This work addresses control information loss in manned-unmanned teaming networks. We consider methods for mitigating smart jammers that target control channels to disrupt transmission scheduling and degrade communication. In this context, the well-studied Max-Weight algorithm achieves throughput optimality without prior knowledge of arrival rates, but its performance in the presence of control information jamming is under-explored. We propose two novel scheduling policies, VFMW-OS and VFMW-HS, to improve network resilience. VFMW-OS dynamically adjusts the control-information coding rate to overcome jamming of the control channel. Likewise, VFMW-HS opportunistically switches between centralized and decentralized scheduling based on the network's queue lengths to further adjust the rate at which control information is exchanged so as to minimize delay. Simulations show both policies enhance robustness and reduce delay under adversarial conditions.

We present a novel framework that leverages time series clustering to improve internet traffic matrix (TM) prediction using deep learning (DL) models. Traffic flows within a TM often exhibit diverse temporal behaviors, which can hinder prediction accuracy when training a single model across all flows. To address this, we propose two clustering strategies —source clustering and histogram clustering —that group flows with similar temporal patterns prior to model training. Clustering creates more homogeneous data subsets, enabling models to capture underlying patterns more effectively and generalize better than global prediction approaches that fit a single model to the entire TM. Compared to existing TM prediction methods, our method reduces RMSE by up to 92% for Abilene and 75% for GÉANT. In routing scenarios, our clustered predictions also reduce maximum link utilization (MLU) bias by 18% and 21%, respectively, demonstrating the practical benefits of clustering when TMs are used for network optimization.

Using data from V2V links along with onboard sensor data is recognized as a crucial step towards the safety and reliability of future automated driving. We address short-term trajectory planning for the ego vehicle. We propose a threat field model of the traffic surrounding the ego vehicle. Informally, the threat field indicates the possibility of collisions in the vehicle’s vicinity. We study uncertainty in the threat field due to uncertainty in the positions and velocities of surrounding vehicles, which may be known to the ego CAV via basic safety messages. The cost of trajectories is defined by the expected threat exposure, and the risk of trajectories is quantified based on the variance in cost. Uncertainty quantification is studied using Monte Carlo sampling as well a perturbation-based approach. The main result of this paper is the observation that small localization errors and/or speed measurement errors can lead to large risks in planned trajectories.

In this paper, we use Discrete Event Systems (DES) to conduct a novel reliability analysis regarding network longevity and sensor nodes (SNs) energy failure in the agriculture domain. Reliability is a critical aspect of Agriculture Internet of Things (IoT) networks. As a result, we propose an operation-based reliability framework for the agriculture IoT networks based on the following: (i) theoretical derivation of a reliability framework from a real-time parametric perspective at the local network level, and (ii) comprehensive assessment of the reliability of agriculture IoT networks using the network lifetime metric. We have proposed a DES model that is used to implement a reliability framework that analyses energy expenditure to optimize network premature failure of operations.

In this paper, we propose a new multi-objective optimization (MOO) framework to maximize power consumption and coverage stability of the clustering-based Agricultural Internet of Things (CA-IoT). The planning, design, and operational phases of CA-IoT networks give rise to energy management, connectivity, and application-related challenges which often result in conflicting MOO problem. The correlations amongst these objectives and their impacts on the network lifespan and operational efficiencies remain unresolved. The impacts and correlations amongst the core MOO decision metrics for our framework are uniquely established from an extensive characterization and implementation of a real CA-IoT network. Sample results from a CA-IoT network based on our MOO Framework performed better than the state of the art in terms of network lifespan, network stability periods, and coverage stability.

In this paper, we propose an energy-efficient multihop routing framework that is based on the total communication costs metric (ETER,min), and implement it in a multihop clustering-based Agricultural Internet of Things (CA-IoT) network. Furthermore, an impact assessment based on the network lifespan or power-savings, scalability/adaptability, and coverage stability metrics is performed on the proposed framework. As the power-constrained CA-IoT network scales, a more realistic, energy-efficient multihop route-actuating framework becomes imperative for achieving the stated desired metrics in the new-age IoT applications. The proposed framework defined the desired location and power consumption specifications of IoT devices. Sample simulation results show that our framework ensures higher network energy-savings, scalability, and network stability span compared to single-hop routing schemes under similar conditions.

Cellular Vehicle-to-everything (C-V2X) was introduced by 3GPP in Release 14, which supports direct vehicle-to-vehicle (V2V) communication using sidelink channels. There are two modes in C-V2X for supporting V2V communication: Mode 3 and Mode 4. Both modes support V2V communications but differ on their spectrum allocation methodology. In out-of-coverage scenario defined under Mode 4, vehicles reserve spectrum resources using semi-persistent scheduling to enable direct communication. Currently, Intelligent Transportation System (ITS) spectrum at 5.9 GHz is emerging as an immediate resource to facilitate vehicular communication which has already been assigned for IEEE 802.11p. However, these dedicated channels will not be sufficient to support the connected vehicles in densely populated cities. Leveraging the existing underutilized wireless spectrum elsewhere, such as in digital television spectrum band is a likely solution. An intelligent scheme is required which can make adaptive decisions based on channel environment to efficiently use the secondary spectrum. Bumblebee behavioral model possess evolved decision-making mechanisms to adaptively solve similar problems while foraging in environments containing multiple floral resources (channels). Simulation results using bumblebee-based algorithm have shown that a memory system based on the averaging of stored channel energy information dramatically increased VDSA performance over a memoryless system in both urban and highway scenarios by 52% and 20%, respectively. In this paper, we propose a vehicular dynamic spectrum access (VDSA) solution based on bumblebee behavioral model for C-V2X and evaluate its performance using OpenAirInterface (OAI) LTE-Sidelink platform.

This paper characterizes the impact of interference on vehicular communications employing beamforming links between transmitters and receivers operating within a multipath propagation environment. Different road scenarios, such as an intersection and a roundabout are considered to determine the performance characteristics of the vehicle communication link. By employing the results for different antenna array elements and by varying distance, we analyse the bit error rate (BER) of vehicles operating in multipath propagation environments with an interferer within the vicinity of an communication link.

