Uncrewed aerial vehicles (UAVs) play a pivotal role in ensuring seamless connectivity for Internet of Things (IoT) devices, particularly in scenarios where conventional terrestrial networks are constrained or temporarily unavailable. However, traditional coverage-hole detection approaches, such as minimizing drive tests, are costly, time-consuming, and reliant on outdated radio-environment data, making them unsuitable for real-time applications. To address these limitations, this paper proposes a UAV-assisted framework for real-time detection and recovery of coverage holes in IoT networks. In the proposed scheme, a patrol UAV is first dispatched to identify coverage holes in regions where the operational status of terrestrial base stations (BSs) is uncertain. Once a coverage hole is detected, one or more UAVs acting as aerial BSs are deployed by a satellite or nearby operational BSs to restore connectivity. The UAV swarm is organized based on Delaunay triangulation, enabling scalable deployment and tractable analytical characterization using stochastic geometry. Moreover, a collision-avoidance mechanism grounded in multi-agent system theory ensures safe and coordinated motion among multiple UAVs. Simulation results demonstrate that the proposed framework achieves high efficiency in both coverage-hole detection and on-demand connectivity restoration while significantly reducing operational cost and time.

Low-altitude wireless networks are increasingly vital for the low-altitude economy, enabling wireless coverage in high-mobility and hard-to-reach environments. However, providing reliable connectivity to sparsely distributed aerial users in dynamic three-dimensional (3D) spaces remains a significant challenge. This paper investigates downlink coverage enhancement in vertical heterogeneous networks (VHetNets) beyond 5G, where unmanned aerial vehicles (UAVs) operate as emerging aerial base stations (ABSs) alongside legacy terrestrial base stations (TBSs). To improve coverage performance, we propose a coordinated multi-point (CoMP) transmission framework that enables joint transmission from ABSs and TBSs. This approach mitigates the limitations of non-uniform user distributions and enhances reliability for sparse aerial users. Two UAV deployment strategies are considered:i)random UAV placement, analyzed using stochastic geometry to derive closed-form coverage expressions, andii)optimized UAV placement using a coverage-aware weightedK-means clustering algorithm to maximize cooperative coverage in underserved areas. Theoretical analyses and Monte Carlo simulations demonstrate that the proposed CoMP-enabled VHetNet significantly improves downlink coverage probability, particularly in scenarios with sparse aerial users. These findings highlight the potential of intelligent UAV coordination and geometry-aware deployment to enable robust, adaptive connectivity in low-altitude wireless networks.

Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data, especially in small-scale datasets, is challenging, and independent edge nodes can lead to inefficient resource utilization and reduced learning performance. To address these issues, this paper proposes a collaborative optimization framework for energy-efficient federated edge learning with small-scale datasets. We first derive an expected learning loss to quantify the relationship between the number of training samples and learning objectives. A stochastic online learning algorithm is then designed to adapt to data variations, and a resource optimization problem with a convergence bound is formulated. Finally, an online distributed algorithm efficiently solves large-scale optimization problems with high scalability. Extensive simulations and autonomous navigation case studies with collision avoidance demonstrate that the proposed approach significantly improves learning performance and resource efficiency compared to state-of-the-art benchmarks.

With the rapid proliferation of large-scale sensor nodes and smart devices, the energy consumption of wireless networks has increased dramatically, posing significant challenges to the design of energy-efficient communication systems. Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged as a promising technology for enhancing energy efficiency, owing to its capability of reconfiguring the wireless propagation environment and providing full-space user coverage. In this paper, we investigate the application of STAR-RIS in a simultaneous wireless information and power transfer (SWIPT) system, leveraging the spatial beamforming capabilities of multiple-input multiple-output (MIMO) and the frequency diversity gain of orthogonal frequency-division multiplexing (OFDM). In specific, we aim to maximize the system’s weighted energy efficiency, subject to individual users’ energy harvesting and achievable data rate requirements. To address the inherent non-convexity of the formulated problem, we adopt a weighted minimum mean square error (WMMSE)-based reformulation, and develop an efficient algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP). Simulation results validate the performance advantages of the proposed STAR-RIS-aided design over conventional RIS. Furthermore, user-specific weight adjustment enables flexible and fair resource allocation across multiple users.

