This paper presents an innovative approach to mitigating the peak-to-average power ratio (PAPR). The proposed method uses a deep learning model called autoencoders (AEs) to simplify the process and avoid the complex calculations of traditional methods such as selective mapping (SLM). Unlike SLM, our approach does not need side information about the PAPR distribution. Through simulations of coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems, the AEbased model offers substantial enhancements in both PAPR reduction and bit error rate (BER) performance when compared to conventional techniques. An error-free transmission can be acheived with a reduction in PAPR exceeding 10 dB compared to the original signal and a 1 dB advantage over SLM. In particular, the AE model achieves the best BER performance of$2 \times 10^{-6}$at 44 dB OSNR, surpassing traditional methods. Furthermore, the model demonstrates robustness against noise and nonlinear distortions, making it appropriate for optical channels experiencing diverse levels of impairment. This innovative technique has the potential to revolutionize next-generation optical communication systems by enabling efficient and reliable data transmission.

In response to traffic congestion in urban areas, the National Aeronautics and Space Administration promotes the concept of Advanced Air Mobility (AAM), which envisages a safe and efficient air transportation system. However, the increased communication demands in AAM can exacerbate the spectrum scarcity issue, so a new communication paradigm is necessary. In this paper, we consider multiple aerial vehicles (AV) flying along their shortest paths for cargo/passenger delivery. During the flight, each AV must make decisions in every time slot on communication resource allocation and velocity selection under safety constraints. Accordingly, we formulate the joint optimization problem to minimize the weighted sum of the total travel time and communication outage time. The optimization problem is formulated as a Markov game and a multi-agent reinforcement learning based algorithm is proposed. Simulation results corroborate the effectiveness of the proposed solution.

With increasing mobility demands in metropolitan areas, the emerging concept of Urban Air Mobility (UAM) opens a new urban air transportation paradigm, where one big challenge is to ensure reliable two-way communications between aerial vehicles and their ground control stations for safe operations. The concept of cellular-based UAM (cUAM) provides a promising solution where aerial vehicles are regarded as new aerial users, sharing the cellular spectrum with existing terrestrial users. However, with new characteristics and demands of aerial users, the severity of spectrum scarcity becomes more prominent and new spectrum management solutions are needed. In this paper, we consider a typical cUAM scenario where multiple aerial vehicles transport passengers/cargo along their pre-defined paths, with the coexistence of terrestrial users. We assume the minimum communication Quality of Service (QoS) must be achieved for all users at all times. Our objective is to simultaneously minimize aerial users' mission completion time and maximize terrestrial users' achievable data rate by jointly optimizing the spectrum allocation for all users and the moving velocities of aerial users. We formulate the optimization problem as a multi-stage Markov Decision Process and propose a multi-agent deep reinforcement learning-based algorithm. We also propose a heuristic greedy algorithm and an orthogonal multiple access algorithm as baseline solutions. Simulation results show that our learning-based solution outperforms the baseline solutions under different network configurations.

As the proliferation of mobile devices has led to an ever-growing demand for wireless Internet services, the spectrum shortage issue becomes increasingly severe and spectrum sharing is regarded as a promising approach to addressing the spectrum shortage issue. In this paper, we propose a practical underlay spectrum sharing scheme for cognitive radio networks (CRNs) where the primary users are oblivious to the secondary users. The key components of our scheme are two MIMO-based interference cancellation (IC) techniques to handle cross-network interference on the secondary network side. The first one is a blind beamforming technique for secondary transmitters. This IC technique allows a secondary transmitter to nullify its generated interference for primary users without requiring channel state information (CSI). The second one is a blind interference cancellation (BIC) technique for secondary receivers. This IC technique enables a secondary receiver to decode its desired signal in the presence of strong unknown interference from primary transmitters. Based on these two MIMO-based IC techniques, we develop a MAC protocol for the secondary network to enable underlay spectrum sharing in CRNs. We have implemented the proposed underlay spectrum sharing scheme on a GNURadio-USRP2 wireless testbed. Experimental results show that the secondary users can achieve an average of 1 bit/s/Hz spectrum efficiency without degrading the performance of the primary users in a real-world office building environment.

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.

