Regulators have urged companies to enhance the informativeness of cybersecurity risk disclosures in financial statements. However, little is known about how the perceived attributes of cybersecurity risk disclosures may influence various stakeholders’ attitudes and behavioral intentions. Drawing on the Belief Reinforcement Model (BRM) and Elaboration Likelihood Model (ELM), this study examines how privacy concern moderates the role of the perceived presence of specificity and the belief about the verifiability of cybersecurity risk disclosures on trust violation and the behavioral intentions of different stakeholders (i.e. users, investors, and employees). The evidence from our experiment suggests that the perceived presence of specific disclosure elements and the belief about disclosure verifiability influences behavioral intentions for all three stakeholders through beliefs and attitudes. Further, those influences vary depending on the level of individuals’ privacy concerns.

In this paper we investigate the effects of connected automated vehicles on traffic patterns. We first experimentally study traffic patterns using two connected human-driven vehicles, which are equipped with vehicle-to-vehicle (V2V) communication, and a connected automated vehicle, which is able to respond to V2V information and control its longitudinal motion. Our experimental results indicate the long-range feedback may benefit traffic flow and that car-following models with delay are able to replicate the experimental results. The data fitted models are used in simulations for a 100-car network to study traffic dynamics with partial penetration of connected automated vehicles.

There is limited research providing guidelines to CEOs, particularly female CEOs, about the efficacy of different responses (i.e. apology and denial) in the aftermath of an insider privacy breach incident. The study synthesizes a multitude of theories under three categories male-gender bias, same-gender bias and opposite-gender bias and uses them along with organizational justice theory to investigate the moderating role of the interaction between CEO gender and the user gender on trust restoration process after an insider breach incident. Trust restoration process is compared across different trust beliefs—ability, benevolence, and integrity. The findings show that the CEO-user gender interaction significantly shapes the trust restoration process. The findings also show that the forces that shape the initial trust work differently for trust restoration and are shaped by the underlying CEO gender—user gender combination. The research has several theoretical, managerial and social implications, and enlists several future research ideas.

Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on perceiving geometry; however, they usually ignore the coherence of objects and perform poorly under scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make depth and flow predictions consistent with objects and robust to low lighting conditions. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction. Our code will be made available at https://github.com/mengyuest/SIGNet

By providing information about the objects that are non-line of sight and/or beyond the detection range of the local sensors, inter-vehicle communication compensates for the limitations of vehicle tracking subsystem in automated driving systems that relies on on-board sensing devices. Tracking capability in such systems can further be improved by making optimal use of the communication channel through sharing of locally created map data instead of transmitting only beacon messages. Message length adaptation, together with transmit rate control can address the scalability issue inherent in the vehicular network. The content of the exchanged information is another important aspect that has significant impact on the map accuracy in cooperative driving systems. In this paper, we study different congestion and content control schemes for a communication architecture aimed at map sharing, and evaluate their performance in terms of a situational awareness metric, namely position tracking error. This paper determines that message content should be concentrated on mapped objects that are located farther away from the sender, but near the edge of local sensor range. This paper also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system.

Currently, many automated vehicle systems primarily perceive the environment from a single perspective and as a result are unable to leverage additional scene information from the viewpoint of other vehicles on the road using vehicleto-vehicle communication technologies. We study how increased data sharing can improve the perception capabilities of automated vehicles. Our methodology shares sensor measurements and objects detected by state-of-the-art deep learning networks between vehicles to increase the automated driving systems confidence of detecting objects using a 3D sensor fusion algorithm. This approach can benefit scenarios where an object may be occluded (fully or partially) or located too far away to classify accurately by a single vehicle alone.

Reliable and timely delivery of periodic V2V (vehicle-to-vehicle) broadcast messages is essential for realizing the benefits of connected vehicles. Existing MAC protocols for ad hoc networks fall short of meeting these requirements. In this paper, we present, CoReCast, the first collision embracing protocol for vehicular networks. CoReCast provides high reliability and low delay by leveraging two unique opportunities: no strict constraint on energy consumption, and availability of GPS clocks to achieve near-perfect time and frequency synchronization.

