with multiple antennas and this estimated channel can be efficiently utilized for both uplink and downlink communication. In the MIMO system, we propose a two-stage communication scheme which begins with uplink transmission using differential space-time block codes and follows with two-way beamforming with blind channel estimation. We demonstrate, through simulations, that the novel schemes possess very good performance and compare them with training-based schemes.

In this paper, the achievable rate for receiver cooperation in ad-hoc networks is presented. We consider two kinds of cooperation. The signal cooperation requires complete synchronization in the network, including phase locked microwave oscillators at separated nodes. In code cooperation the nodes only cooperate on the encoding, but there is no assumption on node synchronization.

This paper presents the study of transmit power adaption and capacity-approaching coding/decoding in multiple-input-multiple-output (MIMO) Rayleigh fading channels under the assumption that perfect channel state information (CSI) is known at both the transmitter and the receiver. We propose three different simple, but powerful, methods for transforming the MIMO fading channel into a set of additive white noise Gaussian (AWGN) channels. We show that the channel capacity can be closely approached by using only a small number of different codes designed for the Gaussian channel.

The desire to reduce complexity at the mobile unit in cellular systems has led to the investigation of signal processing techniques that move computational complexity from the mobile unit to the base station. In this paper we investigate transmitter precoding for downlink time diversion duplex (TDD) code-division multiple-access (CDMA) communications. In particular, we develop a linear MMSE-based precoding strategy using blind channel estimation for fading multipath channels that allows for simple matched filtering at the mobile unit and is easy to make adaptive. We also present a performance analysis using tools developed for the analysis of conventional (receiver-based) linear blind multiuser detection in unknown channels. We compare the analytical and simulation results to traditional receiver-based blind multiuser detection. It is seen that transmitter precoding offers a reasonable alternative for TDD-mode CDMA when minimizing computational complexity at the mobile unit is a priority.

In this paper, blind and group-blind detectors are proposed based on the constant modulus (CM) property of the transmitted signals. By adopting blind and group-blind CM detectors, signi£cant gains are attained compared to the original blind and group-blind detectors, with only a slight increase in complexity. The SINR of the blind and group-blind CM detectors approach the theoretical upper bound for SINR (Cramer-Rao bound). Furthermore, the proposed detectors perform almost as well as the exact MMSE detector at high SNR.

In blind (or group-blind) linear multiuser detection, the detector is estimated from the received signals, with the prior knowledge of only the signature waveform of the desired user (or the signature waveforms of some but not all users). The performance of a number of such estimated linear detectors, including the direct-matrix-inversion (DMI) blind linear minimum mean square error (MMSE) detector, the subspace blind linear MMSE detector, and the form-I and form-II group-blind linear hybrid detectors, are analyzed. Asymptotic limit theorems for each of the estimates of these detectors (when the signal sample size is large) are established, based on which approximate expressions for the average output signal-to-interference-plus-noise ratios (SINRs) and bit-error rates (BERs) are given. To gain insights on these analytical results, the performance of these detectors in an equicorrelated code-division multiple-acces (CDMA) system is compared. Examples are provided to demonstrate the excellent match between the theory developed here and the simulation results.

Commercial applications for the location of subscribers of wireless services continue to expand. Consequently, finding the Cramer-Rao bound (CRB), which serves as an optimality criterion for the location estimation problem, is of interest. We derive the CRB for the estimation of channel parameters and mobile position in an asynchronous direct sequence code division multiple access (DS-CDMA) system operating over fading channels. It is assumed that the location estimates are obtained from the bearings and/or propagation delays estimated at one or more cluster(s) of antenna arrays of arbitrary geometry. Among other applications, the CRB on the positioning accuracy may serve as a design tool to find an optimum antenna placement, or to evaluate the practicability of a legal demand for emergency location.

In blind multiuser detection for CDMA systems, the receiver knows only the code of the user of interest, while in group-blind multiuser detection the receiver knows a subset of codes, e.g., the in-cell users in a base station. This paper derives bounds for the performance of linear blind and group-blind multiuser detectors. The bounds are derived under a number of different system assumptions. The bounds show the theoretical gain by using group-blind detectors over blind detectors, and they also show that previously developed blind and group-blind detectors are relatively close to optimum among estimators using only second order moments. However, the bounds also show that a considerable gap exists to optimum detectors that are not restricted to second order moments.

A nonlinear group-blind technique is developed for joint detection of some given users' data in a CDMA uplink environment with the presence of unknown interference. This method performs the so-called "slowest-descent search" over a likelihood function of the desired users, starting from the estimate closest to the unconstrained maximizer of the likelihood function, and along mutually orthogonal directions where this likelihood function drops to the slowest. Simulation results show that this new nonlinear technique offers substantial performance improvement over the previously proposed linear group-blind multiuser detectors with little attendant increase in computational complexity. The problem of group-blind multiuser detection in the presence of both unknown interference and impulsive ambient noise is also treated under the framework of slowest-descent search, with the aid of a novel subspace-based robust interference cancellation scheme. It is seen that this robust group-blind method significantly outperforms the robust blind multiuser detection scheme proposed previously.

This paper considers the problem of multiuser detection for CDMA systems where the codes of a group of users are known while others are unknown, called group-blind multiuser detectors. An example is the uplink in cellular communication system with interference from both in-cell users, with known codes, and out-of-cell users, with unknown codes. In previous papers linear detectors for this situation were developed. In the current paper, we combine these methods with low-complexity non-linear post processing, using the method of slowest descent, a general method for non-linear optimization. The performance of this new group-blind detector is compared to that of the non-blind MMSE detector as well as the linear group blind detector, and the non-linear detector is seen to have a considerably better performance, in particular in heavily loaded systems.