This paper proposes a unified optimization framework for a power splitting (PS)-based multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system with Tomlinson-Harashima pre coding (THP)-based non-linear transceivers or linear transceivers. Our aim is to minimize the transmit power under the sum mean-square-error (MSE) and energy harvesting (EH) constraints. To solve this formulated non-convex problem, we propose a structural solution (SS) which applies the closed-form expressions of equalization matrices, feedback matrices and precoding matrices to establish an equivalent optimization problem in terms of the power allocation and PS ratio. Then the equivalent problem is solved by a two-layer optimization scheme. Simulation results show that the THP-based non-linear transceivers need less transmit power than linear transceivers to achieve the same performance of EH and sum MSE.

Traditional low-power wide-area network (LPWAN) transceivers typically compromise data rates to achieve deep coverage. This paper presents a novel transceiver that achieves high receiver sensitivity and low computational complexity. At the transmitter, we replace the conventional direct sequence spread spectrum (DSSS) preamble with a chirp spread spectrum (CSS) preamble, consisting of a pair of down-chirp and up-chirp signals that are conjugate to each other, simplifying packet synchronization. For enhanced coverage, the payload incorporates continuous phase frequency shift keying (CPFSK) to maintain a constant envelope and phase continuity, in conjunction with DSSS to achieve a high spreading gain. At the receiver, we develop a double-peak detection method to improve synchronization and a non-coherent joint despreading and demodulation scheme that increases receiver sensitivity while maintaining simplicity in implementation. Furthermore, we optimize the preamble detection threshold and spreading sequences for maximum non-coherent receiver performance. The software-defined radio (SDR) prototype, developed using GNU Radio and USRP, along with operational snapshots, showcases its practical engineering applications. Extensive Monte Carlo simulations and field-test trials demonstrate that our transceiver outperforms traditional ones in terms of receiver sensitivity, while also being low in complexity and cost-effective for LPWAN requirements.

The evolution of Internet of Things technologies is driven by four key demands: ultra-low power consumption, high spectral efficiency, reduced implementation cost, and support for massive connectivity. To address these challenges, this paper proposes two novel modulation schemes that integrate continuous phase modulation (CPM) with spread spectrum (SS) techniques. We begin by establishing the quasi-orthogonality properties of CPM-SS sequences. The first scheme, termed IM-CPM-SS, employs index modulation (IM) to select spreading sequences from the CPM-SS set, thereby improving spectral efficiency while maintaining the constant-envelope property. The second scheme, referred to as CIM-CPM-SS, introduces code index modulation (CIM), which partitions the input bits such that one subset is mapped to phase-shift keying symbols and the other to CPM-SS sequence indices. Both schemes are applied to downlink non-orthogonal multiple access (NOMA) systems. We analyze their performance in terms of bit error rate (BER), spectral and energy efficiency, computational complexity, and peak-to-average power ratio characteristics under nonlinear amplifier conditions. Simulation results demonstrate that both schemes outperform conventional approaches in BER while preserving the benefits of constant-envelope, continuous-phase signaling. Furthermore, they achieve higher spectral and energy efficiency and exhibit strong resilience to nonlinear distortions in downlink NOMA scenarios.

Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Parameter sharing consists of synchronous and asynchronous ways: the former transmits parameters as blocks or frames and waits until all transmissions finish, whereas the latter provides messages about the status of pending and failed parameter transmission requests. Whatever synchronous or asynchronous parameter sharing is applied, the learning model shall adapt to distinct network architectures as an improper learning model will deteriorate learning performance and, even worse, lead to model divergence for the asynchronous transmission in resource-limited large-scale Internet-of-Things (IoT) networks. This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This decentralized learning model approaches a convex function as the number of nodes increases, and its learning process converges to a global stationary point with a higher probability than the centralized FL model. Moreover, by jointly accounting for the convergence bound of federated learning and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to minimize the transmission delay. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low transmission delay.