This paper introduces an integration strategy between ns3, a network simulator, and NetLogo, an agent-based model (ABM), to help answer policy level broadband allocation questions. Economic development is maximized by accurately modeling the communications system and human behavior together. The digital divide is well recognized as a root cause of economic inequality and a global problem in both rural and urban communities. Within the United States, federal and state efforts have funded investments in broadband infrastructure. However, available funds are limited or provided as 50-100% loans and eligibility issues limit participation. Consequently, infrastructure planners are in need of new tools to strategically allocate available funds. While ABM is a popular policy analysis tool, other rural broadband analyses have not accurately incorporated network limitations. By integrating ns3 and NetLogo, we improve the validity of policy experiments by accurately representing both technical and human components of the system.

In this paper, we propose a distributed Vehicular Dynamic Spectrum Access (VDSA) framework for vehicles operating in platoon formations. Given the potential for significant congestion in licensed frequency bands for vehicular applications such as 5.9 GHz. Our approach proposes to offload part of the intra-platoon data traffic to spectral white-spaces in order to enhance vehicular connectivity in support of on-road operations. To enable VDSA, a Bumblebee-based decision making process is employed which is based on the behavioral models of animals, is employed to provide a means of distributed transmission band selection. Simulation results show the distributed VDSA framework improves the leader packets reception ratio by 5%, thus indicating its potential to increase in reliability of intra-platoon communications.

In this paper, we analyze the performance of the multichannel version of Enhanced Distributed Channel Access (EDCA) and compare its results with the single channel implementation. The novelty with this analysis is that we consider all four Access Category (AC) queues per channel across all channels are enabled. EDCA is an option for ensuring quality-of-service (QoS) of vehicular network communications. EDCA applies four ACs with different priorities to various services within one vehicular node. Additionally, multichannel operation can be deployed in order to provide a variety of safety or non-safety services. In multichannel mode, the time is divided into either a Control Channel Interval (CCHI) or a Service Channel Interval (SCHI) consisting of equal time durations. The safety-related messages and the control messages are sent during CCHI. The non-safety messages are transmitted during the SCHI. The simulation experiments are performed via a custom-built vehicular network simulation environment created using MATLAB Discrete Event System (DES). The results verify that multichannel is effective and necessary to guarantee a relative low data latency.

The bumblebee has recently been proposed as a model to optimize channel allocation in connected vehicle networks where the quality of each channel varies unpredictably over time and space. A fundamental mathematical challenge that must be overcome before implementing the bumblebee model is determining the theoretical upper bound of spectrum optimization that can be achieved under such stochastic channel conditions. In this paper, we leverage the concept of queuing theory in order to conduct the performance bound analysis of bumblebee-inspired distributed optimization operation in vehicle-to-vehicle (V2V) environments. We initially established the maximum switching costs associated with urban and highway environment, and then used GEMV2and SUMO to compute the performance bounds for several metrics in a time-variant urban environment, including Pm(the probability of all channels being busy) and mean response time. We discuss the implications of these results for future development of bumblebee-inspired vehicular communication systems.

In this paper, we design a test-bed for a bumblebee inspired channel selection algorithm employed on a wireless ad-hoc network. Vehicles in connected ad-hoc networks are routinely challenged with the complex decision-making problem of either staying with the same channel or moving to a different channel under highly time-varying channel quality conditions. In order to enable vehicles to adapt to these time-varying channel conditions, we designed a bumblebee-inspired decision-making algorithm. The proposed algorithm uses temporal channel quality information for optimal channel selection. Channel energy values are used for making switching decisions by taking switching costs into consideration. We implemented a test-bed inside a controlled laboratory environment using ADALM-Pluto Software-defined radios (SDRs) to evaluate the proposed approach. The test-bed can be configured to emulate a vehicular network environment. Furthermore, in this work we show the novelty of our bumblebee algorithm for optimal channel selection in a steady environment. We compared the performance of our Bumblebee algorithm with a random channel selection algorithm, and noticed an increase of 158.33% in the throughput of the network with our algorithm.

In this paper, we present a novel approach for mitigating positioning errors in vehicle-to-vehicle (V2V) networking environments where digital beamforming is conducted at both transmitters and receivers. Location information of all transmitters and receivers in V2V networks performing beamforming is essential in order to ensure reliable link quality. However, there exists several sources of error where this location information can be corrupted or out-of-date. By leveraging the proposed approach in this paper employing state estimation, these errors can be mitigated, thus providing more accurate location information relative to V2V networking architectures that do not employ techniques to help mitigate potential sources of location error. Simulation results show a 99% improvement of the proposed approach relative to V2V beamformiong architectures that do not account for location errors.

In this paper, we present an analysis of Vehicle-to-Vehicle (V2V) Networks employing beamforming at both the transmitter and the receiver when positioning errors are present. Specifically, we will examine the performance of this system setup when the source of the positioning errors are from the Global Positioning System (GPS) measurements as well as from variations in the delays due to the overhead communication channels. To achieve adequate performance, beamforming requires precise location information of the transmitter and the receiver, and when accurate information is unavailable the system performance can potentially deteriorate. This is especially challenging in highly mobile vehicle networking environments where the operating conditions and location information varies rapidly in time. Simulation results are obtained showing Bit Error Rate (BER) values increasing by 99.9% when the exact GPS locations are known by the vehicles when compared to a 6.6m error in GPS location.

In this paper, we propose a low decay, low bias dataset synthesis framework that models Machine Learning (ML) dataset theory using Python classes and instruction files, and whose simulation results show an 11.58% entropy decrease at classification time relative to state-of-the-art training sets. The demand for signal-domain Neural Networks (NNs) have increased significantly in recent years with respect to the classification of observed radio activity. In particular, there has been a growing interest in choosing appropriate training data in order to enhance NN performance at classification time. Developing ML based signal classifiers requires training data that captures the underlying probability distribution of real signals. To synthesize a set of training data that can capture the large variance in signal characteristics, a robust framework that can support arbitrary baseband signals and channel conditions is presented.

In this paper, we propose a VANET architecture capable of securing anonymous data collection from a distributed set of autonomous vehicles. The proposed architecture features a hybrid combination of centralized and decentralized routing concepts. Unlike other VANET implementations, our proposed architecture allows lower latency by having a ad hoc routing option as well as greater availability via the centralized methods. The proposed architecture guarantees user anonymity within the VANET framework. Most VANET models assume most users do not value the privacy of their identity. We assume that each vehicle is equipped with a VANET computer capable of storing data, and that vehicles can communicate directly with each other and exchange data within short distances.

