Support vector machines (SVMs) have been the learning model of choice in numerous classification applications. While SVMs are widely successful in real-world deployments, they remain susceptible to mislabeled examples in training datasets where the presence of few faults can severely affect decision boundaries, thereby affecting the model's performance on unseen data. In this brief, we develop and describe in implementation detail a novel method based on $L_{1}$ -norm principal-component data analysis and geometry that aims to filter out atypical data instances on a class-by-class basis before the training phase of SVMs and thus provide the classifier with robust support-vector candidates for making classification boundaries. The proposed dataset curation method is entirely data-driven (touch-free), unsupervised, and computationally efficient. Extensive experimental studies on real datasets included in this brief illustrate the $L_{1}$ -norm curation method and demonstrate its efficacy in protecting SVM models from data faults during learning.

In this work, we present a new federation framework for Union-Labs, an innovative cloud-based resource-sharing infrastructure designed for next-generation (NextG) and Internet of Things (IoT) over-the-air (OTA) experiments. The framework aims to reduce the federation complexity for testbeds developers by automating tedious backend operations, thereby providing scalable federation and remote access to various wireless testbeds. We first describe the key components of the new federation framework, including the Systems Manager Integration Engine (SMIE), the Automated Script Generator (ASG), and the Database Context Manager (DCM). We then prototype and deploy the new Federation Plane on the Amazon Web Services (AWS) public cloud, demonstrating its effectiveness by federating two wireless testbeds: i) UB NeXT, a 5G-and-beyond (5G+) testbed at the University at Buffalo, and ii) UT IoT, an IoT testbed at the University of Utah1.

We consider the problem of dynamically optimizing a multiple-input multiple-output (MIMO) wireless waveform in a given potentially heavily utilized fixed frequency band with applications in near-field or far-field autonomous machine-to-machine communications. In particular, we find the transmitter beam weight vector and the pulse code sequence that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR joint space-time receiver filter. We propose and derive two novel model-based solutions: (a) Disjoint, space first (transmit weight vector) then time (pulse code sequence) waveform optimization and (b) jointly optimal transmit weight vector and pulse code sequence optimization (a mixed integer programming problem.) The proposed formally derived algorithmic solutions are studied in extensive simulations under varying waveform code length, near-field/far-field and spread-spectrum/ non-spread-spectrum interference, in light and dense interference scenarios. Our findings highlight the effectiveness of the described methods compared to static conventionally designed MIMO links and the remarkable ability of the joint space-time optimized waveforms to avoid heavy interference.

In this article we explore new techniques that can enable open remote experimentation for mobile networks. We first propose a cloud-based framework called CloudRAFT, based on which experimenters are allowed to remotely access and control experimental resources via public cloud AWS and share the resulting data and code via the cloud. Then, we discuss the enabling techniques for CloudRAFT, including Amazon serverless service, VNC-based remote command line, and Websocket-based real time communications, among others. Finally, we showcase the application of these techniques in enabling remote access to UB NeXT, a software-defined testbed that has been developed at University at Buffalo for wireless mobile network modeling, optimization and deployment. This work verifies the feasibility of accessing, controlling and sharing wireless testbeds through a remote public cloud.

Data-driven control based on AI/ML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces in the real world because of high time and labor cost, weather limitations as well as safety concerns. In this work we attempt to tackle this challenge by designing a multi-fidelity simulator taking wireless Unmanned Aerial Vehicle (UAV) networks into consideration. We design the simulator by interfacing two Unmanned Aerial System (UAS) simulators we have developed in prior years: UBSim and UB-ANC. The former focuses on UAV network optimization and policy training by considering explicitly the network environments such as blockage dynamics, while the latter focuses more on high-fidelity UAV flight control. We first develop a coordination interface referred to as SimSocket for signaling exchanges between UBSim and UB-ANC in simulations, and then showcase coordinated simulations based on UBSim and UB-ANC. The new research that can be enabled by the integrated simulator is also discussed for digital twin-based UAS systems.

We introduce RF-SITL, a radio frequency (RF) software-in-the-loop (SITL) channel emulator developed with GNU Radio and the University at Buffalo’s Airborne Networking and Communications (UB-ANC) emulator to enable integrated simulation of systems comprising multiple unmanned aerial vehicles (UAVs) interacting over a wireless communication channel. RF-SITL could be paired with any multi-robot simulator to enable I/Q sample-level fidelity simulation of communication interactions between the robots by accurately simulating channel effects, including interference, noise, distance-dependent path loss, and packet losses. RF-SITL works as follows: 1) it instantiates a virtual software-defined transceiver in GNU Radio for each UAV simulated in the UB-ANC Emulator; 2) it builds an interference channel model in which each network node receives the superposition of signals transmitted from other nodes; and 3) it synchronizes the location of each simulated UAV in the UB-ANC Emulator with the virtualized RF transceivers in RF-SITL, such that the communication channel between nodes can accurately model distance-dependent channel effects, such as path loss. With these capabilities, we can use both off-the-shelf and custom-built signal processing flowgraphs that simulate Gaussian Minimum Shift Keying (GMSK), 802.11-like Orthogonal Frequency Division Multiplexing (OFDM), and direct sequence spread-spectrum (DSSS) links in GNU Radio to simulate swarm UAV networks prior to their deployment in software-defined radios in a swarm UAV network.