In wireless LANs (WLANs), network-wide time and frequency synchronization among user devices is widely regarded as a necessity for uplink MU-MIMO. Therefore, to enable uplink MU-MIMO in 802.11ax, dedicated MAC protocols (e.g., trigger frame and timing advance mechanism) have been proposed to synchronize user devices in the time and frequency domains. Such MAC protocols increase not only network complexity but also communication overhead. In this paper, we show that the time and frequency synchronization among user devices is not a necessity for uplink MU-MIMO. We propose a practical uplink MU-MIMO solution which does not require time and frequency alignments of the signals from user devices. The key component in our solution is a new PHY design for AP's receiver, which can decode the concurrent signals from multiple asynchronous user devices. We have built a prototype of our uplink MU-MIMO solution on USPR2-GNURadio testbed. Experimental results show that, using the new PHY, an M-antenna AP can successfully decode the concurrent signals from M asynchronous user devices (2 ≤ M ≤ 4).

In wireless multicast multimedia streaming, every data block (i.e., a group of packets) needs to be delivered within a hard deadline. On the other hand, to provide multilevel services, the widely adopted scalable video coding (SVC) prioritizes a video bit stream into a base layer and several enhancement layers. In this paper, we combine SVC with the transmission delay constraint and focus on multicasting hard deadline constrained prioritized (HDCP) data over wireless erasure channels. Specifically, we first define the performance metric and formulate the optimization problem. Then, we focus on a new adaptive RNC encoding and scheduling scheme to maximize the effective network throughput. Particularly, we propose simple and efficient greedy scheduling technique, which not only obtains near-optimal performance with low-time complexity but also suits time-varying fading channels. In addition, we extend our discussion to the more practical scenario with erroneous feedback and propose sampling-based feedback mechanism to reduce the system overhead. Both analytical and simulation results are provided to corroborate the effectiveness of the proposed techniques.

For physical-layer secrecy performance, existing studies only focus on coding schemes that can achieve the secrecy capacity. However, finite blocklength penalty could be fatal for security of private message. In this paper, we explore the impact of finite blocklength on secrecy for the wiretap channel model. We propose secrecy performance matrices for finite blocklength analysis and provide analytical expressions to evaluate the secrecy performance. We demonstrate trade-off between secrecy and reliability, and also provide an upper bound of secret information rate in finite blocklength case. The results are also applied to indicate asymptotic performance.

Cognitive radio and MIMO have drawn significant attention to achieve high spectrum utilization efficiency. On the other hand, increasing energy demand and soaring energy related operating cost call for new design of energy efficient communication networks. In this paper, we study the energy efficiency optimization in a cognitive radio MIMO network. Specifically, we propose both distributed and centralized energy efficiency optimization algorithms. Since the original fractional problems are nonconvex, we use Dinkelbach's method to transform them into parametric problem and solve the optimal solution iteratively. Simulation results show that, while the centralized algorithm outperforms the distributed algorithm in terms of network-wise energy efficiency, it may lose fairness among all CR links in high interference scenario.

Future wireless networks will face the dual challenge of supporting large traffic volumes while providing reliable service for delay-sensitive traffic. To meet the challenge, relay network has been introduced as a new network architecture for the fourth generation (4G) LTE-Advanced (LTE-A) networks. In this paper, we investigate resource allocation including subcarrier and power allocation for LTE-A relay networks under statistical quality of service (QoS) constraints. By dual decomposition, we derive the optimal subcarrier and power allocation strategies to maximize the effective capacity (EC) of the underlying LTE-A relay systems. Characteristics of optimal resource allocation strategies are identified, and a low-complexity suboptimal scheme is developed through optimizing the subcarrier and power allocation individually. Our result suggests that the optimal subcarrier and power allocation strategies depend heavily on the underlying QoS constraint. For example, in the low signal-to-interference-plus-noise (SINR) regime, when there are less stringent QoS constraints, base stations and relay stations tend to allocate all the power to the best available subcarrier. However, as QoS requirements become more stringent, both base stations and relay stations will spread their power over available subcarriers. On the other hand, in the high SINR regime, regardless of the QoS constraints, base stations and relay stations tend to equally allocate power among available subcarriers.

In this paper, a new network-transmission-based (NTB) algorithm is proposed for human activity recognition in videos. The proposed NTB algorithm models the entire scene as an error-free network. In this network, each node corresponds to a patch of the scene and each edge represents the activity correlation between the corresponding patches. Based on this network, we further model people in the scene as packages, while human activities can be modeled as the process of package transmission in the network. By analyzing these specific package transmission processes, various activities can be effectively detected. The implementation of our NTB algorithm into abnormal activity detection and group activity recognition are described in detail in this paper. Experimental results demonstrate the effectiveness of our proposed algorithm.