Lane changing behavior is one source of vehicle accidents on the road. Before changing to another lane, the driver needs to carefully assess the distances to surrounding vehicles. Vehicle-to-Vehicle (V2V) communications provide an opportunity to make lane changing decisions more safe and efficient. Within a vehicular network, all connected vehicles are sharing drive information in real-time, which improves the vehicle's situational awareness regarding the surrounding traffic conditions. This paper presents two lane changing schemes for connected vehicles based on V2V communications. The proposed schemes use simple ACK to make sure the messages communicated during the lane changing process is reliable. Furthermore, the proposed schemes are evaluated by a custom-built VANET simulator created using the MATLAB Discrete-Event System environment. The simulation results prove that the proposed lane changing schemes not only guarantee the safety of lane changes but also improve the efficiency of road traffic.

Future spacecraft communication subsystems will potentially benefit from software-defined radios controlled by artificial intelligence algorithms. In this paper, we propose a novel radio resource allocation algorithm leveraging multiobjective reinforcement learning and artificial neural network ensembles able to manage available resources and conflicting mission-based goals. The uncertainty in the performance of thousands of possible radio parameter combinations and the dynamic behavior of the radio channel over time producing a continuous multidimensional state–action space requires a fixed-size memory continuous state–action mapping instead of the traditional discrete mapping. In addition, actions need to be decoupled from states in order to allow for online learning, performance monitoring, and resource allocation prediction. The proposed approach leverages the authors’ previous research on constraining decisions predicted to have poor performance through ”virtual environment exploration.” The simulation results show the performance for different communication mission profiles, and accuracy benchmarks are provided for the future research reference. The proposed approach constitutes part of the core cognitive engine proof-of-concept delivered to the NASA John H. Glenn Research Center’s SCaN Testbed radios on-board the International Space Station.

Given recent advances in High Speed Trains (HSTs), there has been an extensive amount of research dedicated to implementing Long Term Evolution cellular communications for Railways (LTE-R) with respect to next generation wireless communication systems. Achieving a reliable coverage inside a tunnel environment has always been a challenging task. Leaky Coaxial Cables (LCXs) have been shown to provide a uniform signal coverage in indoor environments such as offices and malls, and have been experimentally evaluated for tunnel environments. In this paper we model the HST channel inside a tunnel employing LCX and considering the classical two-ray propagation model. We analyze the LTE-R communication system utilizing the LCX and determine small-scale fading characteristics of the channel. Our channel model also takes into account the large Doppler shift caused by the high velocity of the trains. In addition to the proposed channel model, a dynamic Rician K-factor function depending on the reflector's material properties is also derived. The proposed channel is characterized by its bit error rate (BER) performance.

In this paper we implement a cooperative spectrum sensing test-bed using software-defined radios with different sensing capabilities, e.g., sampling rates, RF characteristics, etc. Normalized energy detection is used for spectrum sensing due to its low implementation complexity. The proposed heterogeneous spectrum sensing test-bed is implemented using USRP N210s and RTL-SDRs within a controlled indoor laboratory environment. All signal processing is performed using GNU-Radio and post processing is conducted using MATLAB. Sensor units (SUs) are designed to generate the local test statistic L and transmit it to a Fusion Center for decision making. The Fusion Center (FC) is a base station that decides whether signal is present or not based on the data from all sensor units. We implement both soft and hard data fusion techniques and compare their performance in a practical fading channel scenario.

The IEEE 802.11p/DSRC protocol is considered to be a viable technology for inter-vehicle safety communications. In order to support the control algorithms employed by autonomous vehicles, low packet delay is a requirement for Vehicle Ad-Hoc Networks (VANETs). In a VANET, vehicle-to-vehicle (V2V) communications play an important role for ensuring a safe and reliable autonomous driving environment since safety messages are transmitted via V2V communication. The Wireless Access in Vehicular Environment (WAVE) protocol includes the Enhanced Distribution Channel Access (EDCA) mechanism to ensure the Quality of Service (QoS). Therefore, it is necessary to create accurate simulations that guarantee the V2V communications will work as described in the protocols. In this paper, we will present a newly designed VANET simulator based on a Discrete-Event System in MATLAB and a series of simulations of the MAC/PHY layer for the DSRC/WAVE V2V network. The simulation results for the delay are compared with previous papers in order to verify the simulator.

Due to the differences in terms of antenna height, scatterer density, and relative speed, V2I links exhibit different propagation characteristics compared to V2V links. We develop a geometry-based path loss and shadow fading model for V2I links. We separately model the following types of V2I links: line-of-sight, non-line-of-sight due to vehicles, non-line-of-sight due to foliage, and non-line-of-sight due to buildings. We validate the proposed model using V2I field measurements. We implement the model in the GEMV2 simulator, and make the source code publicly available.

There is a "Digital Divide" between rural and non-rural residents. A major initiative in bridging this gap is the approval of rules for the use of television white spaces (TVWSs) by the Federal Communications Commission (FCC). The IEEE 802.22 standard provides guidelines for utilizing these TVWS channels while protecting the incumbent user's rights. However, due to the high peak-to-average-power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM)-based IEEE 802.22 signals, the power amplifier (PA) in the transmitter RF chain is operated in its saturation region, causing signal clipping and hence, spectral leakage in the adjacent channels. To combat this issue, we propose a novel, computationally fast approach for digital predistortion (DPD) of the PA in spectrally agile wireless transmitters such that the RF mask is complied with.