In this paper, we develop a framework for using the dedicated short range communication (DSRC) waveform for the purposes of automotive radar. Our approach operates on the frequency domain channel estimates generated by the OFDM physical layer used in DSRC. We consider a two path channel model, with the first cluster corresponding to direct signal interference and the second cluster corresponding to the signal reflected from the target. The target ranging and direction of arrival information is encoded in the parameters of the reflected path. We estimate the parameters of the direct and reflected path using the least squares matching pursuit algorithm by exploiting their relative power difference. The performance of the algorithm is evaluated through numerical simulations assuming low power omnidirectional 5 dBi antennas, Swerling type 0 and type 3 target models, 10 MHz transmission bandwidth and different analog-to-digital quantization resolutions. Simulations results show submeter accuracy in location estimation for a significant range of target distances.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Instantly decodable network codes (IDNCs) are suggested in the literature to mitigate the problem of decoding delay in network coding. IDNC is also suggested for broadcast scenarios, where the goal is to maximize the number of decoded packets by the receivers, e.g., in multimedia broadcast. It is shown that after uncoded transmission of all the packets, one coded packet can be instantly decodable by a large number of users, when the transmitter is sighted, i.e., it has a perfect knowledge of the lost packets by each receiver. We introduce and study blind IDNC for broadcast, where the transmitter has no knowledge of the lost packets. Similar to the sighted IDNC, first all data packets are transmitted uncoded. Then, assuming the same erasure rate for all users, we allow a small number of coded packets (one, two or three), and study how these coded packets should be constructed to recover as many lost packets at the receivers as possible. The optimal solutions when one blind packet or two non-overlapping blind packets are transmitted are found. We see that two blind transmissions have comparable performance to a single optimal sighted transmission. Moreover, we prove that three blind transmissions outperform any single sighted transmission.

By providing information about the objects that are non-line of sight and/or beyond the detection range of the local sensors, inter-vehicle communication compensates for the limitations of vehicle tracking subsystem in automated driving systems that relies on on-board sensing devices. Tracking capability in such systems can further be improved by making optimal use of the communication channel through sharing of locally created map data instead of transmitting only beacon messages. While map data are collected from both sensor and communicated information, exchanging a subset of the local map requires some sort of content control scheme in place to ensure that useful information is being exchanged among vehicles that would enhance tracking accuracy. This paper investigates how information sharing based on proximity of mapped objects impact the position tracking capability. It also compares deterministic and probabilistic variants of the distance based content control approaches. Preliminary results show that exchanging information about objects located near the edge of sensor range increases the tracking performance. It is also shown that the probabilistic approaches have higher mapping accuracy than their deterministic counterparts.

Understanding the surrounding environment including both still and moving objects is crucial to the design and optimization of intelligent vehicles. Knowledge about the vehicle environment could facilitate reliable detection of moving objects, especially irregular events (e.g., pedestrians crossing the road, vehicles making sudden lane changes,) for the purpose of avoiding collisions. Inspired by the analogy between point cloud and video data, we propose to formulate a problem of reconstructing the vehicle environment (e.g., terrains and buildings) from a sequence of point cloud sets. Built upon existing point cloud registration tool such as iterated closest point (ICP), we have developed an expectation-maximization (EM)-ICP technique that can automatically mosaic multiple point cloud sets into a larger one characterizing the still environment surrounding the vehicle. Moreover, we propose to address the issue of irregularity detection from the extracted moving objects. Our experimental results have shown successful reconstruction of a variety of challenging vehicle environments (including rural and urban, road and intersection, etc.) and simultaneous tracking/segmentation of multiple moving objects.

Millimeter wave (mmWave) could be a key technology to support high data rate demands for automated vehicles. MmWave needs array gain for the best performance, but this requires correctly pointing the beam, known as beam alignment. Dynamic blockages make beam alignment challenging in the vehicular setting. This paper proposes to leverage a vehicle's position along with past beam measurements to rank desirable pointing directions that can reduce the required beam training to a small set of pointing directions. The ranking is conducted using a learning-to-rank approach, which is a popular machine learning method used in recommender systems. The learning uses a kernel based model, and a new metric for evaluating ranked lists of pointing directions tailored to beam alignment is proposed. The proposed method provides a scalable framework for exploiting context information.

