The growing demand for deployment of Artificial Intelligence (AI) on resource-constrained edge devices has motivated extensive research on the design of efficient edge-compatible AI hardware accelerators. One of the most promising solutions are the self-adaptive AI accelerators, capable of optimizing in real time their performance and energy consumption according to application requirements. This work introduces the EU-funded project Twinning for Excellence in Adaptive Edge Artificial Intelligence (AIDA4Edge), aimed to advance the state-of-the-art in the design of adaptive neural network accelerators for edge applications. The main goal is to develop a novel hybrid self-adaptive neural network architecture combining spiking and artificial neural networks, and supporting runtime adaptation of network functionality, precision and reliability. Furthermore, we aim to enhance the neural network training by incorporating hardware and quantization constraints in an automated tuning engine.

We present an investigation into the design of an acoustic communication network, where multiple users are distributed across space and transmit and receive simultaneously in the same band to and from a common base station. Specifically, we focus on a system that utilizes orthogonal frequency division multiplexing as a modulation method, and allows users to transmit and receive in either synchronous or asynchronous fashion. To distinguish between users on the uplink, the base station employs a combination of code-division and space-division multiple access. The base station iteratively steers a beam to each stable propagation path of the desired user’s channel while placing nulls in the direction of other paths, as well as in the directions of interfering users. Finally, the multiple paths of the desired user are recombined before data detection. The process is repeated for each user. Broadband beamforming is employed to account for the broadband nature of acoustic signals. The beamformer coefficients on each carrier depend on the angles of signal arrivals, which are estimated during the uplink transmission and used to construct both the uplink and the downlink beamformer. On the downlink, the base station utilizes angle information to assemble beamforming weights and point in the direction of stable paths of the users. It superimposes multiuser signals and transmits the sum signal to the users. The signal intended for a given user reaches only that user, requiring just a simple detector. Each user is equipped by a single-element transducer. To demonstrate the design concepts, we conducted simulations using a shallow water channel model and performed experimental over-the-air tests in an indoor environment using an acoustic communications testbed. The results were excellent, thereby encouraging future implementations in practical systems.

Multiple-Input Multiple-Output (MIMO) systems have been presented for Terahertz (THz) communications to combat high path loss and enable Terabit-per-second (Tbps) links. The lack of experimental MIMO channel measurements, however, has restricted the majority of THz MIMO work to the theoretical realm. This paper presents a novel, first-of-its-kind correlation-based MIMO channel sounder with a timing reference for sub-THz communications, which enables broadband, long-range, and time-varying channel characterization of MIMO systems. Preliminary results from a conference room setting show that 28.5% of the tested scenarios can support spatial multiplexing, and even with beamforming, the capacity outperforms the SISO cases substantially.

We address a scenario in which networked sensor nodes measure the strength of the field generated by a number of moving objects and transmit their measurements to a fusion center (FC) in a random access manner for the final reconstruction of the objects' trajectories. To ensure scalability over an arbitrary coverage area, we divide the total observation area into design units (DUs), each consisting of several sensing cells. An extended Kalman filter is assigned to each cell, while neighboring cells communicate with each other to exchange current status, thus allowing state fusion whereby the Kalman filters adjust (overwrite) their local estimates at the end of each updating interval. In this manner, provisions are made for the objects leaving and entering a DU, yielding a system design that is scalable across a large coverage area without an increase in complexity (size) of the Kalman filters. We provide a step-by-step procedure for designing the system, taking into account the fact that sensors communicate to the FCs using random access over band-limited and imperfect channels where packet loss is inevitable due to collisions as well as communication noise. In addition, we study different rate control schemes in which the FC instructs the sensors to increase or decrease their sensing (transmission) rate in accordance with the currently estimated object locations. Performance is evaluated through simulation, showing the effectiveness of the approach proposed in terms of the mean squared localization error and data throughput, and quantifying the effect of limited bandwidth and lossy communication.

We propose computationally efficient carrier frequency offset estimators for multicarrier underwater acoustic communication using identical pilot tones equi-spaced in the frequency domain. The first estimator uses the phase of the maximal eigenvector of a channel-dependent correlation matrix. Next, the phase of the minimal eigenvector of a channel-independent correlation matrix is combined with the first estimation using a weighted linear least squares principle. The third estimator solves a generalized eigenvalue decomposition problem by jointly considering the two correlation matrices, and then performs a similar second step as the previous estimator. Simulations and pool trials show that the proposed estimators achieve similar performance as common estimation techniques while surpassing them in severe environments.