Abstract In this paper, we propose an approach for mitigating deterministic and partially deterministic jamming signals from the received signal space, thus yielding recoverable signal vectors at a target receiver. Aimed at friendly or self‐jamming environments, where enemy communication jamming is paramount. The proposed approach employs a concept calledspectral subtraction, where one or more known signals can be removed from the received signal space via the subtraction of their spectral characteristics from the received composite signal. Although spectral subtraction is used in a variety of speech communication scenarios, it has not been extensively employed in wireless applications because of several practical challenges, such as achieving both sufficient time alignment and accurate waveform characterization of the signal to be removed. In order to assess these challenges, as well as analyze the potential benefits of the proposed approach, validation was performed using actual over‐the‐air experimentation using software‐defined radio technology. The feasibility study of the proposed approach for achieving sufficient signal removal was examined for a constrained operating scenario, and experimental results show that spectral subtraction can be achieved in a physical transmission environment. Although physical simulations were limited, they provide baseline results for a previously untested method of jammer suppression. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Compared with a wired network, a wireless network is not protected by the cable transmission medium. Information is broadcasted over the air and it can be intercepted by anyone within the transmission range. Even though the transmissions could potentially be protected by security authentication mechanisms, malicious users can still intercept the information by mimicking the characteristics of normal user or a legitimate access point. This scenario is referred as a man‐in‐the‐middle (MITM) attack. In the MITM attack, the attackers can bypass the security mechanisms, intercept the unprotected transmission packets, and sniff the information. Because of several vulnerabilities in the IEEE 802.11 protocol, it is difficult to defend against a wireless MITM attack. In this paper, a received signal strength indicator (RSSI)‐based detection mechanism for MITM attacks is proposed. RSSI information is an arbitrary integer that indicates the power level being received by the antenna. The random RSSI values are processed via a sliding window, yielding statistic information about the signal characteristics such as mean and standard deviation profiles. By analyzing those profiles, the detection mechanism can detect if a rogue access point, the key component of an MITM attack, is launched. Our proposed approach has been validated via hardware experimentation using Backtrack 5 tools and MATLAB software suite. Copyright © 2014 John Wiley & Sons, Ltd.

In this article, we focus on the RF and digital front-end design and implementation challenges associated with future 5G radio access systems with special emphasis on spectrally contained waveforms and small-cell system scenarios. In general, filter bank based multicarrier (FBMC) type of techniques have various potential benefits due to their excellent spectral containment compared to classical OFDM(A). However, these spectrally contained waveforms lose their intriguing spectral properties when power amplifier (PA) nonlinearities are considered. Flexible and efficient digital predistortion (DPD) algorithms are thus considered an interesting solution in order to restore the spectral containment of such advanced 5G waveforms. Noncontiguous spectrally-agile transmission is another key feature of future 5G systems for increasing data rates and spectral allocation flexibility. However, the PA nonlinearities impose even more severe challenges in such noncontiguous transmission scenarios due to the resulting spurious intermodulation emissions that can easily violate the emission limits or even desensitize the own receiver in frequency division duplexing based systems. Furthermore, at the network deployment level, different small-cell concepts are expected to play a major role in future 5G networks. Unlike the ordinary macro base-stations, the digital computing capabilities in small-cell base-stations are much more limited. Furthermore, they should also adopt lower-cost and small-size analog RF components, while still maintaining high energy-efficiency. The afore-mentioned constraints, along with advanced 5G waveforms, call for flexible and low-complexity DPD solutions, a challenge addressed in this article. We report novel DPD methods with built-in capability to direct the linearization performance to pre-defined frequencies or subbands in a flexible manner, and demonstrate their good performance and complexity benefits in the context of non-contiguous FBMC transmission.

In this paper, we analyze the probability of error for secondary users employed in a cognitive radio network operating within the vicinity of several primary users. Secondary( unlicensed) users must coexist with primary(licensed) users, ensuring that the legacy rights of the primary user transmissions are respected. Consequently, the secondary users must either sense the channel periodically, which has some detection delay, or must check a spectrum availability database, which may not be updated in real-time. Hence, there will be times when the primary user and the secondary user will occupy the same band and interfere with each other. The aim of this paper is to quantify the impact of the probability of error of the secondary user when it does not react quickly enough to the presence of a primary user. Several numerical examples are provided in order to illustrate the analytical results.

In this paper, we analyze the performance of high-velocity connected vehicles network. We assess the V2V (vehicle-to-vehicle) architecture in two different scenarios. The first scenario involves direct communication between source and the destination via a non-line-of-sight (NLOS) link. The second scenario uses line-of-sight (LOS) multihop relay links to connect source and destination. For both scenarios, we use two performance metrics: the probability of error and latency. The results of our analysis show the performance limits of this type of networks in practical situations. For example, the LOS network outperforms the NLOS one at an SNR of 7dB if the connected vehicles are moving at consistent velocities, and the number of hops is limited to 8.

In this short paper, we present an analysis for co- channel interference between femtocell (FC) downlink transmissions in a purely homogeneous femtocell type network. We assume no direct coordination between FCs since, as of Release 12, no low-latency links are provided for intra-cell communication. Our simulations utilize VoIP traffic models to understand the interference impact of QoS oriented traffic as network densities increase. The preliminary results demonstrate the reduced performance of FCs under this co-channel model when resource allocations depend on existing sensing and traditional usage of sensing information.

In this short paper, we present a Software-Defined Radio testbed for multi-node simulations. This testbed was created with the goals of providing repeatable simulations, while reducing development time of SDR implementations. To provide an accessible development environment, the testbed is primarily based in the MATLAB language.

This paper analyzes a mobile satellite communication system with respect to the BER performance between a geostationary satellite and a moving node. Specifically, we study a scenario where the channel causes ionospheric scintillation-based multipath fading within the UHF frequency band. The scenario considers medium and high ionospheric scintillation indexes for Rural Area (RA) and Hilly Terrain (HT) multipath profiles. A BER performance decrease of more than 2 orders of magnitude for an ionospheric scintillation index of 0.3 occurs when the terrestrial multipath fading is added to ionospheric scintillated signals. This effect can be associated with satellite link loss, such as the one observed during Operation Anaconda at the Battle of Takur Ghar [1] in Afghanistan. Consequently, we propose a channel model that accounts for these effects, composed by two Rician channels connected in series and employs a K-factor equation in terms of the terrain's reflection coefficient.

The estimation of temporal occupancy statistics is a common monitoring output for spectrum management. In emerging dynamic spectrum access (DSA) networks as well as for recent interference limit policies such as harm claim threshold mechanisms, this is even more crucial for compliance enforcement since operational parameters additionally can include temporal constraints. In this paper, random temporal sampling is explored, for probabilistic characterization of temporal channel occupancy. The precision and bias performance due to various random temporal measurement plan designs are examined, both analytically as well as through experiments implemented using Software Defined Radio technology. A framework for performance analysis of random temporal sampling for compliance enforcement is presented, and a lower bound on sensing performance in terms of estimator precision is derived for spectrum occupancy modeled as an alternating renewal process. Using random sampling, estimator variance is seen to approach the theoretical lower bound using larger sample sizes, or through sparser sampling. Results further suggest that the detection error impacts the performance of random temporal sampling for average temporal occupancy estimation. The work further motivates the use of probabilistic characterization of spectrum occupancy for compliance enforcement, given the non-deterministic behavior of dynamic spectrum access mechanisms in emerging wireless network deployment scenarios.