Even though privacy concerns (PC) and security concerns (SC) are acknowledged as separate constructs, many studies argue that they are related or, even worse, confused. Building upon the non-overlapping SC and PC dimensions, we propose an Internet Users Information Transmission Security Concern (IUITSC) scale. We argue that the scale when used in conjunction with the PC scale would cover the entire gamut of SCs and PCs (SC–PC). The construct is tested in a nomological network of personality and trust. The article first develops the theoretical foundation for multiple factor structures including various first-order factor models as well as the second-order factor model. The results, based on extensive statistical tests and nomological examination, suggest that the IUITSC may be represented as a second-order construct comprising of authentication, confidentiality, integrity, and non-repudiation. We argue that information assurance is the common theme that ties these constructs into a higher order IUITSC construct.

Beam alignment is a source of overhead in mobile millimeter wave communication systems due to the need for frequent repointing. Beam switching architectures can reduce the amount of repointing required by leveraging position prediction. This paper presents an optimization of beam design in terms of rate. We consider a non-congested two-lane highway scenario where road side units are installed on lighting poles. Under this scenario, line-of-sight to the road side unit is very likely and vehicle speed does not vary much. We formulate and solve numerically using a gradient descent method for an optimal beam design to maximize the data rate for non-overlap beams. The result shows close performance to the equal coverage beam design. We study the effect of the overlap on the average rate and outage and compare the equal coverage with the equal beamwidth design. Numerical examples show that the equal coverage design can achieve up to 1.5× the rate of the equal beamwidth design confirming the importance of the choice of beam design.

Channel congestion is one of the major challenges for IEEE 802.11p-based vehicular ad hoc networks. To tackle the challenge, several algorithms have been proposed and some of them are being considered for standardization. Situations could arise where vehicles with different algorithms operate in the same network. Our previous work has investigated the performance of a mixed-algorithm vehicular network for the CAM-DCC and LIMERIC algorithms and identified that the CAM-DCC vehicles could potentially experience a performance degradation after introducing the LIMERIC vehicles into the network. In this work, we study whether it is possible to eliminate or bound this degradation. We propose a CBP target adjustment mechanism which controls the CBP target of LIMERIC vehicles according to vehicle density and mixing situation of the two algorithms in the network to limit the performance degradation of CAM-DCC vehicles to a desired level. The proposed mechanism is evaluated via both MATLAB and ns-2 simulations and the simulation results indicate that the performance degradation of the CAM-DCC vehicles is controlled as expected with only negligible impact on the performance of LIMERIC vehicles, which still perform similar or better than CAM-DCC vehicles.

Channel congestion is one of the major challenges for IEEE 802.11p-based vehicular networks. Unless controlled, congestion increases with vehicle density, leading to high packet loss and degraded safety application performance. We study two classes of congestion control algorithms, i.e., reactive state-based and linear adaptive. In this paper, the reactive state-based approach is represented by the decentralized congestion control framework defined in the European Telecommunications Standards Institute. The linear adaptive approach is represented by the LInear MEssage Rate Integrated Control (LIMERIC) algorithm. Both approaches control safety message transmissions as a function of channel load [i.e., channel busy percentage (CBP)]. A reactive state-based approach uses CBP directly, defining an appropriate transmission behavior for each CBP value, e.g., via a table lookup. By contrast, a linear adaptive approach identifies the transmission behavior that drives CBP toward a target channel load. Little is known about the relative performance of these approaches and any existing comparison is limited by incomplete implementations or stability anomalies. To address this, this paper makes three main contributions. First, we study and compare the two aforementioned approaches in terms of channel stability and show that the reactive state-based approach can be subject to major oscillation. Second, we identify the root causes and introduce stable reactive algorithms. Finally, we compare the performance of the stable reactive approach with the linear adaptive approach and the legacy IEEE 802.11p. It is shown that the linear adaptive approach still achieves a higher message throughput for any given vehicle density for the defined performance metrics.