The recent surge of implantable and wearable medical devices have paved the way for realizing intra-body networks (IBNs). Traditional RF-based techniques fall short in wirelessly connecting such devices owing to absorption within body tissues. A different approach is known as galvanic coupling, which employs weak electrical current within naturally conducting tissues to enable intra-body communication. This work is focused on channel characterization of the human body tissues considering the propagation of such electrical signals through it that carry data. Experiments were conducted using porcine tissue (in lieu of actual human tissue) with skin, fat and muscle layers in the frequency range of 100 kHz to 1 MHz. By utilizing single-carrier BPSK modulated Pseudorandom Noise Sequences, a correlative channel sounding system was implemented, leading to the following contributions: (1) measurements of the channel impulse and frequency response, (2) a noise analysis and capacity estimation, and (3) the comparison of results with existing models.

Air-to-Ground (A-G) communication networks play an important role providing wireless access to areas that lack cellular or satellite infrastructure. The use of Multiple-Input Multiple-Output (MIMO) technology has been considered to increase the data rate and range of these systems, and over-the-air experiments have shown promising results, however, they lack a detailed characterization of the A-G channel. In this paper an extension to the Geometric Air-to- Ground Ellipsoidal (GAGE) radio channel model is proposed to predict the performance of MIMO for low- altitude A-G systems. The geometry and propagation loss characteristics of the model are defined, and example results for a hilly rural environment are shown for 2x2 Spatial Multiplexing (SMX) and Space-Time Block Coding (STBC). The results predict that an uncoded multi-carrier system can use STBC for gains up to 16 dB or use SMX to increase the throughput by a factor of two.

This article considers the joint problem of packet scheduling and self-localization in an underwater acoustic sensor network with randomly distributed nodes. In terms of packet scheduling, our goal is to minimize the localization time, and to do so we consider two packet transmission schemes, namely a collision-free scheme (CFS), and a collision-tolerant scheme (CTS). The required localization time is formulated for these schemes, and through analytical results and numerical examples their performances are shown to be dependent on the circumstances. When the packet duration is short (as is the case for a localization packet), the operating area is large (above 3 km in at least one dimension), and the average probability of packet-loss is not close to zero, the collision-tolerant scheme is found to require a shorter localization time. At the same time, its implementation complexity is lower than that of the collision-free scheme, because in CTS, the anchors work independently. CTS consumes slightly more energy to make up for packet collisions, but it is shown to provide a better localization accuracy. An iterative Gauss-Newton algorithm is employed by each sensor node for self-localization, and the Cramér Rao lower bound is evaluated as a benchmark.

We investigate the use of differential space frequency block codes (SFBCs) with orthogonal frequency division multiplexing (OFDM) over underwater acoustic channels. While SFBC efficiently exploits spatial transmit diversity, differentially coherent detection eliminates the need for extensive signal processing required for channel tracking. System performance is demonstrated using real data transmitted in the 12-26 kHz acoustic band from a vehicle moving at 0.5-2 m/s and received over a 100 m shallow water channel, using 4-QAM and a varying number of carriers ranging from 128 to 2048. Performance results demonstrate the advantage of the differentially coherent SFBC detection over the conventional, coherent SFBC detection which suffers from imperfect channel estimation.

Underwater Acoustic Sensor Networks find use in critical time-sensitive applications such as disaster prevention and coastline protection. The sensor nodes used in such networks are powered by batteries that are difficult to recharge or replace. Hence it is imperative that routing algorithms used in such networks be very energy efficient while also satisfying necessary delay constraints for time-sensitive applications. Unlike the radio frequency medium, underwater acoustic channels have low bandwidth, large propagation delays and long multipath delay spreads. While energy-efficient routing is an actively researched area for terrestrial radio frequency networks, results from those studies generally do not apply to underwater acoustic networks due to vast differences in channel characteristics. In this paper we explore delay-constrained energy optimization for routing in underwater acoustic sensor networks. Specifically, we propose an offline Mixed Integer Linear Programming based routing algorithm that enables computation of delay constrained energy efficient routes.