In this paper, we present the evolution of software-defined radio (SDR) technology and show how it is currently at the forefront of numerous advances within the wireless sector, enabling new applications considered unrealizable only a decade ago. Specifically, this paper focuses on SDR from a discrete-time sampling perspective and discusses the efforts that are currently being pursued in order to further bridge the gap between these discrete-time samples, the hardware used to generate this information from continuous-time over-the-air signaling waveforms, and the software and digital logic used to process these samples into digital data via baseband processing. Given the extensive deployment of SDR technology across a growing number of applications, such as national defense, public safety, connected vehicles, education, and scientific research and development activities, it is vitally important that the wireless community understands the features, advantages, and limitations of this technology. With its versatility, cost, and functionality continuously improving, SDR has become a viable solution for prototyping wireless transceivers and networks that are much more tailored to specific applications and performance requirements relative to available off-the-shelf wireless solutions. To highlight the advantages and current issues with SDR technology, this paper presents several examples using a recently released, commercially available SDR platform.

In this paper, we present a novel digital predistortion (DPD) solution based on a direct learning approach, which is capable of reducing the unwanted emissions resulting from the power amplifier (PA) at any prespecified frequency located in the transmitter's out-of-band or spurious domain. The proposed scheme is based on evaluating the power spectral density (PSD) of the PA output signal and optimizing the DPD coefficients iteratively in order to minimize the output PSD around the prespecified frequency. To highlight the feasibility of the proposed implementation, the predistortion processing is kept as simple as possible, deploying quasi-memoryless polynomial models. Efficient mitigation of unwanted emissions around the target frequency is demonstrated via simulations and actual RF measurements, in both single- and dual-carrier waveform scenarios, using memoryless and memory-based PAs. The proposed DPD solution could be potentially employed in applications such as mobile devices utilizing noncontiguous multicarrier transmission, where the intermodulation spurs may overlap with the device's own receiver band, or could be potentially violating the spurious emission limits. Another target application is cognitive radio, where the PA may produce unwanted emissions that are interfering with primary-user transmissions. To the best of the authors' knowledge, there does not exist a similar technique in the open literature, and thus, the purpose of this paper is to encourage scientific discussions and technological innovations toward the creation of relatively low-complexity frequency-optimized predistortion techniques employed against selected unwanted emissions produced by the transmitter.

In this paper, we propose a statistical channel model for decode-and-forward relaying vehicular ad hoc networks (VANETs) with single-input-single-output (SISO) antennas. The proposed model uses a sum-of-sinusoids (SoS) Rician model, which is derived for highway scenarios with line-of-sight (LOS) components, time varying conditions and multipath excess delay. Since the time delay differences between multipath components are considered in this work as opposed to previous studies, the power of channel impulse response is conserved. One of the advantages of the proposed channel model is that it does not depend on parameters such as distance and angle between vehicles, which changes continuously in a highway transmission environment. Using the proposed channel model, we analyze the relay network capacity of a VANET operating in highway traffic conditions. The results show that, the proposed channel model has higher performance relative to previous studies since the model contains the effect of time delay between multipath components.

Information transmission through the use of video imagining systems has the potential to build upon the success of imaging communication systems, such as QR Codes and 2D Barcodes. Video communication systems add a temporal dimension to image-based information transmission systems, greatly expanding the amount of data that can be transferred. Such a system allows for secure transfer of data through the use of a line-of-sight (LOS) visual channel, making it attractive for several applications, including in-store purchases, banking, and stealth military missions. The nature of the LOS visual channel also reduces interference from other applications, making it a very attractive option for implementing short range communications systems from a cell phone, computer, tablet, or other device. This paper investigates how a video imaging system can be used for information transmission. A prototype system has been designed and implemented as a proof of concept, and the performance of the prototype is evaluated.

Underlay communications has the potential to become a valuable tool for increasing the capacity of spectrum provided that the quality of communications is upheld. In this paper, we propose a method for underlay nodes to detect, and coordinate with, other underlay nodes to form a network on preoccupied spectrum. Specifically, we introduce a novel optimization algorithm, called the Coordinated Optimization of EXposure Introduced by a Secondary Transmitter (COEXIST), which selects the best communications channel for all nodes within a network while minimizing interference with the primary user. This algorithm does not require knowledge of spatial location of the primary users or underlay nodes. With the proposed solution, underlay nodes can detect other nodes at power levels far below the power level of the primary user and, once a network is formed, nodes can continue to communicate at very low power levels without causing interference to primary users transmitting at the same time.

In this paper, we present an adaptive digital communication system using field programmable gate array (FPGA) technology. This system adapts the Universal Software Radio Peripheral (USRP) to better suit the space and power limitations of the CubeSat satellite form factor and the Space Plug-and-Play Avionics (SPA) protocol. The result is a highly-adaptive, plug and play software-defined radio (SDR) that is easily incorporated into any CubeSat design.

In this paper, we propose a reinforcement learning-based approach for enabling vehicles to make intelligent channel selection choices across TV whitespace spectrum. In order for vehicle communication networks to dynamically access TV whitespace in a secondary manner, it is imperative that these communication systems be capable of coexisting with other types of secondary wireless networks operating within the same frequency range. Consequently, we first propose a TV whitespace channel sharing scheme that would facilitate the coexistence between WLAN, WRAN, and vehicular communication networks. Using the channel utilization variations observed by a collection of mobile vehicular communication systems, we then devised a reinforcement learning-based adaptive channel selection algorithm that employs channel utilization sensing in order to reinforce the decisions made by the vehicular communication system. Moreover, the parameters of the proposed learning approach are adaptively tuned in order to achieve better adaptation to a particular environment. A computer emulation environment composed of actual real-world sensing measurement data and a simulated TV whitespace network is created in order to accurately model the characteristics of future wireless environment, as well as to test the proposed learning-based channel access approach. Experimental results show a significant performance improvement with respect to vehicle communication.