Channel congestion is one of the major challenges for IEEE 802.11p-based vehicular ad hoc networks. Facing the challenge, several algorithms have been proposed. Two good representatives are the Decentralized Congestion Control (DCC) framework defined by ETSI and LIMERIC, a linear control algorithm. Both algorithms control message transmission rate as a function of channel load (i.e., channel busy percentage, CBP). In this work, DCC is assumed to be deployed for day one applications and LIMERIC is introduced into the network afterwards. Given such a mixed vehicular network with vehicles running either DCC or LIMERIC, we evaluate the performance of the two algorithms in the mixed scenario and study the impact of such mixed network operation on the performance through ns-2 simulations. It is observed that converting some CAM-DCC vehicles to LIMERIC vehicles will not lead to any significant performance degradation of either CAM-DCC or LIMERIC vehicles. Any observed performance degradation is small enough so that we would expect typical DSRC applications to still be feasible. Performance differences between DCC and LIMERIC exist but they can be reduced through careful selection of the specific parameters of LIMERIC and DCC, respectively.

Privacy policy statements and privacy assurance cues are among the most important website features that online providers use to increase individuals’ trust and willingness to disclose private information online. The focus of this study is a comprehensive examination of the process by which privacy assurance mechanisms influence trust and the moderating role of privacy concern in this process. We use the lens of the Elaboration Likelihood Model to investigate the way different individuals perceive and process privacy assurance mechanisms. We argue that the trust-enhancing role of these mechanisms depends on the individual’s privacy concern. The results of this study articulate the process by which various privacy assurance mechanisms operate in enhancing an individual’s trust, and show that there are distinct behavioral differences between individuals with high- vs low-privacy concern when forming their trust to disclose private information. The paper sheds new light on the role of elaboration in the trust building process, and shows why privacy assurance mechanisms have different impacts depending on individuals’ privacy concerns.

This study examines the role individuals' frugality plays in moderating the impact of privacy and security concerns on trust-discount tradeoff. Trust-discount tradeoff measures a user's intention on a continuum to transact with a higher-trust, higher-premium website as opposed to lower-trust, higher-discount website. The hypotheses are examined in three different contexts: e-commerce, health and finance. The findings suggest that frugality does moderate the impact of privacy and security concerns on trust-discount tradeoff in all three contexts, such that security concern is positively associated with preference for higher-trust, higher-premium websites for individuals with a high level of frugality, and that privacy concern is positively associated with preference for higher-trust, higher-premium websites for individuals with a low level of frugality. However, the subsequent analysis reveals that the reasons for the above moderating impact differ depending upon the underlying contextual sensitivity. This study is one of the first to provide contextual examination of the role of frugality, privacy and security concerns in trust-discount tradeoff.

In this letter, we investigate power allocation schemes for OFDM-based cognitive radio (CR) systems while taking errors due to imperfect sensing of primary user (PU) into account. The interference is introduced into PU band by CR user not only because of co-existence in side-by-side bands but also by missed detection due to imperfect sensing. We propose an optimal scheme such that the transmission capacity of CR users is maximized while keeping the total interference within a prescribed threshold and keeping the total power within a budget. As the optimal scheme can have a high complexity, low complexity suboptimal schemes have also been proposed. The performance of the proposed schemes is compared to the schemes that do not take sensing errors into consideration. Presented numerical results demonstrate the strength of our proposed schemes.

One of the most important challenges for DSRC deployment is maintaining high performance under heavy channel load. The study of congestion control mechanisms will be impractical if it requires hundreds of physical radios deployed on vehicles. In this paper we demonstrate two important results that together suggest a strategy for investigating congestion control. First, we show that the NS-2 simulator accurately models the MAC and PHY associated with an IEEE 802.11 hardware implementation. This opens the door to using simulation as the primary investigative tool. Second, we show that a technique in which one radio emulates N vehicles can produce results quite similar to the case in which a larger number of distinct vehicles exist. This will allow selective validation tests with tens of physical radios emulating hundreds of vehicles. The means by which we demonstrate these results is a cross-validation technique. We show that three test approaches produce consistently similar results: physical radios with emulation, NS-2 with emulation, and NS-2 without emulation. The close agreement among these results validates both the radio emulation technique and the NS-2 implementation of the 802.11 MAC and PHY protocols. We use this cross-validation technique to compare results for a wide variety of transmit power, message rate, data rate, channel bandwidth, message size and EDCA settings.

