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In this paper, we investigate the unmanned aerial vehicle (UAV)-Aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem's hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.

In this paper, we investigate the unmanned aerial vehicle (UAV)-Aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem's hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.

Slow fading broadcast channels can model a wide range of applications in wireless networks. Due to delay requirements and the unavailability of the channel state information at the transmitter (CSIT), these channels for many applications are non-ergodic. The appropriate measure for designing signals in non-ergodic channels is the outage probability. In this paper, we provide a method to optimize space-time block codes (STBCs) based on the outage probability at moderate SNRs. Multilevel polar coded-modulation is a new class of coded-modulation techniques that benefits from low-complexity decoders and simple rate matching. In this paper, we derive the outage optimality condition for multistage decoding and propose a rule for determining component code rates. We also derive an upper bound on the outage probability of STBCs for designing the set-partitioning-based labeling. Finally, due to the optimality of the outage-minimized STBCs for long codes, we introduce a novel method for the joint optimization of short-to-moderate length polar codes and STBCs.

Link failures in wide area networks are common and cause significant data losses. Mesh-based protection schemes offer high capacity efficiency but they are slow and require complex signaling. Additionally, real-time reconfigurations of cross-connects threaten their transmission integrity. On the other hand, there are other schemes that are proactive. Proactivity results in higher restoration speed, lower signaling complexity, and higher transmission integrity. This paper introduces a coding-based proactive protection scheme, named Coded Path Protection (CPP). In CPP, a backup stream of the primary data is encoded with other data streams, resulting in capacity savings. In addition to a systematic approach of building valid coding structures, this paper presents an optimal and simple capacity placement and coding group formation algorithm. The algorithm converts the sharing structure of any solution of a Shared Path Protection (SPP) technique into a coding structure with minimum extra capacity. We conducted quantitative and qualitative comparisons of our technique with SPP and Shared Link Protection (SLP). Simulation results confirm that the CPP is significantly faster than both the SPP and the SLP. It is clearly more capacity efficient than the SLP while it incurs marginal extra capacity beyond that of the SPP. © 1972-2012 IEEE.

In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial sparsity of legitimate user's channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limited number of radio-frequency (RF) chains, while artificial noise (AN) is broadcast over the remaining nondominant angles interfering only with the eavesdropper with a high probability. It is shown that the channel sparsity plays a fundamental role analogous to secret keys in achieving secure communication. Hence, by defining two statistical measures of the channel sparsity, we analytically characterize its impact on secrecy rate. In particular, a substantial improvement on secrecy rate can be obtained by the proposed scheme due to the uncertainty, i.e., 'entropy', introduced by the channel sparsity which is unknown to the eavesdropper. It is revealed that sparsity in the power domain can always contribute to the secrecy rate. In contrast, in the angle domain, there exists an optimal level of sparsity that maximizes the secrecy rate. The effectiveness of the proposed scheme and derived results are verified by numerical simulations.

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks.

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks.

In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial sparsity of legitimate user's channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limited number of radio-frequency (RF) chains, while artificial noise (AN) is broadcast over the remaining nondominant angles interfering only with the eavesdropper with a high probability. It is shown that the channel sparsity plays a fundamental role analogous to secret keys in achieving secure communication. Hence, by defining two statistical measures of the channel sparsity, we analytically characterize its impact on secrecy rate. In particular, a substantial improvement on secrecy rate can be obtained by the proposed scheme due to the uncertainty, i.e., 'entropy', introduced by the channel sparsity which is unknown to the eavesdropper. It is revealed that sparsity in the power domain can always contribute to the secrecy rate. In contrast, in the angle domain, there exists an optimal level of sparsity that maximizes the secrecy rate. The effectiveness of the proposed scheme and derived results are verified by numerical simulations.

Link failures in wide area networks are common and cause significant data losses. Mesh-based protection schemes offer high capacity efficiency but they are slow and require complex signaling. Additionally, real-time reconfigurations of cross-connects threaten their transmission integrity. On the other hand, there are other schemes that are proactive. Proactivity results in higher restoration speed, lower signaling complexity, and higher transmission integrity. This paper introduces a coding-based proactive protection scheme, named Coded Path Protection (CPP). In CPP, a backup stream of the primary data is encoded with other data streams, resulting in capacity savings. In addition to a systematic approach of building valid coding structures, this paper presents an optimal and simple capacity placement and coding group formation algorithm. The algorithm converts the sharing structure of any solution of a Shared Path Protection (SPP) technique into a coding structure with minimum extra capacity. We conducted quantitative and qualitative comparisons of our technique with SPP and Shared Link Protection (SLP). Simulation results confirm that the CPP is significantly faster than both the SPP and the SLP. It is clearly more capacity efficient than the SLP while it incurs marginal extra capacity beyond that of the SPP. © 1972-2012 IEEE.