Random Access Compressed Sensing (RACS) is an efficient method for data gathering from a network of distributed sensors with limited resources. RACS relies on integrating random sensing with the communication architecture, and achieves overall efficiency in terms of the energy per bit of information successfully delivered. To address realistic deployment conditions, we consider data gathering over a fading and noisy communication channel. We provide a framework for system design under various fading conditions, and quantify the bandwidth and energy requirements of RACS in fading. We show that for most practical values of the signal to noise ratio, energy utilization is higher in a fading channel than it is in a non-fading channel, while the minimum required bandwidth is lower. Finally, we demonstrate the savings in the overall energy and the bandwidth requirements of RACS compared to a conventional TDMA scheme. We show that considerable gains in energy -on the order of 10 dB- are achievable, as well as a reduction in the required bandwidth, e.g., 2.5-fold decrease in the bandwidth for a network of 4000 nodes.

ABSTRACT Motivated by the advances in acoustic modem technology and the growing number of applications that call for ad hoc deployable autonomous underwater systems (floating sensors, crawlers, vehicles), we address the problem of network initialization upon deployment. A neighbor discovery protocol is proposed, whose goal is to establish communication links over a large area, with a finite power budget that mandates multi‐hopping to provide full coverage. The protocol uses random access to eliminate the need for scheduling (i.e., enable system operation without a global clock reference) and power control to ensure that full connectivity is provided using shortest links (i.e., to conserve batteries and prolong the system's lifetime). Transmit power allocation takes into account the acoustic propagation loss, while additional large‐scale variation in the average received power is modeled via log‐normal fading which is confirmed by experimental observations. System performance is assessed through simulation, by measuring the energy consumption, time to completion, and reliability in the presence of fading. Fading is shown to have a degrading effect on the system reliability, and protocol adjustments are proposed to recover the performance under the constraint on maximum power. The key features of the protocol are simplicity of implementation and efficient use of power.Copyright © 2012 John Wiley & Sons, Ltd.

Random Access Compressed Sensing (RACS) is an efficient method for data telemetry from a network of distributed sensors deployed in a challenging field environment with limited resources. RACS relies on integrating sensing with the communication architecture, in order to achieve overall efficiency in terms of the energy per bit of information successfully delivered to the fusion center. Targeting realistic deployment conditions, we consider data gathering over a Ricean fading channel and in the presence of communication noise. We provide a framework for system design and study the energy and bandwidth requirements of the network. We then show that compared to a conventional TDMA network with ARQ, RACS achieves significant energy and bandwidth savings. For example, for a network of 4000 nodes, we observe 10 dB gain in the energy as well as a 2.5-fold reduction in the required bandwidth.

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.

In networks with large latency, feedback about received packets may lag considerably the transmission of the original packets, limiting the feedback's usefulness. Moreover, time duplex constraints may entail that receiving feedback may be costly. In this work, we consider tailoring feedback and coding jointly in such settings to reduce the expected delay for successful in order reception of packets. We find that, in certain applications, judicious choices provide results that are close to those that would be obtained with a full-duplex system. We study two cases of data transmission: one-to-all broadcast and all-to-all broadcast. We also analyze important practical considerations weighing the trade off between performance and complexity in applications that rely on random linear network coding. Finally, we study the problem of transmission of information under the large latency and time duplexing constraints in the presence of random packet arrivals. In particular, we analyze the problem of using a batch by batch approach and an online network coding approach with Poisson arrivals. We present numerical results to illustrate the performance under a variety of scenarios and show the benefits of the proposed schemes as compared to typical ARQ and scheduling schemes.

Inspired by the theory of compressed sensing and employing random channel access, we propose a distributed energy-efficient sensor network scheme denoted by Random Access Compressed Sensing (RACS). The proposed scheme is suitable for long-term deployment of large underwater networks, in which saving energy and bandwidth is of crucial importance. During each frame, a randomly chosen subset of nodes participate in the sensing process, then share the channel using random access. Due to the nature of random access, packets may collide at the fusion center. To account for the packet loss that occurs due to collisions, the network design employs the concept of sufficient sensing probability. With this probability, sufficiently many data packets - as required for field reconstruction based on compressed sensing - are to be received. The RACS scheme prolongs network life-time while employing a simple and distributed scheme which eliminates the need for scheduling.