In this paper, a link-layer synchronization and medium access control (MAC) protocol for very-high-speed wireless communication networks in the Terahertz (THz) band is presented. The protocol relies on a receiver-initiated handshake to guarantee synchronization between transmitter and receiver. Two scenarios are considered, namely, a macroscale scenario, where nodes utilize rotating directional antennas to periodically sweep the space while overcoming the distance problem at THz frequencies, and a nanoscale scenario, where nano-devices require energy harvesting systems to operate. Both scenarios are implemented on a centralized and an ad-hoc network architecture. A carrier-based physical layer is considered for the macro-scenario, whereas the physical layer for the nano-scenario is based on a femtosecond-long pulse-based modulation scheme with packet interleaving. The performance of the proposed MAC protocol is analytically investigated in terms of delay, throughput and probability of successful packet delivery, and compared to that of an adapted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) with and without handshake. The results are validated by means of extensive simulations with ns-3, in which all the necessary THz elements have been implemented. The results show that the proposed protocol can maximize the successful packet delivery probability without compromising the achievable throughput in THz-band communication networks.

Reservoir Computing (RC) is a class of machine learning approaches that is suitable for prediction tasks with low computational complexity. In this paper, an RC-based symbol detection for MIMO- OFDM systems is presented where RC is realized through the echo state network (ESN). Detailed energy-efficiency analysis is conducted to characterize the energy-efficiency of the introduced symbol detector. To be specific, the transmit power, the circuit power, and the computational power at both transmitter and receiver are jointly considered for the energy-efficiency analysis. The overall system energy-efficiency as well as the receiver energy-efficiency of the introduced RC-based symbol detector are compared with those of the popular linear minimum mean squared error (LMMSE)-based approach. Simulation and numerical results show that the RC-based symbol detector is a ``green'' solution compared to the traditional LMMSE-based method with lower energy consumption per information bit.

Mobile health (m-Health) is one potential application of cyber-physical systems, where biomedical sensors and mobile devices interact tightly together to transmit medical data to an m-Health server. In this paper, we consider a three-tier hierarchical m-Health system: 1) the sensor network tier capturing vital signals; 2) the mobile computing network tier processing and routing the sensed data to a fixed remote location; and 3) the back-end network tier processing and analyzing the sensed medical data along with patient's medical history. Based on this architecture, a physical layer security scheme for the second tier is developed and network performance of the introduced scheme is analyzed under different metrics using stochastic geometry. To be specific, secure transmission range and average end-to-end delay are analyzed for two different strategies: the mobile device transmits: 1) to the nearest neighbor and 2) to the furthest neighbor. Furthermore, we consider the cases of full knowledge on eavesdroppers' locations and when such information is unavailable. Results show that transmitting to the nearest neighbor achieves the highest secure transmission distance with the lowest mean delay when full information on eavesdroppers is available.

In this paper, an optimal power and time assignment strategy is developed for hybrid automatic-repeat-request (H-ARQ) communication protocol over quasi-static Rayleigh fading channels. For any given average total time duration and energy budget, we try to find the sequences of power values and time durations for H-ARQ retransmission rounds that minimize the outage probability of the H-ARQ protocol. We solve the joint optimization of power and time assignment for the H-ARQ protocol and derive a set of equations that describe the optimal transmission power and time duration sequence which enable a recursive calculation. Numerical results show that the performance of the proposed optimal-time and optimal-power assignment scheme is substantially better than that of the conventional equal-time and equal-power scheme as well as the equal-time and optimal-power scheme. For example, when the maximum transmission number is L=2 and the target data amount per packet is D_0=2000 bits, the optimal-time and optimal-power assignment scheme saves about 28dB in terms of the average energy consumption.