Future wireless communication networks will face the dual challenge of supporting large traffic volumes while providing reliable service for heterogeneous traffic types. In order to meet the ever increasing traffic demand, heterogeneous relay network is introduced as an enabling technology for the fourth generation (4G) mobile broadband networks. In this paper, we will investigate optimal power and subcarrier allocation strategies for heterogeneous relay networks under statistical quality of service (QoS) constraints. To be specific, we will characterize the effective capacity of a wireless relay system under QoS constraints. The properties of the optimal resource and subcarrier allocation strategies for delay-sensitive traffic over heterogeneous relay networks will also be identified. Two low complexity resource and subcarrier allocation algorithms will be introduced to optimize the effective capacity. Our results suggest that the optimal power and subcarrier allocation strategies depend heavily on the underlying QoS constraint. In the low signal-to-interference-plus-noise (SINR) regime, when there is no QoS constraint, both base stations and relay nodes will allocate all the transmit power to the best subcarrier. However, as the QoS requirement becomes more stringent, both base stations and relay nodes will spread their transmit power over multiple subcarriers.

In this paper, we consider a random wireless sensor network where the sensor nodes are distributed randomly and form clusters to cooperatively transmit data packets using cooperative multi-input-multi-output (CMIMO) technique. We focus on analyzing the overall system performance in terms of packet error rate (PER). And then propose a new node sleep strategy to minimize the overall energy consumption. We take the random distribution of sensor nodes into consideration and get a closed form of overall PER expression that simplifies the overall energy consumption optimization. We analyze the tradeoff between the overall energy consumption and sensor nodes sleep rate. The simulation results corroborate our analysis and show that the proposed sensor node sleep strategy has significant energy saving compared with other transmission schemes.

SUMMARY Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest‐end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the ‘cloud computing’ paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch‐based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN‐customized topologies and customized addressing protocols in the large‐scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright © 2012 John Wiley & Sons, Ltd.

This paper considers random wireless sensor networks where nodes are distributed randomly and form clusters to transmit the packets to relay clusters using cooperative multi-input-multi-output (CMIMO) technique. First, we study CMIMO energy consumption and divide it into intra-cluster energy consumption and inter-cluster energy consumption. Then we focus on analyzing the system performance in terms of overall packet error rate (PER) and energy consumption. Based on system performance metrics, we propose a new node sleep strategy, where overall energy consumption is minimized by coordinating inter-cluster transmitting energy consumption and the number of sensor nodes in activation. Compared to existing works, our contributions are (1) we consider random distribution of nodes and get a closed-form expression of overall PER that simplifies the energy minimization problem; (2) we analyze the tradeoff between overall energy consumption and nodes active rate. In simulation, we investigate the relationships among nodes active rate, inter-cluster transmitting energy consumption and the overall energy consumption. Furthermore, we discuss how these parameters are affected by the nodes density, the inter-cluster transmitting distance, the cluster radius and the intra-cluster energy consumption. Finally, we validate that the proposed nodes sleep strategy has significant energy savings compared with non-sleep cooperative transmission and direct transmission.

Wireless multicast for delay-sensitive data is challenging because of the heterogeneity effect where each receiver may experience different packet losses. Fortunately, network coding, a new advanced routing protocol, offers significant advantages over the traditional Automatic Repeat reQuest (ARQ) protocols in that it mitigates the need for retransmission and has the potential to approach the min-cut capacity. Network-coded multicast would be, however, vulnerable to false packet injection attacks, in which the adversary injects bogus packets to prevent receivers from correctly decoding the original data. Without a right defense in place, even a single bogus packet can completely change the decoding outcome. Existing solutions either incur high computation cost or cannot withstand high packet loss. In this paper, we propose a novel scheme to defend against false packet injection attacks on network-coded multicast for delay-sensitive data. Specifically, we propose an efficient authentication mechanism based on null space properties of coded packets, aiming to enable receivers to detect any bogus packets with high probability. We further design an adaptive scheduling algorithm based on the Markov Decision Processes (MDP) to maximize the number of authenticated packets received within a given time constraint. Both analytical and simulation results have been provided to demonstrate the efficacy and efficiency of our proposed scheme.