With the development of low-power devices, wireless power transfer and wireless energy harvesting techniques become increasingly important. As the critical component, the rectifier is required to maintain good performance under different scenarios. Most existing works focus on the improvement in bandwidth and input power range with a constant load value. However, the impedance of the driven devices cannot be predicted in practical applications, resulting in performance deterioration. Thus, an adaptive signal diversion approach is proposed to diver the injected signal to the parallel high and low load branches with distinct reactance compensation networks. This topology can be applied to both single- and dual-band rectifiers with simplicity and scalability in design. For validation, two rectifiers are designed, fabricated, and measured. The rectifier operating at 2.4 GHz achieves a load range of 0.16 k$\Omega $to 6 k$\Omega $(load variation ratio of 37.5). The dual-band rectifier working at 2.49 GHz and 5.14 GHz achieves load ranges from 0.21 to 5.4 k$\Omega $(load variation ratio of 25.7) and from 0.07 to 4.2 k$\Omega $(load variation ratio of 60), respectively. It can be found that the proposed rectifiers exhibit the largest load variation ratio with the minimum number of diodes and load compared with the state-of-the-art works.

Unmanned aerial vehicle (UAV) communications have been widely accepted as promising technologies to support air-to-ground communications in the forthcoming sixth-generation (6G) wireless networks. This paper proposes a novel air-to-ground communication model consisting of aerial base stations served by UAVs and terrestrial user equipments (UEs) by integrating the technique of coordinated multi-point (CoMP) transmission with the theory of stochastic geometry. In particular, a CoMP set consisting of multiple UAVs is developed based on the theory of Poisson-Delaunay tetrahedralization. Effective UAV formation control and UAV swarm tracking schemes for two typical scenarios, including static and mobile UEs, are also developed using the multi-agent system theory to ensure that collaborative UAVs can efficiently reach target spatial positions for mission execution. Thanks to the ease of mathematical tractability, this model provides explicit performance expressions for a typical UE’s coverage probability and achievable ergodic rate. Extensive simulation and numerical results corroborate that the proposed scheme outperforms UAV communications without CoMP transmission and obtains similar performance to the conventional CoMP scheme while avoiding search overhead.

Air-to-ground (A2G) networks, using unmanned aerial vehicles (UAVs) as base stations to serve terrestrial user equipments (UEs), are promising for extending the spatial coverage capability in future communication systems. Coordinated transmission among multiple UAVs significantly improves network coverage and throughput compared to a single UAV transmission. However, implementing coordinated multi-point (CoMP) transmission for UAV mobility requires complex cooperation procedures, regardless of the handoff mechanism involved. This paper designs a novel CoMP transmission strategy that enables terrestrial UEs to achieve reliable and seamless connections with mobile UAVs. Specifically, a computationally efficient CoMP transmission method based on the theory of Poisson-Delaunay triangulation is developed, where an efficient subdivision search strategy for a CoMP UAV set is designed to minimize search overhead by a divide-and-conquer approach. For concrete performance evaluation, the cooperative handoff probability of the typical UE is analyzed, and the coverage probability with handoffs is derived. Simulation results demonstrate that the proposed scheme outperforms the conventional Voronoi scheme with the nearest serving UAV regarding coverage probabilities with handoffs. Moreover, each UE has a fixed and unique serving UAV set to avoid real-time dynamic UAV searching and achieve effective load balancing, significantly reducing system resource costs and enhancing network coverage performance.

The propagation loss of RF signals is a significant issue in simultaneous wireless information and power transfer (SWIPT) systems. Additionally, ensuring information security is crucial due to the broadcasting nature of wireless channels. To address these challenges, we exploit the potential of active intelligent reflecting surface (IRS) in a multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system. The active IRS provides better beamforming gain than the passive IRS, reducing the “double-fading” effect. Moreover, the noise introduced at the active IRS can be used as artificial noise (AN) to jam eavesdroppers. This paper formulates a secrecy sum-rate maximization problem related to precoding matrices, power splitting (PS) ratios, and the IRS matrix. Since the problem is highly non-convex, we propose a block coordinate descent (BCD)-based algorithm to find a sub-optimal solution. Moreover, we develop a heuristic algorithm based on the zero-forcing precoding scheme to reduce computational complexity. Simulation results show that the active IRS achieves a higher secrecy sum rate than the passive and non-IRS systems, especially when the transmit power is low or the direct link is blocked. Moreover, increasing the power budget at the active IRS can significantly improve the secrecy sum rate.