Energy efficiency is one of the major challenges of 5G networks. As data rates are expected to increase by 1000x from 4G to 5G, energy efficiency will need to improve by about the same amount. Recently, energy harvesting techniques have attracted lots of attention from the scientific community, due to their ability to increase network lifetime. More specific, energy harvesting from ambient radio frequency (RF) signals is of special importance, especially with the recent RF circuit advancements. In this paper, we consider a device-to-device (D2D) communication in underlay cellular networks, where D2D users reuse the spectrum occupied by cellular users. We introduce the concept of spatial RF energy harvesting, where D2D users harvest RF power from uplink cellular transmissions, if it exceeds a predesigned threshold, in a spatial region. Using tools from stochastic geometry, we obtain a closed form expression for the probability of activating RF power conversion circuit by making full use of spatial locations of ambient RF signals. Subsequently, we study the impact of RF energy harvesting region radius to harvest sufficient power on the signal-to-interference (SIR) ratio of D2D network. Simulation results provide insights for the required advancements to design highly efficient RF harvesting circuits.

With the explosive growth of mobile traffic demand, device-to-device (D2D) communication offers a promising approach to reduce cellular congestion by offloading users' traffic to D2D network. In this paper, we consider a relay- assisted D2D communication in underlay cellular networks, where D2D users access a proportion of the spectrum occupied by cellular users. This poses two fundamental issues. The first issue is related to protecting the cellular transmissions by reducing the spectrum partition factor, which measures the fraction of spectrum available to D2D users. The second issue is that as we limit the spectrum available to D2D users, less D2D transmissions can be supported, which in turn increases the distances between them. To address these issues, we allow a relay user to assist D2D communications in order to achieve higher D2D spectral efficiency without affecting cellular spectral efficiency. In fact, results show significant improvements in spectral efficiency not only to D2D, but to the whole network, especially in a dense D2D environment.

In this paper, we consider device-to-device (D2D) communication underlaying cellular networks, where D2D users harvest radio frequency (RF) power from uplink cellular transmissions. This paper addresses two important issues for energy harvesting-based D2D cellular networks. The first is how the energy harvested from cellular ambient RF signals affects the D2D spectral efficiency. Using tools from stochastic geometry, we investigate this issue by first characterizing the transmission probability of D2D transmitter as the probability of having enough battery power, and then obtaining analytic expressions of the spectral efficiency for D2D and cellular networks. The second issue is related to the cellular spectral efficiency: the more RF energy D2D users can harvest, the higher their transmission probability becomes, which leads to generating more interference in the cellular network. We carry out simulations to understand this impact of RF energy harvesting on the spectral efficiency of both D2D and cellular networks. Results show promising improvement to the whole network, in terms of the weighted spectral efficiency, when employing RF harvesting technology and when there are enough available channels in the network.

To suppress the co-channel interference in a multi-cell multi-user multiple-input-multiple-output downlink cellular network, a novel interference alignment transceiver beam-forming design along with a low complexity iterative coordinated beam-forming scheme is introduced. While the latter combats the intra-cell interference, the former is utilized to mitigate the inter-cell interference. The proposed schemes consider the codebook-based feedback, which is adopted in the LTE/LTE-advanced systems. Optimal downlink user-specific and cell-specific beam-forming matrices are characterized to maximize the lower bound of expected signal-to-leakage-plus-noise ratio and to minimize the residual inter-cell interference, respectively. Moreover, closed-form expressions for these beam-forming matrices under limited channel state information feedback and in the presence of the quantization error are identified. Simulations are conducted to investigate the performance of the proposed strategy. The results indicate that our scheme can significantly improve the average spectral-efficiency of the underlying network when compared with existing ones where the quantization error is neglected. Furthermore, for a fixed payload size of the codebook, unlike zero-forcing beam-forming in which the sum throughput is bounded as the signal-to-noise ratio (SNR) increases, in our scheme, the performance gap between rank 2 feedback and perfect feedback remains approximately constant as SNR increases.

Reservoir Computing (RC) is a recent neurologically inspired concept for processing time dependent data that lends itself particularly well to hardware implementation by using the device physics to conduct information processing. In this paper, we apply RC to channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. Due to the multipath propagation environment between a transmitter and receiver, the received signal undergoes attenuation, time delay and phase shift. For mitigating these random effects and decoding the transmitted signal at the receiver, accurate channel estimation is vital. Statistical approaches for channel estimation assume that accurate channel information is available at the receiver. However, the time-variance of the channel complicates the channel estimation process by making the current estimation outdated. Recurrent Neural Networks (RNNs), which are analogous to the functioning of the human brain, are therefore utilized for channel prediction. Training algorithms for RNNs are categorized as gradient-descent methods, which often results in high computational complexity and leads to non-convergence due to the presence of bifurcations. In this paper, an Echo State Network (ESN), which is a class of RC approach, has been used for training a RNN to estimate the channel state information. Using this approach, the training and hence, the implementation complexity is significantly reduced. Simulation results show significant improvement in channel estimation accuracy for the proposed method.