In this paper, the power optimization of the multicarrier cognitive system underlying the primary network is investigated. We consider the interference coupled cognitive network under individual secondary user's power constraint and primary user's rate constraint. A multicarrier discrete distributed (MCDD) algorithm based on Gibbs sampler is proposed. Although the problem is nonconcave, MCDD is proved to converge to the global optimal solution. To reduce the computational complexity and convergence time, the Gibbs sampler based Lagrangian algorithm (GSLA) is proposed to get a near optimal solution. We also provide simulation results to show the effectiveness of the proposed algorithms.

Network coding offers a promising platform for multicast transmission by approaching its min-cut capacity. However, pushing the network throughput toward this upper bound comes with a sacrifice in delivery delay due to the decoding procedure that requires performing batch of coded packets. Further, in some transmission scenarios where the receivers experience deep fading or unable to collect a full set of the transmitted data, no useful information is recovered. The effect is more severe in the networks where the transmitted information has priority structure with hard deadline constraint due to the limited delivery time and data interdependencies. In this paper, we consider single-hop wireless networks where the transmitter wishes to multicast hard deadline constrained prioritized data to many receivers over lossy channels. We first study the network performance of a variety of transmission techniques, depending on how the transmitter schedules transmission in each time slot. We then propose an adaptive encoding and scheduling technique to maximize the network throughput. To find the optimal transmission scheduling at the presence of the network dynamics, we cast the problem in the framework of Markov Decision Processes (MDP) and use backward induction method to find an optimal solution. We further propose simulation-based algorithm and greedy scheduling technique that obtain high performance with much lower time complexity. Both analytical and simulation results have been provided to corroborate the effectiveness of the proposed techniques.

In this paper, we propose an energy-efficient cooperative geographic routing (ECGR) to reach energy-efficient routing in wireless sensor networks(WSN). ECGR fully takes advantage of cooperative diversity and geographic routing. At physical layer, the sensor nodes cooperatively transmit the same packet to achieve farther transmission distance of each hop. While at the network layer, ECGR adaptively selects the appropriate cooperative nodes to form forwarding clusters to forward the packet when we consider energy consumption and geographic information of the sensor nodes. In order to fully assess the energy consumption, we take circuit energy consumption of the sensor nodes into consideration. According to the description above, we increase the transmitting distance of each hop but decrease the hop number. Meanwhile, the overall network energy consumption is reduced and balanced among nodes to extend the life of the network.

Wireless multicast for delay-sensitive data is challenging because different receivers may experience different packet losses. Network coding offers significant advantages over the traditional Automatic Repeat-reQuest (ARQ) protocols in that it mitigates the need for retransmission and has the potential to approach the min-cut capacity. Network-coded multicast would be, however, vulnerable to false packet injection attacks, in which the adversary injects bogus packets to prevent receivers from correctly decoding the original data. Without a right defense in place, even a single bogus packet can completely change the decoding outcome. Existing solutions either incur high computation cost or cannot withstand high packet loss. In this paper, we propose a novel scheme to defend against false packet injection attacks on network-coded multicast for delay-sensitive data. Specifically, we propose an efficient authentication mechanism based on null space properties of coded packets, aiming to enable receivers to detect any bogus packets with high probability. We further design an adaptive scheduling algorithm based on Markov Decision Processes (MDP) to maximize the number of authenticated packets that can be received within a given time constraint. Both analytical and simulation results have been provided to demonstrate the efficacy and efficiency of our proposed scheme.

In this paper, a new algorithm is proposed for facial expression mapping. The proposed algorithm first introduces a new elastic model to balance the global and local warping effects such that the impacts from facial feature differences between people can be avoided, thus more reasonable geometric warping results can be created. Furthermore, a muscle-distribution-based (MD) model is also proposed. The proposed MD model utilizes the muscle distribution information of the human face to evaluate and strengthen the facial illumination details. By this way, the impacts from human face difference as well as the effects of unsuitable noise filtering can be effectively alleviated. Experimental results show that our proposed algorithm can create obviously better facial expression results than the existing methods.

In this paper, a new heat-map-based (HMB) algorithm is proposed for human group activity recognition. The proposed algorithm first models people trajectories as series of "heat sources" and then applies a thermal diffusion process to create a heat map (HM) for representing the group activities. Based on this heat map, a new surface-fitting (SF) method is also proposed for recognizing human group activities. Our proposed HM feature can efficiently keep the temporal motion information of the group activities while the proposed SF method can effectively catch the characteristics of the heat map for activity recognition. Experimental results demonstrate the effectiveness of our proposed algorithm.