Physical-layer authentication (PLA) can provide lightweight security solutions for the next-generation Internet of Things (IoT) networks. This paper adopts and modifies the spreading code authentication techniques initially designed for the Global Navigation Satellite System to apply PLA to narrowband IoT networks. In particular, the chip values of the PHY-layer preamble are replaced by a message-generated tag at the transmitter. At the receiver, two kinds of test statistics, cross-correlation and double-correlation values, are computed to decide the authenticity of a received signal. Also, the closed-form expressions of the corresponding optimal thresholds for the hypothesis tests are explicitly derived. Afterward, the single-frame authentication schemes are extended to multi-frame authentication, where several frames are jointly authenticated. Simulation results of the proposed schemes, along with the prototype validation of the double-correlation scheme based on the GNU Radio/USRP SDR platform, corroborate the effectiveness of the PLA strategies.

Energy-efficient scheduling and resource allocation strategies help reduce interference and extend the lifetime of power-limited Internet of Things (IoT) devices. This paper focuses on improving the transmission efficiency and working time of power-limited data acquisition equipment, e.g., low-power consumption IoT sensors. In particular, the cognitive device tunes its transmission time and power rationally to avoid interference and recharges itself by conducting energy harvesting. Inspired by the concept of the age of information, we coin the concept of the value of update (VoU) and use it to guide devices to upload data in a timely manner and optimize the key parameters through a deep deterministic policy gradient (DDPG) neural network to maximize the long-term VoU. Finally, extensive simulations are conducted to demonstrate the effectiveness and robustness of the proposed scheme.

This paper designs a novel ground-to-air communication scheme to serve unmanned aerial vehicles (UAVs) through legacy terrestrial base stations (BSs). In particular, a tractable coordinated multi-point (CoMP) transmission based on the geometry of Poisson-Delaunay triangulation is developed, which provides reliable and seamless connectivity for UAVs. An effective dynamic frequency allocation scheme is designed to eliminate inter-cell interference by using the theory of circle packing. For exact performance evaluation, the handoff probability of a typical UAV is characterized, and then the coverage probability with handoffs is attained. Simulation and numerical results corroborate that the proposed scheme outperforms the conventional CoMP scheme with three nearest cooperating BSs in terms of handoff and coverage probabilities. Moreover, as each UAV has a fixed and unique CoMP BS set, it avoids the real-time dynamic BS searching process, thus reducing the feedback overhead.

In Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper considers efficient communication under a task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms.

To meet the further explosive capacity demand, network densification is a promising technology. The load imbalance between cells in an ultra-dense heterogeneous network is a major challenge, which seriously affects the performance of the system. Existing mobility load balancing (MLB) methods usually operate in reactive mode. The parameters of the cells are adjusted reactively according to the dynamic changes of network load. The inherent reactivity of existing LB schemes undermines the quality of experience in 5G and beyond. To solve this problem, we propose a mobility management framework for load balancing, which firstly uses the Bayesian additive regression trees to predict the users’ temporal and spatial mobility based on a weekly cycle and then formulate the MLB optimization problem based on the soft load. Moreover, one solution is proposed to solve the 01 MLB problem. The simulation results show that the proposed method can better optimize the network performance and realize the intelligent mobile management for the future network.

This article performs a unified analysis of three coordinated multipoint (CoMP) transmission strategies in the downlink of mmWave cellular networks, including the fixed-number base station (BS) cooperation (FNC), the fixed-region BS cooperation (FRC), and the interference-aware BS cooperation (IAC). We first develop a comprehensive framework for CoMP operation in cellular networks, and investigate the network performance under a Poisson point process (PPP) model together with mmWave spectrum. To show what fraction of users in the network achieve target reliability for a given signal to interference-plus-noise ratio (SINR)/signal-to-interference ratio (SIR), we derive the SINR/SIR meta distributions, and further obtain the coverage probability as well as mean local delay for the three cooperation strategies. A pivotal intermediate step to compute the performance metrics is the derivation of joint distributions of distances between a typical user and cooperative BSs. Our analysis demonstrates that parameters of blockage have a significant influence on the network performance for the three CoMP schemes. We find that the FRC scheme makes more users achieve the given link reliability for the scenario with a low density of BSs, while the IAC scheme provides better performance for the network with a high density of BSs. Moreover, the optimal CoMP scheme can be approximately selected by considering the nearest distance from the serving BS to user and the radius of the approximate line-of-sight (LoS) region in the cellular networks.