In this letter, we develop an optimal power allocation algorithm for the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with different statistical interference constraints imposed by different primary users (PUs). Given the fact that the interference constraints are met in a statistical manner, the CR transmitter does not require the instantaneous channel quality feedback from the PU receivers. A suboptimal algorithm with reduced complexity has been proposed and the performance has been investigated. Presented numerical results show that with our proposed optimal power allocation algorithm CR user can achieve significantly higher transmission capacity for given statistical interference constraints and a given power budget compared to the classical power allocation algorithms namely, uniform and water-filling power allocation algorithms. The suboptimal algorithm outperforms both water-filling algorithm and uniform power loading algorithm. The proposed suboptimal algorithm give an option of using a low complexity power allocation algorithm where complexity is an issue with a certain amount of transmission rate degradation.

In this letter, we investigate the relay and power allocation problem for OFDM-based cognitive radio (CR) systems with single antennae. We propose a method where the capacity of CR user employing relays is maximized while total transmission power is kept within a budget and the interference introduced to the primary user (PU) band is kept within a prescribed threshold. The optimization problem is a mixed-integer problem, which is NP-hard. Hence in this letter, we have proposed three sub-optimal schemes. The presented numerical results show that the performance of proposed suboptimal schemes is close to the optimal solution.

In a cooperative relay-assisted communication system that uses rateless codes, packets get transmitted from a source to a destination at a rate that depends on instantaneous channel states of the wireless links between nodes. When multiple relays are present, the relay with the highest channel gain to the source is the first to successfully decode a packet from the source and forward it to the destination. Thus, the unique properties of rateless codes ensure that both rate adaptation and relay selection occur without the transmitting source or relays acquiring instantaneous channel knowledge. In this paper, we show that in such cooperative systems, buffering packets at relays significantly increases throughput. We develop a novel analysis of these systems that combines the communication-theoretic aspects of cooperation over fading channels with the queuing-theoretic aspects associated with buffering. Closed-form expressions are derived for the throughput and end-to-end delay for the general case in which the channels between various nodes are not statistically identical. Corresponding results are also derived for benchmark systems that either do not exploit spatial diversity or do not buffer packets. Altogether, our results show that buffering - a capability that will be commonly available in practical deployments of relays - amplifies the benefits of cooperation.

Relay selection combined with buffering of packets of relays can substantially increase the throughput of a cooperative network that uses rateless codes. However, buffering also increases the end-to-end delays due to the additional queuing delays at the relay nodes. In this paper we propose a novel method that exploits a unique property of rateless codes that enables a receiver to decode a packet from non-contiguous and unordered portions of the received signal. In it, each relay, depending on its queue length, ignores its received coded bits with a given probability. We show that this substantially reduces the end-to-end delays while retaining almost all of the throughput gain achieved by buffering. In effect, the method increases the odds that the packet is first decoded by a relay with a smaller queue. Thus, the queuing load is balanced across the relays and traded off with transmission times. We derive explicit necessary and sufficient conditions for the stability of this system when the various channels undergo fading. Despite encountering analytically intractable G/GI/1 queues in our system, we also gain insights about the method by analyzing a similar system with a simpler model for the relay-to-destination transmission times.

In this paper, we investigate joint overlay and underlay power allocation for OFDM-based cognitive radio (CR) systems. Unlike existing work in literature where power is allocated in either overlay only or underlay fashion, we propose schemes which perform a joint allocation. Specifically, the total capacity of CR is maximized while maintaining a total power budget and keeping the interference introduced to the primary user (PU) band below a threshold. As the complexity of optimal scheme can be high, a suboptimal scheme has also been proposed. Presented simulation results show that a significant gain in transmission capacity is achieved by using joint overlay and underlay allocation scheme as compared to either overlay or underlay only scheme. Further, numerical results show that even our proposed suboptimal scheme outperforms either overlay or underlay only scheme.