Differential amplify-and-forward (DAF) cooperative relaying appears to be an attractive strategy for wireless networks where channel estimation is not feasible or it is rather avoided. In this work, we intend to determine the optimum relay location in order to minimize the outage probability of the DAF cooperative relaying in case that the system is allowed to deploy the relay. We consider two scenarios. First, we optimize the relay location for the DAF cooperative relaying with any given power assignment at the source and the relay. Second, we further consider relay location optimization with optimum power assignment in which the performance of the DAF relaying is further enhanced with the price of higher complexity. In order to obtain the optimum relay location, we develop a recursive algorithm based on the fix-point theorem and show that the relay location has a unique optimum solution which can be determined recursively from the sequence developed in our theorem. We also study the impact of the path-loss exponent to the optimal relay location and found that the relay should be deployed closer to the source in lossy environment such as near-ground environment than that in the free space environment. Extensive numerical results are provided to validate and illustrate the theoretical development.

Ongoing research in graphene-based nano-transceivers and nano-antennas points to the Terahertz (THz) band (0.1-10 THz) as the communication frequency range for nano-devices. Femtosecond-long pulse-based modulation schemes have been proposed to enable ultra-broadband communication among nano-devices. One of the main challenges with ultra-high-speed pulse-based communications is the need for tight symbol synchronization between transmitter and receiver. In this paper, a synchronization scheme for pulse-based THz-band communications is designed and analyzed. The proposed scheme is aimed at iteratively estimating the symbol start time and reducing the observation window length for the symbol detector. The proposed scheme is fully analog and can be implemented with a combination of voltage-controlled delay (VCD) lines and Continuous-Time Moving-Average (CTMA) symbol detectors. Closed form expressions are obtained for the number of preamble symbols needed to achieve synchronization as well as the maximum number of bits that can be transmitted before requiring re-synchronization in the presence of clock skew. Similarly, the symbol error rate of the CTMA receiver is analytically modeled as a function of the resulting observation window length. Finally, the synchronization and symbol detection impact on the achievable throughput is studied. The developed scheme is experimentally tested with measured THz pulses and its performance is numerically investigated. The results show how the proposed scheme can successfully estimate the symbol start time and minimize the symbol error rate with less than ten synchronization preamble bits.

In this paper, a link-layer synchronization and medium access control (MAC) protocol for very-high-speed wireless communication networks in the THz band is presented. The protocol relies on a receiver-initiated handshake as a way to guarantee synchronization between transmitter and receiver. In addition, it incorporates a sliding window flow control mechanism, which combined with the one-way handshake, maximizes the channel utilization. Two different application scenarios are considered, namely, a macroscale scenario, in which nodes utilize turning directional antennas to periodically sweep the space while overcoming the distance problem at THz frequencies, and a nanoscale scenario, in which nano-nodes require energy harvesting systems to operate. A carrier-based physical layer is considered for the macro-scenario, whereas the physical layer for the nano-scenario is based on a femtosecond- long pulse-based modulation scheme with frame interleaving. The performance of the proposed MAC protocol is analytically investigated in terms of delay, throughput and successful packet transmission probability, and compared to that of an adapted Carrier Sense Multiple Access with Collision Avoidance with and without handshake. The results are validated by means of extensive simulations with ns-3, in which all the necessary THz elements have been implemented. The results show that the proposed protocol can maximize the successful packet delivery probability without compromising the achievable throughput in THz-band communication networks.

We present a real-time, priority-aware joint packet fragmentation and error protection scheme for transmitting H.264/AVC compressed video over Rayleigh fading channels. A priority is assigned to every video slice based on the predicted cumulative mean squared error (CMSE) contributed by its loss relative to all the slices of the group of pictures (GOP). Here, the CMSE of a slice, which is predicted by using our low-complexity generalized linear model, represents the degradation in video quality contributed by its loss. The slices of a frame belonging to the same priority are aggregated to form video packet(s). We simulate the fragment error rates (FERs) for a combination of different fragment sizes and rate compatible punctured convolutional (RCPC) code rates. These FERs are then used to determine the optimal fragment sizes and code rates for packets of each priority class by minimizing the expected normalized predicted CMSE of all the priority classes per GOP in H.264 video bitstream. We observed a significant improvement in the received video quality over the conventional and priority-agnostic packet fragmentation schemes.