We analyze the performance of underwater acoustic ad-hoc networks in the presence of interference. We assume a uniform distribution of nodes over a finite area, and focus for simplicity on a two-dimensional network. The node-to-node channel is modeled using frequency dependent path loss and Ricean fading. We adopt a communication theoretic approach and study the sustainable number of hops through the network as an indicator of the network connectivity, as well as power and bandwidth requirements. The network operation is highly dependent on the node density with two distinct regions of limited performance: the coverage-limited region, where the number of nodes in the network is small, and the interference-limited region, where the number of nodes is large. We show that a desired level of connectivity can be achieved through a judicious selection of the operating frequency, power and bandwidth. Numerical examples illustrate the results of the analysis. These results motivate us to propose a hierarchical underwater acoustic sensor network architecture in which the sensors and the collector stations operate in distinct layers. The hierarchical architecture is supported by the property of the acoustic underwater transmission medium that for each transmission distance there exists an operating frequency for which the narrow-band signal-to-noise ratio is maximized. The sensors and the collector stations are consequently allocated different operating frequencies. We assume a uniform distribution of both sensors and collector stations over the finite area of the sensing field, and model the channel accordingly. The analysis is performed under the assumption that there is interference from other nodes within the same layer of the hierarchy. Numerical examples illustrate the network performance and demonstrate that the preferred operating frequencies ensure network operation without any cross-interference between the collector network and the sensor network.

In this paper we propose new bounds on the achievable information rate for discrete-time Gaussian channels with intersymbol interference (ISI) and independent and uniformly distributed (i.u.d.) channel input symbols drawn from finite-order modulation alphabets. Specifically, we are interested in developing new bounds on the achievable rates for sparse channels with long memory. We obtain a lower bound which can be achieved by practical receivers, based on MMSE channel shortening and suboptimal symbol detection for a reduced-state channel. An upper bound is given in the form of a semi-analytical solution derived using basic information theoretic inequalities, by a grouping of the channel taps into several clusters resulting in a newly defined single-input multiple-output (SIMO) channel. We show that the so obtained time-dispersive SIMO channel can be represented by an equivalent single-input single-output (SISO) channel with a significantly shorter channel memory. The reduced computational complexity allows the use of the BCJR algorithm for the newly defined channel. The proposed bounds are illustrated through several sparse channel examples and i.u.d. input symbols, showing that the upper bound significantly outperforms existing bounds. Performance of our lower bound strongly depends on the channel structure, showing best results for minimum-phase and maximum-phase systems.

We study an online random linear network coding approach for time division duplexing (TDD) channels under Poisson arrivals. We model the system as a bulk-service queue with variable bulk size and with feedback, i.e., when a set of packets are serviced at a given time, they might be reintroduced to the queue to form part of the next service batch. We show that there is an optimal number of coded data packets that the sender should transmit back-to-back before stopping to wait for an acknowledgement from the receiver. This number depends on the latency, probability of packet erasure, degrees of freedom at the receiver, the size of the coding window, and the arrival rate of the Poisson process. Random network coding is performed across a moving window of packets that depends on the packets in the queue, design constraints on the window size, and the feedback sent from the receiver. We study the mean time between generating a packet at the source and it being "seen", but not necessarily decoded, at the receiver. We also analyze the mean time between a decoding event and the next, defined as the decoding of all the packets that have been previously "seen" and those packets involved in the current window of packets. Inherently, a decoding event implies an in-order decoding of a batch of data packets. We present numerical results illustrating the trade-off between mean delay and mean time between decoding events.

Fast and accurate Carrier Frequency Offset (CFO) estimation is a problem of significance in many multi-carrier modulation based systems, especially in uplink Orthogonal Frequency Division Multiple Access (OFDMA) where the presence of multiple users exacerbates the inter-carrier interference (ICI) and results in multi-user interference (MUI). In this paper, a new technique called partial FFT demodulation is proposed. Estimators for the CFO are derived by considering an approximated matched filter for each user, implemented efficiently using several FFTs operating on sub-intervals of an OFDM block. Through simulations, the feasibility and performance of the proposed estimators are demonstrated. Associated trade-offs are discussed.