We propose a novel channel selection method for transmitting information that takes into account the amount of communication data and control data generated by the secondary users within a cognitive radio network. In this paper, multiple primary channels are characterized according to a channel occupancy ratio and a state transition ratio (STR), from which the secondary user selects the channel most suitable for transmitting the control information. By obtaining these ratios, the secondary user can estimate the size of a spectral white space for each channel belonging to the primary user. The proposed method is evaluated through computer simulation results and we can confirm the long-term white space can be remained for data transmission.

In this paper, we propose an approach for detecting primary user emulation attacks in cognitive radio networks. Cognitive radios (CRs) have been proposed as a promising solution for improving spectrum utilization via opportunistic spectrum sharing. In a CR network environment, primary (licensed) users have priority over secondary (unlicensed) users when accessing the wireless channel. Thus, if a malicious secondary user exploits this spectrum access etiquette by mimicking the spectral characteristics of a primary user, it can gain priority access to a wireless channel over other secondary users. Our proposed approach is initiated by energy detection to locate the existing users on the frequency band. The approach employs a cyclostationary calculation to represent the features of the user signals, which are then fed into an artificial neural network for classification. As opposed to current techniques for detecting primary user emulation attacks in CR networks, our proposed approach does not require any special hardware or time synchronization algorithms in the wireless network. Consequently, existing systems can readily employ the proposed approach without significant structural and functional modifications. The proposed approach is validated via computer simulations as well as by experimental hardware implementations using USRP2 platform. The hardware experiment shows that our approach can achieve a percentage of correct detection around 98% in actual wireless environments.

Future vehicular communications systems are expected to utilize the vacant channels (white spaces) of the spectrum, otherwise allocated for specific designated use. One such candidate of white space comes from the TV broadcast band. In this demonstration, we will first present animated results of a TV spectrum measurement campaign along the entire portion of Interstate I-90 located in the US state of Massachusetts. Next, we will demonstrate a cyber-physical proof-of-concept lab implementation of our previously developed control and data channel assignment schemes for vehicle-to-vehicle communications over (TV) white space. Finally we will show a video of actual vehicle to vehicle communications field tests conducted in Japan using TV white space.

Previous research on the security of network coding focuses on the detection of pollution attacks. The capabilities of network coding to detect malicious attacks have not been fully explored. We propose a new mechanism based on physical layer network coding to detect the Sybil nodes. When two signal sequences collide at the receiver, the starting point of the collision is determined by the distances between the receiver and the senders. When the distance between two receivers is large enough, they can combine their interference sequences to recover the original data packets. On the contrary, the Sybil nodes attached to the same physical device cannot accomplish the data recovery procedure. We have proposed several schemes at both physical and network layers to transform the idea into a practical approach. The investigation shows that the wireless nodes can effectively detect Sybil nodes without the adoption of special hardware or time synchronization.

Previous research on physical layer network coding (PNC) focuses on the improvements in bandwidth usage efficiency. In this paper, we propose a PNC-based node localization mechanism. When two signal sequences collide at the receiver, the starting point of collision is determined by the distances between the receiver and senders. When the signal interference results from two receivers are combined together, we can determine a hyperbola with two senders as the respective focal points. In this way, by using multiple pairs of anchor nodes as senders, we can determine multiple hyperbolas and the node position will be at the intersection point of these hyperbolas. The proposed approach does not require the wireless nodes to be equipped with any special hardware such as synchronized clocks. We propose several schemes at the physical and network layers to transform the idea into a practical approach. We also investigate the overhead, localization accuracy, and safety of the approach.

In this paper, we present a channel state information (CSI) feedback reduction algorithm to be used in an adaptive orthogonal frequency division multiplexing (OFDM) system. Adaptive OFDM transceivers, which offer improved data rates and power efficiency over conventional OFDM systems, require a duplex connection between the transmitter and receiver such that the CSI necessary for the transmitter to recalculate the optimal bit and power distributions can be fed back. When a large number of subcarriers are employed, the amount of CSI to be transmitted in the reverse link can consume a prohibitively large amount of bandwidth. In order for adaptive OFDM systems to be implemented in practice, this feedback data has to be reduced. Existing methods of reducing CSI feedback entail compressing the bit and power distributions separately, resulting in the transmission of redundant information due to the correlation between the two distributions. The proposed algorithm removes this redundancy and improves upon an existing feedback compression algorithm that both reduces the number of feedback transmissions necessary to maintain some desired bit error rate (BER) and compresses the CSI by feeding back subchannel gains rather than the bit and power distributions themselves. After applying the proposed algorithm to an adaptive OFDM system with parameters characteristic of wireless local area networks (WLANs), the results show that the number of feedback transmissions can be reduced by up to 90% for slowly varying channels and the CSI to be fed back can be compressed by more than a factor of three.

In this paper, we devised a novel indoor navigation system based on a probabilistic approach that employs data from the wireless adapter, accelerometer, and compass of the mobile device in order to determine user position. A routing algorithm employed by the device calculates the optimal path between user position and its destination. Experimental results verified that two meter accuracy was achieved by the proposed design. This technique shows promise for future handheld indoor navigation systems that can be used in malls, museums, hospitals, and college campuses.

In this paper, we propose a novel spectrum occupancy model designed to generate accurate temporal and frequency behavior of various wireless transmissions. Our proposed work builds upon existing concepts in open literature in order to develop a more accurate time-varying spectrum occupancy model. This model can be employed by wireless researchers for evaluating new wireless communication and networking algorithms and techniques designed to perform dynamic spectrum access (DSA). Using statistical characteristics extracted from actual radio frequency measurements, first- and second-order parameters are employed in a statistical spectrum occupancy model based on a combination of several different probability density functions (PDFs) defining various features of a specific spectrum band with several concurrent transmissions. To assess the accuracy of the model, the output characteristics of the proposed spectrum occupancy model are compared with realtime radio frequency measurements in the television and paging bands.

In this paper, we provide a new perspective for analyzing spectrum occupancy by introduced an M/M/l queueing model to generate accurate temporal and frequency behavior of various wireless transmissions. Our proposed research builds upon existing concepts in the open literature in order to develop a more accurate time-varying spectrum occupancy model. This model can be employed by wireless researchers for evaluating new wireless communication and networking algorithms and techniques designed to perform dynamic spectrum access (DSA). Using statistical characteristics extracted from actual radio frequency measurements, first- and second-order parameters are employed in a statistical spectrum occupancy model based on a combination of several different probability density functions (PDFs) defining various features of a specific spectrum band with several concurrent transmissions. To assess the accuracy of the model, the output characteristics of the proposed spectrum occupancy model are compared with real-time radio frequency measurements in the paging and ISM bands.