Simultaneous wireless information and power transfer (SWIPT) has emerged as an appealing solution to prolonging the lifetime of low-power Internet of Things (IoT) networks. Meanwhile, an intelligent reflecting surface (IRS) can reconstruct a favorable wireless propagation environment for IoT terminals to achieve high spectrum and energy efficiencies. To take full advantage of these two technologies, this article studies the optimization of an IRS-assisted multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system with nonlinear energy harvesting (EH) model. In particular, we aim to maximize the achievable data rate by jointly designing the transmit precoding matrices, the IRS matrix, as well as the power splitting (PS) ratio subject to the transmit power and harvested power constraints. Since the formulated problem is highly nonconvex, we develop an alternating optimization (AO)-based algorithm to find a high-quality suboptimal solution. Moreover, we further design a heuristic algorithm based on a two-stage optimization strategy to reduce the computational complexity. Simulation results verify that the proposed AO-based algorithm can significantly improve the achievable data rate compared to conventional benchmarks, and the proposed heuristic low-complexity algorithm can achieve comparable performance to the AO-based algorithm.

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency.

Large scale location management in wireless powered communication networks (WPCNs) can benefit the network performance, and it can also provide location based services for IoT applications without maintaining the batteries. However, it is difficult to attain accurate node positions only based on ranging information from anchors. Thus, cooperative localization is an effective way to increase the node positioning accuracy. In this paper, we mainly investigate the performance of cooperative localization in WPCNs, in which the nodes require energy from remote energy access point (E-AP). We firstly analyze the Cramer-Rao Lower Bound (CRLB) for the full connected´ network and the spatial recursive form for a single joint node respectively. Then we propose beamforming schemes to further optimize the cooperative localization performance, which are designed to achieve the minimum localization errors. The simulations demonstrate the highly accurate localization performance of our proposed schemes, which outperform the existing power allocation schemes.

The 5th generation mobile networks (5G) and their key technologies need to provide higher spectrum efficiency utilization and smooth integration of broadband and narrowband systems. Filter bank multi-carrier transmission with Offset Orthogonal Amplitude Modulation (FBMC-OQAM) has great advantages to achieve this goal by combating multipath effects and offering high-speed data rate in wireless channels. In this paper, an improved partial transmit sequence(PTS) scheme employing particle swarm optimization (PSO) algorithm is proposed to reduce the peak-to-average-power ratio (PAPR) of the FBMC-OQAM system. Compared with the conventional PTS scheme, the proposed scheme takes the overlapping structure of FBMC-OQAM signal into account, and develop a segmentation scheme based on windowing for the PTS scheme (W-PTS). To reduce the computational complexity, we further propose a PSO-PTS scheme, which significantly reduces the computational complexity. Simulation results show that the proposed scheme could provide a significant performance in PAPR reduction with a relative low complexity.

As a forerunner in 5G ecosystem construction and industry application, Narrowband Internet of Things (NB-IoT) will be inevitably coexisting with legacy Long-Term Evolution (LTE) system. To meet the key performance indicators defined in 5G standard, it is imperative for NB-IoT to mitigate the LTE interference. By virtue of the strong temporal correlation of NB-IoT signal, this paper develops a sparse recovery algorithm based on K-means clustering, which iteratively clusters the correlation coefficients between the measurement vector and each column of observation matrix. Compared with the ideal case without interference, extensive simulation results demonstrate the effective recovery of the proposed algorithm.

Computation efficiency (CE) is crucial to mobile edge computing (MEC) for intelligent Internet of Things (IoT) applications, in addition to energy efficiency. To improve CE, this paper designs a backscatter-assisted wireless powered MEC network, where IoT terminals can partially offload their computation tasks via hybrid offloading through harvest-then-transmit protocol and/or backscatter communications. In particular, a CE maximization problem is formulated from a system perspective and an iterative algorithm is developed to tackle the problem, by using the Dinkelbach and Lagrangian duality methods. Simulation results demonstrate the superiority of the proposed scheme over competing ones and the flexibility in achieving trade-offs between different computation and communication modes.