We formulate the problem of point-to-point scheduling at a congested network node as a Markov decision process (MDP) that considers the deadlines and priorities of each packet as well as the dynamic packet arrivals and channel conditions. Within this framework, we formulate the problem with the objective of maximizing the node's long-run priority-weighted throughput subject to instantaneous transmission rate constraints. We then analyze the structural properties of the optimal scheduling policy with respect to the deadlines and priorities of the backlogged packets. Additionally, we compare our approach to existing heuristics such as Priority Queueing (PQ), Earliest Deadline First (EDF), and Weighted Fair Queueing (WFQ). Our MDP-based approach outperforms all three heuristics not only because it takes into account the packets' priorities and deadlines, but also because it takes into account the future channel and packet arrival dynamics. Lastly, we experimentally show that the optimal scheduling policy has a switch-over type structure in several key parameters including the relative priorities of different traffic classes, the discount factor, and the traffic load intensity.

In this work, we intend to carry out optimized spread-spectrum concealment of multiuser data under a given digital image. First, the overall image is pre-processed into transform-domain small blocks from which host vectors are obtained via zig-zag scanning vectorization. Multiuser data hiding is performed in the generated host vectors. Under this data hiding system model, we give an orthogonal set of embedding spread-spectrum signatures that achieves maximum sum signal-to-interference-plus-noise ratio at the output of the linear-filter receivers for any fixed embedding amplitude values. Then, for any given total embedding distortion constraint, we present the optimal multi-signature assignment and amplitude allocation that maximizes the sum capacity of the concealment procedure. The practical implication of the results is sum signal-to-interference-plus-noise ratio, sum-capacity optimal multiuser/multi-signature spread-spectrum data hiding in the digital image medium. Numerical results demonstrate the effectiveness of the proposed methods. Copyright © 2014 John Wiley & Sons, Ltd.

In underlay cognitive radio networks (CRNs), secondary users (SUs) have to dynamically control their transmit powers so that the interference to primary users (PUs) is tolerable. Under this constraint, SUs' data link usually suffers from either high error rate with limited transmission range or long end-to-end delay caused by multi-hop transmissions. To improve the performance of SUs, cooperative routing using mutual-information accumulation is introduced in CRNs for the first time in this paper. To be specific, the routing and resource allocation problem in underlay CRNs is investigated and is factored into two sub-problems, each of which can be solved efficiently. Furthermore, a distributed algorithm is introduced and simulation results show that both the centralized and distributed algorithms can reduce upto 77% of the end-to-end delay compared to the traditional multi-hop delay-optimal routing in CRNs. Finally, theoretical analysis on the end-to-end delay for a one-dimensional (1-D) network in the low signal-to-interference ratio (SIR) region is conducted. Both the analytical and simulation results show that mutual-information accumulation can significantly decrease the delay of underlay CRNs, especially in the scenario where PUs have a tight interference power constraint.

To improve the spectral efficiency of a cognitive radio system, sensing based spectrum sharing (SBSS) technique combines the advantages of spectrum overlay and spectrum underlay. In this paper, we study the performance of SBSS under primary users' (PUs') rate loss constraint. To be specific, efficient algorithms are introduced to find the optimal sensing time and power allocation in both single-carrier and multi-carrier systems. Performance evaluation is conducted to compare the system throughput between SBSS and spectrum overlay techniques. Simulation results suggest that SBSS outperforms spectrum overlay when the transmitter of the secondary user (SUT) is far away from the receiver of the primary user (PUR) in the single-carrier case. In the multi-carrier case, SBSS can achieve 15.2% increase of spectral efficiency over spectrum overlay when all the channel gains are independent and identically distributed.

We investigate the problem of embedding data in raw video sequences with minimum video mean-square distortion for any required data recovery error rate. In particular, for any given video frame sequence and any (block) transform domain of interest, we find the optimal carrier and scalar parametrized linear operator on the video data that maximize the output signal-to-interference-plus-noise ratio (SINR) of the maximum-SINR data receiver filter or, equivalently, minimize the average embedding distortion for any target message extraction error rate. The procedure is extended from single-carrier to multi-carrier (multiple messages) embedding. As a practical consideration, a sub-optimal computationally efficient embedding algorithm is also proposed. Extensive experimental results demonstrate that sub-optimal embedding as described has video distortion versus data extraction error rate performance comparable to optimal embedding. Our studies also demonstrate the robustness of the optimal (and sub-optimal) embedding schemes to H.264 compliant encoding.

We consider the problem of embedding multiuser data in digital media (such as images, video sequences, audio) with minimum perceived distortion. In this direction, we find the orthonormal set of embedding carriers that achieves maximum sum signal-to-interference-plus-noise ratio (sum-SINR) at the output of the receiver linear filters for any fixed embedding amplitude values. Then, for any given total embedding distortion constraint, we calculate the optimal multicarrier assignment and embedding amplitude values that maximize the sum capacity of the embedding process. Experimental results presented herein for multiuser data embedding in images demonstrate the effectiveness of the proposed methods.