Dynamic spectrum access networks are expected to operate in a manner that is transparent to the incumbent licensed spectrum users. Conventional non-adaptive wideband spectrum sensing approaches could potentially be inefficient since they employ the same scanning resolution to track signals, even though different signals require spectrum scans with different sensing parameters. In this paper, we present an adaptive spectrum sensing framework that varies its parameters according to the characteristics of the spectrum of interest. Furthermore, we propose a dynamic scheduling algorithm for spectrum sensing that can be implemented in both a conservative and non-conservative manner. This algorithm allocates different time resolutions to several portions of spectrum using a backoff mechanism. The proposed algorithm has been employed to perform the 'listenbefore- talk' function of a cognitive radio, and performance results have been obtained with respect to the improvement in the sensing efficiency over a non-adaptive approach, secondary spectrum utilization, and interference rate.

In this paper, we present a novel algorithm for reducing sidelobe interference power levels in OFDM-based cognitive radios. Existing techniques for sidelobe suppression can be computationally intensive when determining the complex-valued amplitude levels for the cancellation subcarriers. Exploiting the fact that different sequences have different sidelobe power levels, the proposed algorithm employs a constellation expansion-based iterative approach in order to suppress the sidelobe power levels. An important advantage of the proposed technique is that, no side information needs to be transmitted. Simulation results show that the proposed algorithm can be employed in a wide range of operating conditions at the cost of a slight increase in the bit error rate and the peak-to-average power ratio characteristics.

In this paper, we present a novel peak-to-average power ratio (PAPR) reduction algorithm for OFDM systems employing a symbol balancing approach with several non-data bearing (NDB) subcarriers. The proposed algorithm assigns the NDB subcarriers values that, when combined with interleaving, reduces the long correlation patterns of the input frame that can potentially lead to high PAPR. Furthermore, the proposed algorithm can use either a fixed number of NDB subcarriers or a variable number of NDB subcarriers according to the amount of inter-subcarrier correlation, thus attempting to increase the data throughput of the system. Simulation results for a 256-subcarrier OFDM system employing QPSK symbols and 16 interleavers can achieve a PAPR reduction of 4 dB for a complementary cumulative distribution of 0.01%.

In this paper, we present a quantitative comparison of two agile modulation techniques employed by cognitive radio transceivers operating in a dynamic spectrum access (DSA) network. One of the modulation technique is non-contiguous orthogonal frequency division multiplexing (NC-OFDM), which is designed to avoid interference with the transmissions of incumbent users by deactivating subcarriers within their vicinity. The other modulation technique under study is a variant of multicarrier code division multiple access (MC-CDMA). Although several studies comparing conventional OFDM and MC-CDMA has been conducted in literature to justify robust error performance of MC-CDMA, a quantitative performance evaluation of these schemes has not been performed when employed in a DSA network. Due to deactivated subcarriers in DSA networks, in this paper we showed their performance can be significantly different from the conventional setup. Analytical expressions for the error probability of an NC-OFDM transceiver have been derived and compared with computer simulation results. The results show that the error robustness of NC-OFDM is relatively constant regardless of the number of deactivated subcarriers, unlike MC-CDMA transmissions, whose error performance degrades with an increase in deactivated subcarriers. 1

In this paper, we propose a novel channel sounding technique, called the multicarrier direct sequence swept time delay cross-correlation (MC-DS-STDCC), which is designed to minimize the interference with incumbent licensed transmissions within the context of a dynamic spectrum access (DSA) network. Conventional channel sounders employ the same transmit power level when operating over a specific frequency band. However, the maximum-tolerable transmit power levels permitted across the spectrum within a DSA network can potentially be non-uniform. Our proposed channel sounder is designed to tailor the transmit power level across the frequency band of operation, limiting it to the constraints imposed by both licensed and unlicensed devices, as well as by regulatory agencies. Employing a combination of multicarrier spread spectrum modulation and a conventional channel sounding approach, our proposed technique achieves a mean squared error of the estimated channel impulse response of 10-3at an SNR of -13 dB and -17 dB respectively for 15- and 31-chip spreading sequences.

In this paper we present the design process of an orthogonal frequency division multiplexing (OFDM) implementation for the Kansas University Agile Radio (KUAR). The KUAR is a portable, experimental, FPGA-based software-defined radio unit employed as a test bed to facilitate advanced research into frequency agile and cognitive radios. The baseband processing in this implementation is accomplished entirely on a Xilinx Virtex-II Pro FPGA that is built into the KUAR Our OFDM PHY implementation demonstrates the capabilities of the KUAR and serves as an important first step towards conducting actual experiments on multicarrier schemes for dynamic spectrum access communications, such as non-contiguous (NC)-OFDM.

In this paper, we present a statistical analysis of the peak-to-average power ratio (PAPR) for non-contiguous orthogonal frequency division multiplexing (NC-OFDM) signals. When studying contiguous OFDM signals, most PAPR analysis techniques assume the symbols to be identically and independently distributed (i.i.d.). However, in an NC-OFDM transmission, where a large number of subcarriers could be deactivated, this assumption is no longer valid. The proposed PAPR analysis is derived specifically for the NC-OFDM transmission scenario. Results show that NC-OFDM signal exhibit higher PAPR values relative to contiguous OFDM transmission at the same information rate.

In this paper, a novel loading algorithm consisting of four variants is proposed for a multicarrier transceiver employing multiple antennas configured for spatial diversity. The primary objective of the proposed algorithm is to increase the overall throughput while ensuring the mean bit error rate (BER) is below a specified limit. To achieve this, spatial diversity is employed to improve the subcarrier signal-to-noise ratio (SNR) values. Simultaneously, (uniform or non-uniform) bit allocation, which is a function of subcarrier SNR, is performed to increase throughput. To reduce power consumption, spatial processing complexity, and hardware costs, antenna subset selection is also performed by the proposed algorithm to choose a set of active transmit/receive antennas. The results show that combining bit allocation with spatial diversity (employing antenna subset selection) can yield substantial throughput increases.