In this paper, a novel patch-based (PB) framework is proposed for detecting abnormal activities using a Pan-Tilt-Zoom (PTZ) camera. We first propose a new scene-patch-based (SSB) algorithm which can efficiently extract the target object's global trajectory from the PTZ camera. Furthermore, we propose an extended network-based (ENB) algorithm for detecting abnormal activities. The proposed ENB algorithm models the entire scene as a network where each node in the network corresponds to a patch of the scene and each edge between nodes corresponds to the activity correlation between the scene patchs. Based on this network, a recursive training strategy is proposed to train the edge weights in the network such that abnormal activities can be effectively detected through these trained edge weights. Experimental results demonstrate the effectiveness of our proposed framework.

In this paper, a new abnormal activity detection algorithm is proposed for multi-camera surveillance applications. The proposed algorithm models the entire scene covered by the multi-camera system as a network. In this network, each node corresponds to a segmentation of the entire scene and each edge represents the activity correlation between the corresponding segmentations. Based on this network, the proposed algorithm further models human activities as the signal transmission process in the network. Thus, abnormal activities can be detected if their 'network transmission energy' is obviously larger than the normal case. Compared with the previous methods, the proposed algorithm is more general and is flexible to handle various multi-camera scenarios and configurations. Experimental results demonstrate the effectiveness of the proposed algorithm.

Video enhancement has played very important roles in many applications. However, most existing enhancement methods only focus on the spatial quality within a frame while the temporal qualities of the enhanced video are often unguaranteed. In this paper, a new algorithm is proposed for video enhancement. The proposed algorithm introduces new temporal constraints and combines them with the spatial constraints such that both the spatial and temporal qualities of the video can be improved. Two strategies are proposed for including the temporal constraints. Experimental results demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose a novel algorithm for improving and visualizing image search results. The proposed algorithm improves user's image search experience by three steps: (1) re-rank the initial image search results by the random walk refinement based on visual consistency and saliency cues, (2) project the re-ranked images into a 2-dimentional panel according to their saliency information and correlations, (3) detect and extract the saliency regions in each image for final visualization. To evaluate the performance of our algorithm, user study has been conducted. Experimental results demonstrate that our visualization algorithm provides more pleasing image search experience than the conventional image search methods.

In this paper, a new package-group-transmission-based algorithm is proposed for human activity recognition in videos. The proposed algorithm first models the entire scene as a network where each node in the network corresponds to a segmentation of the scene. Based on this network, we further model people in the scene as groups of packages. Thus, various human activities can be modeled as the process of "package group transmission" in the network and these activities can be efficiently recognized by suitably analyzing the "package transmission" process. Our proposed algorithm can not only detect activities under the challenging multiple camera scenario, but also be able to recognize various complex group activities among people. Experimental results demonstrate the effectiveness of our proposed algorithm.

Video enhancement plays an important role in various video applications. It is desirable to achieve high visual quality of the entire picture where multiple region-of-interests (ROIs) within the frame can be adaptively and simultaneously enhanced. In this paper, a new global-based video enhancement algorithm is proposed. The proposed algorithm first analyzes features from different ROIs. Then, a 'global' tone mapping curve is created for the entire picture which can adaptively enhance different regions at the same time. According to the statistics of ROIs, two fusion strategies, i.e., piecewise-based and factor-based fusions, are proposed for creating the global tone mapping curve. Experimental results show that the proposed algorithm can obtain more appealing perceptual quality than the state-of-the-art algorithms.

In this paper, we discuss the hybrid capacity region of a generic multicarrier multilayer broadcast and unicast cellular system with independent channel coding over subcarriers. In particular, we analytically derive the capacity region and provide conditions to achieve its boundary. The simulation results show that the hybrid capacity regions are considerably higher than those of the traditional time division multiplexing scheme.

This paper proposes a new scheme for cooperative hybrid network resource allocation. Different from the traditional cognitive radio networks that aim to utilize the spectrum holes (such as white space in TV spectrum) in an uncoordinated way, this paper proposes an OFDMA based collaborative hybrid network. We study the joint resource allocation problem for both the primary users and the secondary users. Results show that by cooperatively allocating resources in the primary network and the secondary network, we can achieve higher spectral efficiency while provide satisfactory admission control and QoS for all users.