In this paper, we investigate secure transmission in a massive multiple-input multiple-output system adopting low-resolution digital-to-analog converters (DACs). Artificial noise (AN) is deliberately transmitted simultaneously with the confidential signals to degrade the eavesdropper's channel quality. By applying the Bussgang theorem, a DAC quantization model is developed which facilitates the analysis of the asymptotic achievable secrecy rate. Interestingly, for a fixed power allocation factor φ , low-resolution DACs typically result in a secrecy rate loss, but in certain cases, they provide superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically, we derive a closed-form SNR threshold which determines whether low-resolution or high-resolution DACs are preferable for improving the secrecy rate. Furthermore, a closed-form expression for the optimal φ is derived. With AN generated in the null-space of the user channel and the optimal φ , low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for random AN with the optimal φ , the secrecy rate is hardly affected by the DAC resolution because the negative impact of the quantization noise can be compensated by reducing the AN power. All the derived analytical results are verified by numerical simulations.

In this paper, we investigate secure transmission in a massive multiple-input multiple-output system adopting low-resolution digital-to-analog converters (DACs). Artificial noise (AN) is deliberately transmitted simultaneously with the confidential signals to degrade the eavesdropper's channel quality. By applying the Bussgang theorem, a DAC quantization model is developed which facilitates the analysis of the asymptotic achievable secrecy rate. Interestingly, for a fixed power allocation factor φ , low-resolution DACs typically result in a secrecy rate loss, but in certain cases, they provide superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically, we derive a closed-form SNR threshold which determines whether low-resolution or high-resolution DACs are preferable for improving the secrecy rate. Furthermore, a closed-form expression for the optimal φ is derived. With AN generated in the null-space of the user channel and the optimal φ , low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for random AN with the optimal φ , the secrecy rate is hardly affected by the DAC resolution because the negative impact of the quantization noise can be compensated by reducing the AN power. All the derived analytical results are verified by numerical simulations.

The performance of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is limited by the sparse nature of propagation channels and the restricted number of radio frequency chains at transceivers. The introduction of reconfigurable antennas offers an additional degree of freedom on designing mmWave MIMO systems. This paper provides a theoretical framework for studying the mmWave MIMO with reconfigurable antennas. Based on the virtual channel model, we present an architecture of reconfigurable mmWave MIMO with beamspace hybrid analog-digital beamformers and reconfigurable antennas at both the transmitter and the receiver. We show that employing reconfigurable antennas can provide throughput gain for the mmWave MIMO. We derive the expression for the average throughput gain of using reconfigurable antennas in the system, and further derive the expression for the outage throughput gain for the scenarios where the channels are (quasi) static. Moreover, we propose a low-complexity algorithm for reconfiguration state selection and beam selection. Our numerical results verify the derived expressions for the throughput gains and demonstrate the near-optimal throughput performance of the proposed low-complexity algorithm.

The performance of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems is limited by the sparse nature of propagation channels and the restricted number of radio frequency chains at transceivers. The introduction of reconfigurable antennas offers an additional degree of freedom on designing mmWave MIMO systems. This paper provides a theoretical framework for studying the mmWave MIMO with reconfigurable antennas. Based on the virtual channel model, we present an architecture of reconfigurable mmWave MIMO with beamspace hybrid analog-digital beamformers and reconfigurable antennas at both the transmitter and the receiver. We show that employing reconfigurable antennas can provide throughput gain for the mmWave MIMO. We derive the expression for the average throughput gain of using reconfigurable antennas in the system, and further derive the expression for the outage throughput gain for the scenarios where the channels are (quasi) static. Moreover, we propose a low-complexity algorithm for reconfiguration state selection and beam selection. Our numerical results verify the derived expressions for the throughput gains and demonstrate the near-optimal throughput performance of the proposed low-complexity algorithm.

In this paper, we study linear transceiver designs for indoor visible light communications (VLCs) with multiple light emitting diodes (LEDs). Specifically, we investigate VLCs including white emitting diodes and VLCs including red/green/blue (RGB) LEDs. The transmitter precoding and the offset are jointly designed by considering certain key practical lighting constraints, such as optical power, non-negativeness, and color illumination. Various non-convex transceiver design problems are formulated aiming to minimize total mean-squareerror to improve transmission reliability. We show that for multi-input single-output white VLCs, the optimal precoding reduces to a simple LED selection strategy. For multi-input multioutput (MIMO) white VLCs, we prove that the optimization problem with multiple constraints can be equivalently simplified to a problem with single constraint, which enables us to propose efficient algorithms to search local optimal solutions. For MIMO RGB VLCs, by using certain useful transformations, we show that the precoding design is equivalent to covariance matrix design of transmit signals, which can be further transformed to a convex optimization problem. To develop an algorithm to find the optimal solution, we derive the optimal structure of the covariance matrix and show that the optimal solution can be obtained via a water-filling approach. Extensive simulation results are provided to verify the performance of the proposed designs.