We consider the problem of extracting blindly data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video). We develop a novel multicarrier/signature iterative generalized least-squares (M-IGLS) core procedure to seek unknown data hidden in hosts via multicarrier spread-spectrum embedding. Neither the original host nor the embedding carriers are assumed available. Experimental studies on images show that the developed algorithm can achieve recovery probability of error close to what may be attained with known embedding carriers and host autocorrelation matrix.

This paper considers the problem of blindly extracting data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video). We first develop a multi-signature iterative generalized least-squares (M-IGLS) core procedure to seek unknown data hidden in hosts via multi-signature direct-sequence spread-spectrum embedding. Neither the original host nor the embedding signatures are assumed available. Then, cross-correlation enhanced M-IGLS (CC-M-IGLS), a procedure described herein in detail that is based on statistical analysis of repeated independent M-IGLS processing of the host, is seen to offer most effective hidden message recovery. Experimental studies on images show that the proposed CC-M-IGLS algorithm can achieve recovery probability of error close to what may be attained with known embedding signatures and host autocorrelation matrix.

In this paper, we study the problem of finding the maximum throughput achievable by secondary users while primary users are guaranteed to achieve the minimum required data rates. We assume that node positions and channel side informations are available, and a virtual central unit is in responsible for scheduling both primary and secondary users. The optimal solution provides a theoretical upper bound of throughput for cognitive radio networks. An iterative algorithm proposed in [1] was extended to solve the throughput optimization problem, which can be decomposed into sub-problems and solved iteratively. However, due to the NP-hardness of Maximum Weighted Independent Set (MWIS) problem which has to be solved at each iteration, some greedy approximation method with polynomial computation time is applied in the iterative algorithm. Finally, we derive a theoretical upper bound for the distance between the greedy solution and the optimal solution, and guarantee that the optimal solution must be within this upper bound of the greedy solution. Numerical results are given to validate the solution.

The capacity optimization problem of MIMO links with interference has attracted an increasing interest. Due to the nonconvexity of the capacity problem, only suboptimal solutions can be found. In the previous works, a Gradient Projection (GP) algorithm [1] and a Quasi-Newton (QN) method [2] were proposed to provide suboptimal solutions subject to the constant power constraint. In this paper, we derive the capacity for MIMO links decomposed via SVD and interfered from other links. Then, each eigenchannel of MIMO link is represented by a set of logical links with a set of discrete data rates and discrete powers. An Integer Programming based algorithm (named as IP) is presented to solve the capacity optimization problem. The solution specifies the set of logical links that can transmit simultaneously. Numerical results show that GP and QN methods achieve better performance than IP method for the case of weak interference because of the convexity of the optimization problem when INR is sufficiently small. In the case of strong interference, IP method achieves better performance than GP and QN methods, which means that transmitting one link at a time is better than transmitting all links simultaneously with full power. In other words, scheduling links to transmit is more important for the case of strong interference.

We consider the problem of passive spread-spectrum steganalysis where the objective is to decide the presence or absence of spread-spectrum hidden data in a given image (a binary hypothesis testing problem). Unlike conventional feature-based approaches, we describe an unsupervised (blind) low-complexity approach based on generalized least-squares principles that may enable rapid high-volume image processing. Extensive experiments on image sets and comparisons with existing steganalysis techniques demonstrate most satisfactory classification performance measured in probability of correct detection versus induced false alarm rate.

In this paper, we propose an approach to manage network resources for a direct sequence code division multiple access (DS-CDMA) visual sensor network where nodes monitor scenes with varying levels of motion. It uses cross-layer optimization across the physical layer, the link layer, and the application layer. Our technique simultaneously assigns a source coding rate, a channel coding rate, and a power level to all nodes in the network based on one of two criteria that maximize the quality of video of the entire network as a whole, subject to a constraint on the total chip rate. One criterion results in the minimal average end-to-end distortion amongst all nodes, while the other criterion minimizes the maximum distortion of the network. Our experimental results demonstrate the effectiveness of the cross-layer optimization.

We consider the problem of cognitive code-division channelization (simultaneous power and code-channel allocation) for secondary transmission links co-existing with an unknown primary code-division multiple-access (CDMA) system. We first develop a blind primary-user identification scheme to detect the binary code sequences (signatures) utilized by primary users. To create a secondary link we propose two alternative procedures -one of moderate and one of low computational complexity- that optimize the secondary transmitting power and binary code-channel assignment in accordance with the detected primary code channels to avoid "harmful" interference. At the same time, the optimization procedures guarantee that the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR linear secondary receiver is no less than a certain threshold to meet secondary transmission quality of service (QoS) requirements. The extension of the channelization problem to multiple secondary links is also investigated. Simulation studies presented herein illustrate the theoretical developments.