We present a systematic network coding approach for time-division duplexing channels. In particular, we study the case of a node transmitting to a single receiver. We show that the use of systematic network coding using XORs can provide the same or close to the same performance in terms of completion time as a random linear network coding scheme that uses a large field size, with the added advantage of requiring fewer and simpler operations during the decoding process. We show that the average computation required to decode using systematic network coding in an erasure channel grows as O(M3Pe3), where M is the number of original packets being coded together, and Pe is the packet erasure probability. This means that systematic network coding requires Pe-3times fewer operations on average than random linear network coding with the same field size.

Capacity scaling laws are analyzed in an underwater acoustic network with n regularly located nodes. A narrow-band model is assumed where the carrier frequency is allowed to scale as a function of n. In the network, we characterize an attenuation parameter that depends on the frequency scaling as well as the transmission distance. A cut-set upper bound on the throughput scaling is then derived in extended networks. Our result indicates that the upper bound is inversely proportional to the attenuation parameter, thus resulting in a highly power-limited network. Furthermore, we describe an achievable scheme based on the simple nearest-neighbor multi-hop (MH) transmission. It is shown under extended networks that the MH scheme is order-optimal as the attenuation parameter scales exponentially with √n (or faster). Finally, these scaling results are extended to a random network realization.

We consider the performance of underwater acoustic ad-hoc networks. We assume a uniform distribution of the nodes over a finite area. The node-to-node channel is modeled through the Ricean fading model. We adopt a communication theoretic approach and study the interdependence of the sustainable number of hops through the network, end-to-end frame error probability, power and bandwidth allocation and operating frequency. We find that the network connectivity, given by the sustainable number of hops, exhibits a bimodal behavior. We present numerical examples that illustrate the results of the analysis.

We study the effect of the field size on the performance of random linear network coding for time division duplexing channels proposed in [1]. In particular, we study the case of a node broadcasting to several receivers. We show that the effect of the field size can be included in the transition probabilities of the Markov chain model of the system. Also, an improved upper bound on the mean number of coded packets required to decode M original data packets using random linear network coding is presented. This bound shows that even if the field size is 2, i.e. we perform XORs amongst randomly selected packets from the pool of M original ones, we will need on average at most M + 2 coded packets in order to decode. Thus, there will be only a very small degradation in performance if M is large. We present numerical results showing that the mean completion time of our scheme with a field size of 2 is close in performance to our scheme when we use larger field sizes. We also show that as M increases, the difference between using a field size of 2 and larger field sizes decreases. Finally, we show that we can get very close to the optimal performance with small field sizes, e.g. a field size of 4 or 8, even when M is not very large.

We study the energy performance of random linear network coding for time division duplexing channels. We assume a packet erasure channel with nodes that cannot transmit and receive information simultaneously. The sender transmits coded data packets back-to-back before stopping to wait for the receiver to acknowledge the number of degrees of freedom, if any, that are required to decode correctly the information. Our analysis shows that, in terms of mean energy consumed, there is an optimal number of coded data packets to send before stopping to listen. This number depends on the energy needed to transmit each coded packet and the acknowledgment (ACK), probabilities of packet and ACK erasure, and the number of degrees of freedom that the receiver requires to decode the data. We show that its energy performance is superior to that of a full-duplex system. We also study the performance of our scheme when the number of coded packets is chosen to minimize the mean time to complete transmission as in. Energy performance under this optimization criterion is found to be close to optimal, thus providing a good trade-off between energy and time required to complete transmissions.

A new random linear network coding scheme for reliable communications for time division duplexing channels is proposed. The setup assumes a packet erasure channel and that nodes cannot transmit and receive information simultaneously. The sender transmits coded data packets back-to-back before stopping to wait for the receiver to acknowledge (ACK) the number of degrees of freedom, if any, that are required to decode correctly the information. We provide an analysis of this problem to show that there is an optimal number of coded data packets, in terms of mean completion time, to be sent before stopping to listen. This number depends on the latency, probabilities of packet erasure and ACK erasure, and the number of degrees of freedom that the receiver requires to decode the data. This scheme is optimal in terms of the mean time to complete the transmission of a fixed number of data packets. We show that its performance is very close to that of a full duplex system, while transmitting a different number of coded packets can cause large degradation in performance, especially if latency is high. Also, we study the throughput performance of our scheme and compare it to existing half-duplex go-back-N and selective repeat ARQ schemes. Numerical results, obtained for different latencies, show that our scheme has similar performance to the selective repeat in most cases and considerable performance gain when latency and packet error probability is high.