Non-orthogonal multiple access (NOMA) is a promising solution for secure transmission under massive access. However, in addition to the uncertain channel state information (CSI) of the eavesdroppers due to their passive nature, the CSI of the legitimate users may also be imperfect at the base station due to the limited feedback. Under both channel uncertainties, the optimal power allocation and transmission rate design for a secure NOMA scheme is currently not known due to the difficulty of handling the probabilistic constraints. This article fills this gap by proposing novel transformation of the probabilistic constraints and variable decoupling so that the security guaranteed sum-rate maximization problem can be solved by alternatively executing branch-and-bound method and difference of convex programming. To scale the solution to a truly massive access scenario, a first-order algorithm with very low complexity is further proposed. Simulation results show that the proposed first-order algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant transmission scheme significantly improves the security guaranteed sum-rate compared to the orthogonal multiple access transmission and NOMA ignoring CSI uncertainty.

This work investigates the energy-efficient resource allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission in cell-free massive multiple-input multiple-output (MIMO) systems, where each user equipment (UE) performs wireless information and power transfer simultaneously. To begin with, the achievable data rates for multicast and unicast services are derived in closed form, as well as the received radio frequency (RF) power at each UE. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem with complex constraints. To suit the massive access setting, a first-order algorithm is developed to find both initial feasible point and the nearly optimal solution. Moreover, an accelerated algorithm is designed to improve the convergence speed. Numerical results demonstrate that the proposed first-order algorithms can achieve almost the same EE as that of second-order approaches yet with much lower computational complexity, which provides insight into the superiority of the proposed algorithms for massive access in cell-free massive MIMO systems.

With the rapid development of wireless technologies, the electromagnetic energy scattered in the environment becomes one of the most attractive energy sources for low power devices which are highly demanded in various applications. Since the available electromagnetic signals may have different frequencies, the corresponding receiving antenna and rectifier are required to support multi-band operation to obtain as much energy as possible. However, existing multi-band rectifiers usually have a complex structure or unsatisfactory performance. For this issue, a self-matched rectifying structure is proposed. The theoretical analysis of the proposed configuration demonstrates the unique self-matched characteristics, which is important to implement multi-band operation with simplicity in design procedure and structure. For validation, two rectifiers are designed, fabricated and measured. The dual-band rectifier achieves measured conversion efficiencies of 69.37% and 55.28% at 2.38 GHz and 4.99 GHz with 5 dBm input power in actual measurement, respectively. Owing to the self-matched property, a tri-band design operating at 1.78 GHz, 2.35 GHz, and 4.97 GHz can be easily implemented and achieve efficiency of 60.95%, 71.37%, and 54.01% under test, respectively. Compared with the existing works, the proposed rectifier has high simplicity in both design and structure maintaining high conversion efficiencies at different frequencies.

In recent years, cognitive services like face recognition and speech recognition has found wide applications in smart cities. The main approach of providing cognitive services depends on cloud computing, which however suffers high response latency and information security problems. In this paper, based on edge computing, prototypes on face recognition and speaker recognition are developed on a cloud radio access network platform. In the face recognition architecture, a traditional face recognition network is cascaded to a face detection network so as to mitigate the interference of irrelevant information. In the speaker recognition architecture, multi-task learning is exploited to improve the generalization of neural network, and a phoneme recognition task is employed as an auxiliary task to assist the speaker recognition. Experimental results corroborate the effectiveness of the proposed architectures in terms of recognition accuracy and response latency.

This work investigates energy-efficient power allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission of cell-free massive multiple-input multiple-output systems. In particular, the achievable data rates for multicast and unicast services are derived. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem. For ease of mathematical tractability, the smooth and successive convex approximations are exploited to transform the original optimization problem into a sequence of quasi-concave problems and, then, Dinkelbach's method is applied to solve the resultant problems. Simulation results demonstrate that the LDM-based transmission achieves higher EE than orthogonal multiplexing schemes.