Motion estimation (ME) is one of the most time-consuming parts in video coding. The use of multiple partition sizes in H.264/AVC makes it even more complicated when compared to ME in conventional video coding standards. It is important to develop fast and effective sub-pixel ME algorithms since: 1) the computation overhead by sub-pixel ME has become relatively significant while the complexity of integer-pixel search has been greatly reduced by fast algorithms, and 2) reducing sub-pixel search points can greatly save the computation for sub-pixel interpolation. In this letter, a novel fast sub-pixel ME algorithm is proposed which performs a “rough” sub-pixel search before the partition selection, and performs a “precise” sub-pixel search for the best partition. By reducing the searching load for the large number of non-best partitions, the computation complexity for sub-pixel search can be greatly decreased. Experimental results show that our method can reduce the sub-pixel search points by more than 50% compared to existing fast sub-pixel ME methods with negligible quality degradation.

In this paper, the performance of orthogonal frequency division multiple access orthogonal frequency division multiple access (OFDMA) is studied in the context of the general multiuser multicarrier multiple-input multiple-output (MIMO) (M3) system for both uplink (UP) and downlink (DL) channels. The relationship between OFDMA and the optimal M3solution is investigated from an information theoretical point of view. Specifically, the necessary and sufficient conditions of OFDMA being the capacity-maximizing M3transmission scheme is derived. In addition, OFDMA optimality is explicitly analyzed in some special cases.

Wireless infrastructures can be conveniently categorized into two types: those that provide point-to-point dasiaunicastpsila connectivity (e.g., cellular) and others that are dasiabroadcastpsila in nature (such as TV and radio) and are inherently point-to-multipoint. This paper introduces a transmission scheme for multicarrier hybrid system that supports both broadcasting and unicasting on a single-frequency platform. In particular, we present a dirty-paper coding (DPC) based overlay architecture to enable interference-free reception of unicast signals. The fundamental limits of hybrid communications are investigated, along with power allocation algorithms that approach the hybrid capacity. Our results show that OFDMA for unicast offers the best hybrid performance and considerable gains over orthogonal multiplexing (e.g., TDM/FDM) can be achieved through interference-free overlay. In addition, we describe approaches to further improve the spectral efficiency and the broadcast coverage through multicell collaboration.

Motivated by the increasing popularity of OFDMA, this paper studies the sum-rate optimality of OFDMA as an uplink multicarrier multiple-access scheme. We address the relationship between OFDMA and the optimal multicarrier multiple- access schemes. More specifically, we are interested in answering the following three questions: (i) what are the conditions under which OFDMA is sum-rate optimal? (ii) what is the probability of OFDMA being sum-rate optimal? and (iii) in the case OFDMA is suboptimal, what is the performance gap between OFDMA and the optimal multicarrier multiple access solution? Within a generic discrete multicarrier uplink framework, we present the necessary and sufficient conditions for OFDMA optimality and derive the probabilities of these conditions for both low and high SNR regions. Our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is large.

This paper studies the optimality of OFDMA in MIMO enabled multi-user multicarrier uplink network. We present the necessary and sufficient conditions for OFDMA being optimal on any boundary point of the capacity region. In the low SNR case, we further derive the probabilities of these conditions. For the general MIMO case, we provide an example with which we quantify OFDMA optimality and show the probability of OFDMA being optimal is non-travial regardless of the SNR regions. To reach the the MISO sum capacity limit, our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is sufficiently large.

Motivated by the increasing popularity of OFDMA, this paper studies the sum rate optimality of OFDMA as an uplink multicarrier multiple access scheme. More specifically, we are interested in answering the following three questions: (i) what are the conditions under which OFDMA is sum-rate optimal? (ii) what is the probability of OFDMA being sum-rate optimal? and (iii) in the case OFDMA is suboptimal, what is the performance gap between OFDMA and the optimal multicarrier solution? Within a generic discrete multicarrier uplink framework, we present the necessary and sufficient conditions for OFDMA optimality and derive the probabilities of these conditions for both low and high SNR regions. Our results show that the number of shared subchannels under the optimal solution should be less than the number of total users, thus the performance gap between the OFDMA scheme and the optimal solution is negligible when the number of subchannels is sufficiently large.