We study the performance of random linear network coding for time division duplexing channels with Poisson arrivals. We model the system as a bulk-service queue with variable bulk size. A full characterization for random linear network coding is provided for time division duplexing channels by means of the moment generating function. We present numerical results for the mean number of packets in the queue and consider the effect of the range of allowable bulk sizes. We show that there exists an optimal choice of this range that minimizes the mean number of data packets in the queue.

We propose a linear network coding scheme to disseminate a finite number of data packets in arbitrary networks. The setup assumes a packet erasure channel, slotted time, and that nodes cannot transmit and receive information simultaneously. The dissemination process is completed when all terminals can decode the original data packets. We also assume a perfect knowledge of the information at each of the nodes, but not necessarily a perfect knowledge of the channel. A centralized controller decides which nodes should transmit, to what set of receiver nodes, and what information should be broadcasted. We show that the problem can be thought of as a scheduling problem, which is hard to solve. Thus, we consider the use of a greedy algorithm that only takes into account the current state of the system to make a decision. The proposed algorithm tries to maximize the impact on the network at each slot, i.e. maximize the number of nodes that will benefit from the coded packet sent by each active transmitter. We show that our scheme is considerably better, in terms of the number of slots to complete transmission, than schemes that choose the node with more information as the transmitter at every time slot.

A phase synchronization method, which provides non-uniform frequency offset compensation needed for wideband OFDM, is coupled with low-complexity channel estimation in the time domain. Sparsing of the channel impulse response leads to an improved performance, while adaptive synchronization supports decision-directed operation and yields low overhead. System performance is demonstrated using experimental data transmitted over a 1 km shallow water channel in the 19 kHz - 31 kHz band.

The 13 papers in this special issue focus on underwater wireless communication networks.

The goal of this paper is two-fold. First, to establish a tractable model for the underwater acoustic channel useful for network optimization in terms of convexity. Second, to propose a network coding based lower bound for transmission power in underwater acoustic networks, and compare this bound to the performance of several network layer schemes. The underwater acoustic channel is characterized by a path loss that depends strongly on transmission distance and signal frequency. The exact relationship among power, transmission band, distance and capacity for the Gaussian noise scenario is a complicated one. We provide a closed-form approximate model for 1) transmission power and 2) optimal frequency band to use, as functions of distance and capacity. The model is obtained through numerical evaluation of analytical results that take into account physical models of acoustic propagation loss and ambient noise. Network coding is applied to determine a lower bound to transmission power for a multicast scenario, for a variety of multicast data rates and transmission distances of interest for practical systems, exploiting physical properties of the underwater acoustic channel. The results quantify the performance gap in transmission power between a variety of routing and network coding schemes and the network coding based lower bound. We illustrate results numerically for different network scenarios.

Recent information-theoretic work has shown the potential capacity increase of multiple-input multiple-output (MIMO) systems compared to single-input single-output (SISO) systems. The singular value decomposition (SVD) has proven very useful in MIMO communication systems but its performance depends on the accuracy of the channel state information (CSI) at both sides of the link. This paper presents a study on the influence of channel estimation errors and noise on the probability of error of the system when no error correction mechanisms are provided.

Multicarrier code-division multiple access (MC-CDMA) combines multicarrier transmission with direct sequence spread spectrum. Different approaches have been adopted which do not assume a perfectly known channel. We examine the forward-link performance of decision-directed adaptive detection schemes, with and without explicit channel estimation, for MC-CDMA systems operating in fast fading channels. We analyze theoretically the impact of channel estimation errors by first considering a simpler system employing a threshold orthogonality restoring combining (TORC) detector with a Kalman channel estimator. We show that the performance deteriorates significantly as the channel fading rate increases and that the fading rate affects the selection of system parameters. We examine the performance of more realistic schemes based on the minimum mean square error (MMSE) criterion using least mean square (LMS) and recursive least square (RLS) adaptation. We present a discussion which compares the decision-directed and pilot-aided approaches and explores the tradeoffs between channel estimation overhead and performance. We find that there is a fading rate range where each method provides a good tradeoff between performance and overhead. We conclude that the MMSE per carrier decision-directed detector with RLS estimation combines good performance in low to moderate fading rates, robustness in parameter variations, and relatively low complexity and overhead. For higher fading rates, however, only pilot-symbol-aided detectors are appropriate.