The current and future wireless systems need to support a multitude of services with a wide range of data rates and reliability requirements. The limited battery resource at the mobile terminals coupled with the hostile multipath fading channel makes the problem of providing reliable multimedia services challenging. In this article, we develop a novel method to mechanize a prioritized unicast transmission at the physical layer in response to the disparate quality of service requirements for multimedia traffic (imposed by upper layers) and investigate how the unequal bit error protection offered by multiresolution modulation is capitalized at the data link layer to yield a substantial throughput gain and minimize the packet loss probability of the "more important" packets in an integrated voice/data and delay-sensitive applications (especially in poor and moderate channel conditions). However, there exists a slight throughput penalty in the high signal-to-noise ratio regime due to competitive nature of the "optimal reliable signaling rate" in an unknown time-varying channel.

In this paper, we present the lowest-computational-complexity maximum-likelihood (ML) decoder known to-date for 4×4 full-diversity Quasi-Orthogonal Space-Time Block Codes (QO-STBC) with symbols from square or rectangular quadrature amplitude modulation (QAM) constellations. The complexity savings come from a simplified quadratic ML decoding statistic that is being presented and the utilization of the signal points of the QAM constellation. Comparative computational complexity analysis is carried out and a simulation study demonstrates the theoretical equivalence of the proposed and original ML implementation.

The current and future wireless systems need to support a multitude of services with a wide range of data rates and reliability requirements. The limited battery resource at the mobile terminals coupled with the hostile multipath fading channel makes the problem of providing reliable multimedia services challenging. In this article, we develop a novel method to mechanize a prioritized unicast transmission at the physical layer in response to the disparate quality of service requirements for multimedia traffic (imposed by upper layers) and investigate how the unequal bit error protection offered by multiresolution modulation is capitalized at the data link layer to yield a substantial throughput gain and minimize the packet loss probability of the "more important" packets in an integrated voice/data and delay-sensitive applications (especially in poor and moderate channel conditions). However, there exists a slight throughput penalty in the high signal-to-noise ratio regime due to competitive nature of the "optimal reliable signaling rate " in an unknown time-varying channel.

Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where local spectrum resources may change from time to time and hop-by-hop. For this reason, a cross-layer opportunistic spectrum access and dynamic routing algorithm for cognitive radio networks is proposed, called ROSA (ROuting and Spectrum Allocation algorithm). Through local control actions, ROSA aims at maximizing the network throughput by performing joint routing, dynamic spectrum allocation, scheduling, and transmit power control. Specifically, the algorithm dynamically allocates spectrum resources to maximize the capacity of links without generating harmful interference to other users while guaranteeing bounded BER for the receiver. In addition, the algorithm aims at maximizing the weighted sum of differential backlogs to stabilize the system by giving priority to higher-capacity links with high differential backlog. The proposed algorithm is distributed, computationally efficient, and with bounded BER guarantees. ROSA is shown through discrete-event packet-level simulations to outperform baseline solutions leading to a high throughput, low delay, and fair bandwidth allocation.

For any given host image or group of host images and any (block) transform domain of interest, we find the signature vector that when used for spread-spectrum (SS) message embedding maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the corresponding maximum-SINR linear filter. We establish that, under a (colored) Gaussian assumption on the transform domain host data, the same derived signature minimizes host distortion for any target message recovery error rate and maximizes the Shannon capacity of the covert steganographic link. Then, we derive jointly optimal signature and linear processor designs for SS embedding in linearly modified transform domain host data and demonstrate orders of magnitude improvement over current SS steganographic practices. Optimized multisignature/multimessage embedding in the same host data is studied as well.

We propose an iterative generalized least squares procedure to recover unknown messages hidden in image hosts via spread-spectrum embedding. Neither the original host nor the embedding signature is assumed available. We demonstrate that for hidden messages of sufficient length (data sample support), recovery can be achieved with probability of error close to what may be attained with known embedding signature and known original host autocorrelation matrix. For small hidden messages, the signature estimate calculated by the iterative generalized least squares procedure can be fed as initial value to a (computationally costly) expectation-maximization signature identification scheme that we derive. Message recovery can again be carried out successfully by means of a linear sample-matrix-inversion minimum-mean-square-error receiver.

We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multiple-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

Blind adaptive linear receivers are considered for the demodulation of direct-sequence code-division multiple-access signals in asynchronous transmissions. The proposed structures are self-synchronized in the sense that adaptive synchronization and demodulation are viewed and treated as an integrated receiver operation. Two computationally efficient combined synchronization/demodulation schemes are proposed, developed, and analyzed. The first scheme is based on the principles of minimum-variance distortionless-response processing, while the second scheme follows the principles of auxiliary-vector filtering and exhibits enhanced performance in short data-record scenarios. In both cases, the resulting receiver is a linear structure of order exactly equal to the system processing gain. Simulation studies included in this paper demonstrate the coarse synchronization as well as the bit-error rate performance of the proposed strategies.