In this paper, we investigate joint sensing and power allocation schemes for OFDM-based cognitive radio (CR) systems. A CR system employing cooperative sensing based on energy detection is considered. Each primary user (PU) band is assigned a subset of sensors and equal gain combining is used at the access point to detect the presence of PU in that particular band. For such a system, we propose algorithms to jointly allocate sensing thresholds at the access point and power at the CR transmitters so that the throughput of the CR system is maximized while taking total transmit power, CR quality of service (QoS) constraints, and interference introduced to the PU band into consideration. The performance of the proposed schemes is compared to the schemes that only optimize either sensing thresholds or transmitted power. Presented numerical results demonstrate the strength of our proposed schemes.

Efficient and reliable subcarrier power allocation in orthogonal frequency-division multiplexing (OFDM)-based cognitive radio networks is a challenging problem. Traditional waterfilling approach is inefficient for such networks due to the strict requirements on the interference generated to the primary users (PUs). In this paper, we present a solution to an energy-efficient resource allocation problem which maximizes the cognitive radio (i.e., secondary) link capacity taking into account the availability of the subcarriers (and hence the reliability of transmission by cognitive radios) and the limits on total interference generated to the PUs. We consider an energy-aware capacity expression by taking into account another factor called subcarrier availability. Optimizing such an expression saves valuable resources such as battery life by selectively allocating power to underutilized subcarriers. Based on a risk-return model, we formulate a convex optimization problem which incorporates a linear average rate loss function in the optimization objective to include the effect of subcarrier availability. Due to the complex structure of the optimal solution, we propose three suboptimal schemes, namely, the step-ladder, nulling, and scaling schemes. We compare the performances of optimal and suboptimal algorithms with the performance of a classical waterfilling scheme. We conclude that waterfilling, unable to satisfy the interference criterion, performs the worst amongst all the schemes considered in this paper.

Cooperative communication using rateless codes, in which the source transmits an infinite number of parity bits to the destination until the receipt of an acknowledgment, has recently attracted considerable interest. It provides a natural and efficient mechanism for accumulating mutual information from multiple transmitting relays. We develop an analysis of queued cooperative relay systems that combines the communication-theoretic transmission aspects of cooperative communication using rateless codes over Rayleigh fading channels with the queuing-theoretic aspects associated with buffering messages at the relays. Relay cooperation combined with queuing reduces the message transmission times and also helps distribute the traffic load in the network, which improves throughput significantly.

In this paper, we investigate an optimal power loading algorithm for an OFDM-based cognitive radio (CR) system. The downlink transmission capacity of the CR user is thereby maximized, while the interference introduced to the primary user (PU) remains within a tolerable range. We also propose two suboptimal loading algorithms that are less complex. We also study the effect of a subcarrier nulling mechanism on the performance of the different algorithms under consideration. The performance of the optimal and suboptimal schemes is compared with the performance of the classical power loading algorithms, e.g., water-filling and uniform power but variable rate loading schemes that are used for conventional OFDMbased systems. Presented numerical results show that for a given interference threshold, the proposed optimal scheme allows CR base station (BS) to transmit more power in order to achieve a higher transmission rate than the classical loading algorithms. These results also show that although the proposed suboptimal schemes have certain degradation in performance compared to the optimal scheme, they outperform the classical loading algorithms. We also present numerical results for nulling mechanism. Finally, we investigate the effect of imperfect channel gain information at the transmitter.

Cognitive radio (CR) technology is an innovative radio design philosophy which aims to increase spectrum utilization by exploiting unused spectrum in dynamically changing environments. Orthogonal frequency division multiplexing (OFDM) is a potential modulation technique for CR networks' air interface. In this paper, we study and explore optimal power loading algorithm for an OFDM-based CR system and the rate in each subcarrier is adjusted according to the power. As such the downlink capacity of the CR user is maximized while the interference introduced to the primary user remains within a tolerable range. We also propose two suboptimal loading algorithms that have less complexity. The performance of optimal and suboptimal schemes are compared with the performance of classical power loading algorithms that are used for conventional OFDM-based systems e.g., water-filling and uniform power but variable rate loading schemes. Presented numerical results show that for a given interference threshold the proposed optimal scheme allows the CR users to transmit more power in order to achieve higher transmission rate than the classical loading algorithms. These results also show that the proposed suboptimal schemes offer a performance close to the optimal scheme. Finally, we study the effect of subcarrier nulling mechanism on the performance of different algorithms under consideration.