In millimeter-wave (mmWave) massive MIMO systems, to decrease hardware cost and energy consumption, hybrid analog and digital precoding is preferred to pure digital precoding. In this paper, a hybrid precoding method for sub-connected mmWave MIMO systems is developed. To start with, we propose to formulate the hybrid precoding matrix as a Kronecker product of an analog precoding matrix and a digital precoding vector, by enforcing phase shifters connecting to each radio-frequency chain to share the same set of coefficients. Then, the optimal design of the digital and analog precoding matrices is formulated as the nearest Kronecker product (NKP) problem, which is analytically tractable. Finally, a low-complexity algorithm is developed to implement the proposed hybrid precoding method. Simulation results corroborate that the NKP-based hybrid precoding is near-optimal and achieves higher data rates than the successive interference cancellation method. Moreover, the energy efficiency of the proposed design is much higher than the fully-connected architecture.

This paper studies the joint transceiver design and receive power splitting (PS) optimization for a multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. We aim to maximize the harvested power at the receiver under a sum mean-square error (MSE) constraint and a total transmit power constraint. By first deriving the linear minimum MSE equalization matrices, we obtain a non-convex optimization problem involving the transmit precoding matrices and the receive PS ratio. For a given PS ratio, we show that the problem can be recast as a semidefinite programming (SDP) problem, which can be solved with the interior point algorithm. Then, by employing the unimodal property of the objective function with respect to the PS ratio, we propose a Golden-section search method to find the optimal PS ratio efficiently. Further, to reduce the complexity of solving the inner SDP problems, we exploit the optimal structure of the precoding matrices to transform the original matrix-based optimization problem into a scalar-based optimization problem, for which the closed-form solution is obtained through convex optimization techniques. Simulations are provided to validate the superior performance of our proposed solutions.

As specified in Release 13 specification of the 3rd Generation Partnership Project (3GPP), narrowband physical uplink shared channel (NPUSCH) is a critical physical layer component of narrowband Internet-of-Things (NB-IoT) system. This paper designs and implements an efficient NPUSCH receiver by using modified discrete Fourier transform channel estimation and exponential moving average interpolation. Moreover, three key blocks including channel equalization, soft demodulation and soft combining are implemented for a full receive processing chain. Extensive simulation results corroborate that the designed receiver obtains lower block error rate than the benchmark specified by 3GPP.

This paper studies the joint transceiver and power splitting (PS) ratio design for a multicarrier multiple-input and multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. We present a unified optimization framework based on the minimization of a general mean square error (MSE) objective function, which includes the most commonly used criteria, such as arithmetic MSE, geometric MSE and maximum MSE minimizations. The optimal equalization matrices are first derived. Then we propose a two-layer scheme to jointly optimize the precoding matrices and the PS ratio. In the inner layer, a structural solution for the precoding matrices is derived based on the Schur-convexity/concavity of the objective function, with which the precoding optimization problems are solved by different closed-form power allocations. In the outer layer, we show that the optimized objective functions obtained by the inner-layer optimization are unimodal with respect to the PS ratio. This enables us to find the optimal PS ratio very efficiently by exploiting the Golden-section search. Simulations are provided to compare the achievable rate and error rate performances of the proposed transceiver schemes.

As of the specification Release 13 completed by the 3rd Generation Partnership Project (3GPP) in June 2016, narrowband Internet-of-Things (NB-IoT) has attracted great attention in both academia and industry. Some new features were further specified in subsequent Releases 14 and 15. In light of these specifications, efficient downlink receiver design is critical to the implementation of NB-IoT, due to the strictly limited hardware resources at a receiver. Conforming to Release 15, this paper develops an efficient downlink receiver by jointly accounting for the synchronization, channel estimation and soft combination for repetitive transmissions. Simulation results demonstrate that both the detection probability for the narrowband primary synchronization signal (NPSS) and the block error rate (BLER) for the narrowband physical downlink sharing channel satisfy the benchmarks designated by 3GPP.