Abstract In this paper, we present a proof‐of‐concept, fixed‐point, DSP hardware implementation of an adaptive bit loading algorithm that is designed for wireless multicarrier transceivers. Adaptive bit loading is used to enhance the performance of multicarrier transceivers by tailoring the subcarrier signal constellations to the channel conditions, which can vary across the subcarriers. Since most bit loading algorithms possess a high computational cost and are unable to cope with rapid variations of wireless channels, they are seldom used in present wireless standards. To prove that adaptive bit loading is feasible for wireless transceivers, our work focuses on the implementation of a known bit loading algorithm that can quickly search for the final bit allocation in an iterative manner. The goal of this algorithm is to yield the largest‐possible throughput while satisfying a mean BER constraint. The performance of the hardware implementation operating in time‐varying channel conditions is studied in terms of the overall throughput. Furthermore, the robustness of the hardware implementation is evaluated, relative to sudden changes in the channel that interrupts the run of the algorithm. Real‐time operations and fixed‐point representation issues are included in the discussion. Additionally, we propose a modified algorithm implementation that is more robust to channel variations. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract This paper presents a genetic‐algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems. Determining the appropriate radio parameters, given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems. Genetic algorithms (GAs) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal. Although there have been implementations of GA‐based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail. Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single‐carrier systems neglect the practical issues of multiple carriers. A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived. The performance analysis results illustrate the trade‐offs between the convergence time of the GA and the size of the GA search space. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper, we present a novel low complexity algorithm for reducing the peak-to-average power ratio (PAPR) occurring in OFDM-based cognitive radios. Although several PAPR reduction algorithms exist in the literature, they are often only effective for specific scenarios. Our proposed algorithm exploits the agility of cognitive radio technology to rapidly choose and employ the appropriate PAPR reduction approach from a set of approaches to achieve a large decrease in PAPR, given the current operating conditions. The results show that for a wide range of operating conditions, the proposed algorithm achieves a large decrease in PAPR, unlikely the PAPR results when only a single reduction approach is employed across the same wide range.

In this paper, we evaluate a cognitive radio transceiver employing both non-contiguous multicarrier modulation (NC-MCM) and adaptive bit allocation. Although NC-MCM and bit allocation have potential benefits with respect to enabling dynamic spectrum access (DSA) and increasing throughput, they also require the transmission of overhead information between the transmitter and the receiver. To reduce this overhead information, operating parameters can be assigned to a block of subcarriers, at the cost of some throughput. The trade-offs between subcarrier block size and two different bit allocation approaches for several DSA scenarios are assessed in this work. The results show that as percentage of available spectrum decreases, the throughput loss of systems employing larger subcarrier block sizes rapidly increases. Nevertheless, larger block sizes also yield greater reductions in transmission overhead.

In this paper, two novel algorithms are presented that simultaneously perform bit allocation and antenna subset selection for multicarrier spatial diversity transceivers. The goal of the bit allocation is to increase overall system throughput while ensuring that the system remains below a specified error rate. One of the proposed algorithms uses the same signal constellation across all subcarriers, while the other varies the signal constellation. To reduce hardware costs, power consumption, and complexity, the proposed algorithms also employ antenna subset selection and a reduced set of radio frequency (RF) chains. However, unlike previously published algorithms, the proposed algorithms choose array configurations that vary across the subcarriers, yielding even further increases in the overall throughput. The results show that the proposed algorithms employing both multiple antennas and bit allocation will increase the overall throughput of the system. Furthermore, the reduced hardware costs due to a smaller number of RF chains is achieved with a negligible throughput penalty.

Adaptive bit loading, a technique which is essential for taking full advantage of multicarrier modulation (MCM), has been shown to yield significant performance improvement, especially in the case of wireless MCM. However, because of its high computational cost and the rapid variations of wireless channels, it is seldom used in present wireless standards. In this paper, we present a proof of concept implementation of an adaptive bit loading algorithm, intended for a wireless MCM system, onto a fixed-point DSP. The algorithm considered is iterative in nature and aims to maximize the throughput under a BER constraint. The performance of the implementation is studied in terms of throughput given time varying conditions. Further, the implementation’s robustness is evaluated, relative to sudden changes in the channel that interrupts the algorithm’s run. Issues such as real-time operations and fixed-point representation are included in the discussion. Additionally, a modified implementation, more robust to channel variations is proposed.

We present discrete adaptive bit loading algorithms for multicarrier systems with uniform (nonadaptive) power allocation operating in a frequency selective fading environment. The algorithms try to maximize the overall throughput of the system while guaranteeing that the mean bit error rate (BER) remains below a prescribed threshold. We also study the impact of imperfect subcarrier signal-to-noise ratio information on throughput performance. Results show that the proposed algorithms have approximately the same throughput and mean BER as the optimal allocation while having a significantly lower computational complexity relative to other algorithms with near-optimal allocations. Moreover, when compared with algorithms that employ approximations to water filling, the computational complexity is comparable while the overall throughput is closer to the optimum.

In this paper, we evaluate and compare the robustness of several adaptive bit loading algorithms for multicarrier transmission systems, when imperfect subcarrier signal-to-noise ratio (SNR) information is used. In particular, we investigate the impact of the uncertainty of data-aided channel estimation techniques on system performance. We also examine an implementation issue associated with adaptive bit loading algorithms that use metrics related to the SNR. Although such metrics can be derived via closed form expressions, look-up tables are used instead to reduce system complexity, resulting in the SNR values being quantized. Thus, we examine the effects of SNR quantization on system performance. Finally, we present a technique for choosing SNR values in a fixed length look-up table in order to minimize quantization error.

We present an efficient bit allocation algorithm for multicarrier systems operating in frequency-selective environment. The proposed algorithm strives to maximize the overall throughput while guaranteeing that the mean bit error rate (BER) remains below a prescribed threshold. The algorithm is compared with several other algorithms found in literature in terms of the overall throughput, mean BER, and relative computational complexity. Furthermore, the algorithms are compared with an exhaustive search routine to determine the optimal bit allocation in terms of maximizing throughput given the constraint on error performance. No power allocation is performed by the algorithms. Results show that the proposed algorithm has approximately the same throughput and mean BER as the optimal solution while possessing a significantly lower computational complexity relative to the other algorithms with similar performance. When compared to algorithms which employ approximations to waterfilling, the computational complexity is comparable while the overall throughput is closer to the optimum.