In this paper, we study linear transceiver designs for indoor visible light communications (VLCs) with multiple light emitting diodes (LEDs). Specifically, we investigate VLCs including white emitting diodes and VLCs including red/green/blue (RGB) LEDs. The transmitter precoding and the offset are jointly designed by considering certain key practical lighting constraints, such as optical power, non-negativeness, and color illumination. Various non-convex transceiver design problems are formulated aiming to minimize total mean-squareerror to improve transmission reliability. We show that for multi-input single-output white VLCs, the optimal precoding reduces to a simple LED selection strategy. For multi-input multioutput (MIMO) white VLCs, we prove that the optimization problem with multiple constraints can be equivalently simplified to a problem with single constraint, which enables us to propose efficient algorithms to search local optimal solutions. For MIMO RGB VLCs, by using certain useful transformations, we show that the precoding design is equivalent to covariance matrix design of transmit signals, which can be further transformed to a convex optimization problem. To develop an algorithm to find the optimal solution, we derive the optimal structure of the covariance matrix and show that the optimal solution can be obtained via a water-filling approach. Extensive simulation results are provided to verify the performance of the proposed designs.

Network coding-based link failure recovery techniques provide near-hitless recovery and offer high capacity efficiency. Diversity coding is the first technique to incorporate coding in this field and is easy to implement over small networks. However, the capacity efficiency of this implementation is restricted by its systematic coding and high design complexity despite having lower complexity than the other coding-based recovery techniques. In this paper, we propose a simple column generation-based design algorithm and a novel advanced diversity coding technique to achieve near-hitless recovery over large networks. The traffic matrix, which consists of unicast connection demands, is decomposed into traffic vectors for each destination node. Further, the connection demands in each traffic vector are partitioned into coding groups. The design framework consists of two parts: a main problem and a subproblem. The main problem is solved with Linear Programming (LP) and Integer Linear Programming (ILP), whereas the subproblem can be solved with different methods. Simulation results suggest that the novel design algorithm simplifies the capacity placement problem, which enables implementing diversity coding-based recovery including the novel coding structure on large networks with arbitrary topology. It achieves near-hitless recovery with an almost optimal capacity efficiency for any single destination-based recovery.

Network coding-based link failure recovery techniques provide near-hitless recovery and offer high capacity efficiency. Diversity coding is the first technique to incorporate coding in this field and is easy to implement over small networks. However, the capacity efficiency of this implementation is restricted by its systematic coding and high design complexity despite having lower complexity than the other coding-based recovery techniques. In this paper, we propose a simple column generation-based design algorithm and a novel advanced diversity coding technique to achieve near-hitless recovery over large networks. The traffic matrix, which consists of unicast connection demands, is decomposed into traffic vectors for each destination node. Further, the connection demands in each traffic vector are partitioned into coding groups. The design framework consists of two parts: a main problem and a subproblem. The main problem is solved with Linear Programming (LP) and Integer Linear Programming (ILP), whereas the subproblem can be solved with different methods. Simulation results suggest that the novel design algorithm simplifies the capacity placement problem, which enables implementing diversity coding-based recovery including the novel coding structure on large networks with arbitrary topology. It achieves near-hitless recovery with an almost optimal capacity efficiency for any single destination-based recovery.

Coding-based restoration techniques have proactive restoration which results in time savings over other state-of-the-art restoration techniques. Diversity coding is a coding-based recovery technique which offers near-hitless restoration with a competitive spare capacity requirement with respect to other techniques. In this paper, we show that diversity coding can achieve sub-ms restoration time. In addition, we develop two optimal algorithms for pre-provisioning of the static traffic and one for the dynamic provisioning of the traffic on-demand. There is one algorithm for systematic and one for non-systematic diversity coding in pre-provisioning. An MIP formulation and an ILP formulation are developed for systematic and non-systematic cases, respectively. The MIP formulation of the systematic diversity coding requires much fewer integer variables and constraints than similar optimal coding-based formulations. In dynamic provisioning, an ILP-based algorithm covers both of the systematic and non-systematic diversity coding. In all scenarios, diversity coding results in smaller restoration time, higher transmission integrity, and much reduced signaling complexity than the existing techniques in the literature. Simulation results indicate that diversity coding has significantly higher restoration speed than Shared Path Protection (SPP) and p-cycle techniques from the literature as well as Synchronous Optical Network (SONET) rings, which are commonly deployed by service providers today. In terms of capacity efficiency, it outperforms SONET rings and 1+1 APS, whereas it may require more extra capacity than the p-cycle technique and SPP. Diversity coding offers a preferable tradeoff which offers two orders of magnitude increase in restoration speed at the expense of less than 26% extra spare capacity. © 1972-2012 IEEE.