In this paper, we investigate the effect of varying the processing gain in place of power control in a CDMA packet radio network. The classic CDMA protocol is modified to accommodate the variable processing gain, allowing for variable packet durations due to variable processing gain. The performance of the variable processing gain system is measured in terms of the network throughput and is compared to the performance of conventional fixed rate CDMA systems in which perfect power control is assumed. Results are first generated for a terrestrial CDMA network and extended to a low Earth orbit satellite (LEOS) communication topology. In the interference limited CDMA network, we have demonstrated that processing gain control can be used in place of power control and, depending on the details of the implementation, can achieve the same or nearly the same throughput performance.

The problem under consideration is the adaptive reception of a multipath direct-sequence spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive impulsive noise. An SS receiver structure is proposed that consists of a vector of adaptive chip-based Hampel nonlinearities followed by an adaptive auxiliary-vector linear tap-weight filter. The nonlinear receiver front end adapts itself to the unknown prevailing noise environment providing robust performance over a wide range of underlying noise distributions. The adaptive auxiliary-vector linear tap-weight filter allows rapid SS interference suppression with a limited data record. Numerical and simulation studies under finite-data-record system adaptation show significant improvement in bit-error-rate performance over the conventional linear minimum variance-distortionless-response (MVDR) SS receiver or conventional MVDR filtering preceded by vector adaptive chip-based nonlinear processing.

This paper analyzes the throughput for an adaptive packet radio network (PRN). The proposed adaptive OFDM spread spectrum (OFDM-SS) system can trade off system processing gain (i.e. user data rate) for transmission quality under changing channel conditions. Classic throughput analysis for fixed and random access schemes is extended for this proposed adaptive PRN. The focus is on pure-ALOHA and nonpersistent CSMA (NP-CSMA) systems. The paper introduces a method to analyze network throughput with variable packet durations due to variable user data rate. The large improvement from the adaptive system compared to the conventional fixed rate counterpart is demonstrated by various mobile user population profiles and radio propagation path loss models.

We consider the problem of robust detection of a spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive non-Gaussian noise. The proposed general SS receiver structure is comprised by a vector of adaptive chip-based nonlinearities followed by an adaptive linear tap-weight filter and combines the relative merits of both nonlinear and linear signal processing. The novel characteristics of our approach are as follows. First, the nonlinear receiver front-end adapts itself to the unknown prevailing noise environment providing robust performance for a wide range of underlying noise distributions. Second, the adaptive linear tap-weight filter that follows the nonlinearly processed chip samples results in a receiver that is proven to be effective in combating SS interference as well. To determine the receiver parameters, we propose, develop, and study three adaptive schemes under a joint mean-square error (MSE), or a joint bit-error-rate (BER), or a joint MSE-BER optimization criterion. As a side result, we derive the optimum decision fusion filter for receivers that utilize hard-limiting (sign) chip nonlinearities. Numerical and simulation results demonstrate the performance of the proposed schemes and offer comparisons with the conventional matched-filter (MF), the decorrelator, the conventional minimum-variance-distortionless-response (MVDR) filter, and the sign-majority vote receiver.

Two systems that use orthogonal frequency division multiplexing (OFDM) as a spread spectrum technique are described and their performance is evaluated in the presence of tone and pulsed Gaussian interference. One system obtains frequency diversity by sending the same data bit simultaneously on all the carriers while a second system obtains both time and frequency diversity by sequentially sending a given data bit on different carriers until it is sent on all possible carriers. Interference excision, in which some of the transform bins are removed in the detection process, is used in the receivers. Simulated BER performance in AWGN and interference is presented for systems that use a number of different block transforms and the modulated lapped transform (MLT) and it is shown that the MLT provides much better performance due to its ability to confine the interference energy to a few transform bins.

In order to mitigate narrow-band interference in spread spectrum communications systems, novel communications receivers incorporating transform domain filtering techniques are designed. In this paper, lapped transforms are used to transform the received data signal to the transform domain wherein adaptive excision is performed. Transform domain detection algorithms, which yield bit decisions based on the remaining signal energy, are analyzed and, together with excision, are employed on a block-by-block basis to suppress single-tone and narrow-band Gaussian interference. System performance is analytically quantified in terms of the overall system bit-error rate (BER). Subsequent results are presented for a variety of channel conditions and compared to those obtained using excision algorithms based on orthonormal block transforms (Medley 1995). These results demonstrate the improved performance and increased robustness with respect to jammer frequency and bandwidth of lapped transform domain excision techniques relative to similar algorithms based on nonweighted block transforms.