Adaptive power and rate control for efficient use of satellite channels in the Ka band are considered. Fading due to rain is modeled as a log-normally distributed, autoregressive process with known statistics. Adaptation policies based on MMSE channel estimation are designed taking into account the effect of channel estimation errors in an optimal manner so as to satisfy the required outage probability for given fading dynamics. Analytical results show average power savings or average bit rate increase that are available from adaptive techniques. It is demonstrated that a large improvement over a non-adaptive system is available provided an appropriately designed adaptation scheme that makes use of the channel fading statistics. Finally, practical methods based on adaptive modulation level selection in a fixed-bandwidth system are evaluated analytically in terms of average bandwidth efficiency.

A formula that computes the maximum number of users supported per base station in a cellular radio network is generalized to consider the frequency reuse number and arbitrary processing gains. The generalization quantifies a cost associated with in-cell interference by accounting for the lack of interference from the desired user on the total interference and by considering the impact of the frequency reuse number on the out-of-cell interference. This interference cost results in an increase in the received Eb/Io relative to FDMA which should be weighted against a reduction in the Eb/Io requirement resulting from using CDMA.

Error probability of an adaptive multiuser diversity receiver is evaluated in terms of channel fading rate and the number of code-division multiple access users. Fading-induced performance loss, which leads to the error probability floor, is established for the proposed coherent combining scheme and compared to that of the differentially coherent receiver with equal-gain combining.

An adaptive multipath decorrelating multiuser receiver is considered for application in Rayleigh fading multipath channels with significant Doppler spread. Coherent diversity combining is performed using adaptively obtained channel estimates in a manner that minimizes the impact of estimation errors on data detection. The bit-error rate of the receiver is evaluated analytically, showing dependence on the fading rate of the channel and tracking capabilities of adaptive least mean square and recursive least square algorithms, in addition to the order of multipath diversity and the number of active code-division multiple-access users.

Multi-carrier CDMA (MC-CDMA) is a hybrid multiple access scheme combining multi-carrier modulation (MCM or OFDM) with direct sequence spread spectrum (DS-SS). Although a common assumption in the analysis of the performance of the various MC-CDMA detectors is that they have perfect knowledge of the fading channel, this assumption is no longer valid as the fading rate (Doppler spread) of the channel increases. We examine the performance of various forms of the minimum mean square error (MMSE) detector operating in a fast fading Rayleigh channel described by a Gauss-Markov state-space model. We show that, although adaptive detection based on the LMS and RLS algorithms is possible without explicit channel estimation, a low complexity detector, termed MMSE per carrier, combined with an RLS channel estimator can achieve better performance as well as increased robustness to the selection of the adaptation parameters.

The performance of coherent acoustic communication systems involving moving platforms (e.g., underwater vehicles and ships) is adversely effected by Doppler shift resulting from relative motion of the transmitter and receiver. This paper presents a series of innovations which, together, dramatically improve the response to Doppler shift of a widely-used adaptive receiver algorithm. The innovations include a frequency-shift estimator, time-scale interpolator and robust phase-locked loop (PLL). These techniques reduce the computational load of the coherent equalizer and provide accurate Doppler tracking. Results from at-sea testing are presented to illustrate the performance of the combined algorithm.

A coherent receiver with a decision-feedback equalizer (DFE) operating on a Rayleigh fading channel under a suitable adaptive algorithm is considered. In the analysis of a DFE, a common assumption is that the receiver has perfect knowledge of the channel impulse response. However, this is not the case in practice, and for a rapidly fading channel, errors in channel tracking can become significant. We analyze theoretically the impact of these errors on the performance of a multichannel DFE. The expressions obtained for the achievable average MPSK bit error probabilities depend on the estimation error covariance. In order to specify this matrix, we focus on a special case when a Kalman filter is used as an optimal channel estimator. In this case, the probability of bit error can be assessed directly in terms of channel fading model parameters, the most interesting of which is the fading rate. Our results show the penalty imposed by imperfect channel estimation, as well as the fading-induced irreducible error rates.>