For Multiple-Input Multiple-Output (MIMO) systems with frequency selective fading channels, Bit-Interleaved Coded Multiple Beamforming (BICMB) with Orthogonal Frequency Division Multiplexing (OFDM) can be employed to offer both spatial and multipath diversity, making it an important technique. Nevertheless, analyzing its diversity is a challenging problem. In this paper, the diversity analysis of BICMB-OFDM is carried out. First, the maximum achievable diversity is derived and a full diversity condition is proved. Then, the performance degradation due to the subcarrier correlation is investigated. Finally, the subcarrier grouping technique is applied to combat the performance degradation and provide multi-user compatibility. © 2013 IEEE.

Coding-based restoration techniques have proactive restoration which results in time savings over other state-of-the-art restoration techniques. Diversity coding is a coding-based recovery technique which offers near-hitless restoration with a competitive spare capacity requirement with respect to other techniques. In this paper, we show that diversity coding can achieve sub-ms restoration time. In addition, we develop two optimal algorithms for pre-provisioning of the static traffic and one for the dynamic provisioning of the traffic on-demand. There is one algorithm for systematic and one for non-systematic diversity coding in pre-provisioning. An MIP formulation and an ILP formulation are developed for systematic and non-systematic cases, respectively. The MIP formulation of the systematic diversity coding requires much fewer integer variables and constraints than similar optimal coding-based formulations. In dynamic provisioning, an ILP-based algorithm covers both of the systematic and non-systematic diversity coding. In all scenarios, diversity coding results in smaller restoration time, higher transmission integrity, and much reduced signaling complexity than the existing techniques in the literature. Simulation results indicate that diversity coding has significantly higher restoration speed than Shared Path Protection (SPP) and p-cycle techniques from the literature as well as Synchronous Optical Network (SONET) rings, which are commonly deployed by service providers today. In terms of capacity efficiency, it outperforms SONET rings and 1+1 APS, whereas it may require more extra capacity than the p-cycle technique and SPP. Diversity coding offers a preferable tradeoff which offers two orders of magnitude increase in restoration speed at the expense of less than 26% extra spare capacity. © 1972-2012 IEEE.

In broadcast scenarios or in the absence of accurate channel, probability distribution information, code design for consistent channel-by-channel performance, rather than average performance over a channel distribution, may be desirable. Root and Varaiya's compound channel theorem for linear Gaussian channels promises the existence of universal codes that operate reliably whenever the channel mutual information (MI) is above the transmitted rate. This paper presents two-dimensional trellis codes that provide such universal performance over the compound linear vector Gaussian channel when demultiplexed over two, three, and four transmit antennas. The presented trellis codes, found by exhaustive search, guarantee consistent performance on every matrix channel that supports the information transmission rate with an MI gap that is similar to the capacity gap of a well-designed additive white Gaussian noise (AWGN)-specific code on the AWGN channel. As a result of their channel-by-channel consistency, the universal trellis codes presented here also deliver comparable, or, in some cases, superior, frame-error rate and bit-error rate performance under quasi-static Rayleigh fading, as compared with trellis codes of similar complexity that are designed specifically for the quasi-static Rayleigh-fading scenario.

In orthogonal frequency-division multiple access (OFDMA), closely spaced multiple subcarriers are assigned to different users for parallel signal transmission. An interleaved subcarrier-assignment scheme is preferred because it provides maximum frequency diversity and increases the capacity in frequency-selective fading channels. The subcarriers are overlapping, but orthogonal to each other such that there is no intercarrier interference (ICI). Carrier-frequency offsets (CFOs) between the transmitter and the receiver destroy the orthogonality and introduces ICI, resulting in multiple-access interference. This paper exploits the inner structure of the signals for CFO estimation in the uplink of interleaved OFDMA systems. A new uplink signal model is presented, and an estimation algorithm based on the signal structure is proposed for estimating the CFOs of all users using only one OFDMA block. Diversity schemes are also presented to improve the estimation performance. Simulation results illustrate the high accuracy and efficiency of the proposed algorithm.