The effect of sampling and quantization on frequency estimation for a single sinusoid is investigated. Asymptotic Cramer-Rao bounds (CRB) for 1-bit quantization and for non-ideal filters are derived, which are simpler to calculate than the exact CRB while still relatively accurate. It is further investigated how many bits should be used in quantization to avoid the problems of 1-bit quantization, and it turns out that 3-4 bits are enough. Finally, oversampled 1-bit quantization is investigated. It is determined how much the signal should be oversampled, and in addition /spl Sigma//spl Delta/ modulators are investigated.

We consider the problem of multi-user detection for CDMA systems where the codes of some users are known while others are unknown, called semi-blind detectors. An example is at the base station of a cellular communication system with interference from both in-cell users, with known codes, and out-of-cell users, with unknown codes. We develop a hybrid semi-blind detector, which is partly decorrelating, partly MMSE. Subspace tracking methods are developed for on-line implementation of the detector. The performance of the detectors is compared to that of the purely blind MMSE detector and the non-blind MMSE detector, and the semi-blind detector is seen to have a considerable better performance.

The problem of adaptive suppression of both multiple-access interference (MAI) and intersymbol interference (ISI) in a dispersive CDMA channel is considered. A subspace-based group-blind technique for this purpose is developed, which blindly suppresses the interfering signals from other cells, while jointly detects the signals within the cell of interest. This technique is extended to the situation where the channel ambient noise is correlated. In this case, two antennas are needed at the receiver for channel estimation and signal separation.

Previously developed blind techniques for multiuser detection in code division multiple access (CDMA) systems lead to several near-far resistant adaptive receivers for demodulating a given user's data with the prior knowledge of only the spreading sequence of that user. In the CDMA uplink, however, typically the base station receiver has the knowledge of the spreading sequences of all the users within the cell, but not that of the users from other cells. In this paper, group-blind techniques are developed for multiuser detection in such scenarios. These new techniques make use of the spreading sequences and the estimated multipath channels of all known users to suppress the intracell interference, while blindly suppressing the intercell interference. Several forms of group-blind linear detectors are developed based on different criteria. Moreover, group-blind multiuser detection in the presence of correlated noise is also considered. In this case, two receiving antennas are needed for channel estimation and signal separation. Simulation results demonstrate that the proposed group-blind linear multiuser detection techniques offer substantial performance gains over the blind linear multiuser detection methods in a CDMA uplink environment.

This paper analyzes combinations of the linear minimum mean square error (MMSE) detector and a nonlinear parallel interference canceller (PIC) for multiuser code-division multiple-access (CDMA) detection. Both the case where all users' codes are known at the receiver and the case where only some codes are known are considered. An upper bound and an approximate formula for the error probability are derived and verified through simulation. It is shown that the combined MMSE/PIC detector can have a considerable performance gain over the MMSE. It is also shown that while the MMSE detector is sensitive to large code cross-correlation values, the combined MMSE/PIC detector is robust to code cross correlations. Finally, use of the MMSE/PIC detector for cellular systems with both inter- and intra-cell interference is considered.

The effect of sampling and quantization on frequency estimation for a single sinusoid is investigated. The Cramer-Rao bound for 1 bit quantization is derived, and compared with the limit of infinite quantization. It is found that 1 bit quantization gives a slightly worse performance, however, with a dramatic increase of variance at certain frequencies. This can be avoided by using 4 times oversampling. The effect of sampling when using non-ideal antialiasing lowpass filters is therefore investigated. Cramer-Rao lower bounds are derived, and the optimal filters and sampling frequencies are found. Finally, fast estimators for 1 bit sampling, in particular correlation based estimators, are derived. The paper concludes with simulation results for 4 times oversampled 1 bit quantization.

This paper analyses combinations of the linear MMSE detector and a non-linear parallel interference canceller (PIC) for multi-user CDMA detection. Approximate formulas and upper and lower bounds for the error probability are derived and verified through simulation. It is shown that the combined MMSE/PIC detector almost always is better than the MMSE, and a partial MMSE/PIC is derived that has superior performance in all cases. It is also shown that although the MMSE detector is near-far resistant, it is sensitive to the code cross-correlation matrix, while the combined MMSE/PIC detector is both near-far resistant and insensitive to the cross-correlation matrix.

We consider the problem of multi-user detection for CDMA systems where the codes of some users are known while others are unknown, semi-blind multi-user detection. An example is at the base station of a cellular communication systems with interference from both in-cell users, with known codes, and out-of-cell users, with unknown codes. We develop a number of decorrelating, semi-blind multi-user detectors, using a subspace approach. One of the detectors has a computationally efficient structure of a subspace estimation common to all known users, followed by a simple processing specific to each user. The performance of the detectors is compared to that of the purely blind decorrelating detector and the non-blind decorrelating detector, and the semi-blind detectors are seen to have the best performance.

A mathematical treatment of particle size and velocity estimation from two channel laser anemometry measurements is considered. Cramer-Rao bounds for the general case are derived, and the corresponding maximum likelihood estimator is analyzed through computer simulations. Low complexity correlation based estimators are derived and their performance is characterized. The results predicted by theory are illustrated by some numerical examples.