Narrowband Internet of Things (NB-IoT) is a powerful technology for massive machine-type communications, which is imperative in the forthcoming 5G wireless communications. Unlike the long-time evolution (LTE) protocol, in the NB-IoT protocol specified by the third generation partnership project (3GPP), the narrowband physical random access channel (NPRACH) is newly introduced and its receiver performance is critical to the success of an NB-IoT system. In this article, an optimal activity detection scheme is first designed by using the Neyman-Pearson criterion. Then, a low-complexity iterative search algorithm is developed for the joint estimation of residual carrier frequency offset (RCFO) and timing advanced (TA), avoiding the effect of phase ambiguity. Finally, simulation results collaborate on the effectiveness and efficiency of the proposed receiver.

Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly “similar” to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user cooperation (AAUC) scheme, which avoids direct transmission to the attacked user and relies on other users for cooperative relaying. The proposed scheme only requires the eavesdropper’s angle information, and adopts an angular secrecy model to represent the average secrecy rate of the attacked system. With this angular model, the AAUC problem turns out to be nonconvex, and a successive convex optimization algorithm, which converges to a Karush-Kuhn-Tucker solution, is proposed. Furthermore, a closed-form solution and a Bregman first-order method are derived for the cases of large-scale antennas and large-scale users, respectively. Extension to the intelligent reflecting surfaces based scheme is also discussed. Simulation results demonstrate the effectiveness of the proposed successive convex optimization based AAUC scheme, and also validate the low-complexity nature of the proposed large-scale optimization algorithms.

In this paper, a novel 3D cellular model consisting of aerial base stations (aBSs) and aerial user equipments (aUEs) is proposed, by integrating the coordinated multi-point (CoMP) transmission technique with the theory of stochastic geometry. For this new 3D architecture, a tractable model for aBSs' deployment based on the binomial-Delaunay tetrahedralization is developed, which ensures seamless coverage for a given space. In addition, a versatile and practical frequency allocation scheme is designed to eliminate the inter-cell interference effectively. Based on this model, performance metrics including the achievable data rate and coverage probability are derived for two types of aUEs: i) the general aUE (i.e., an aUE having distinct distances from its serving aBSs) and ii) the worst-case aUE (i.e., an aUE having equal distances from its serving aBSs). Simulation and numerical results demonstrate that the proposed approach emphatically outperforms the conventional binomial-Voronoi tessellation without CoMP. Insightfully, it provides a similar performance to the binomial-Voronoi tessellation which utilizes the conventional CoMP scheme; yet, introducing a considerably reduced computational complexity and backhaul/ signaling overhead.

Coordinated multi-point (CoMP) transmission is a cooperating technique among base stations (BSs) in a cellular network, with outstanding capability at inter-cell interference (ICI) mitigation. ICI is a dominant source of error, and has detrimental effects on system performance if not managed properly. Based on the theory of Poisson-Delaunay triangulation, this paper proposes a novel analytical model for CoMP operation in cellular networks. Unlike the conventional CoMP operation that is dynamic and needs on-line updating occasionally, the proposed approach enables the cooperating BS set of a user equipment (UE) to be fixed and off-line determined according to the location information of BSs. By using the theory of stochastic geometry, the coverage probability and spectral efficiency of a typical UE are analyzed, and simulation results corroborate the effectiveness of the proposed CoMP scheme and the developed performance analysis.

Cloud radio access network (C-RAN) has been recognized as a promising architecture for next-generation wireless systems to support the rapidly increasing demand for higher data rate. However, the performance of C-RAN is limited by the backhaul capacities, especially for the wireless deployment. While C-RAN with fixed BS caching has been demonstrated to reduce backhaul consumption, it is more challenging to further optimize the cache allocation at BSs with multi-cluster multicast backhaul, where the inter-cluster interference induces additional non-convexity to the cache optimization problem. Despite the challenges, we propose an accelerated first-order algorithm, which achieves much higher content downloading sum-rate than a second-order algorithm running for the same amount of time. Simulation results demonstrate that, by simultaneously delivering the required contents to different multicast clusters, the proposed algorithm achieves significantly higher downloading sum-rate than those of time-division single-cluster transmission schemes. Moreover, it is found that the proposed algorithm allocates larger cache sizes to the farther BSs within the nearer clusters, which provides insight to the superiority of the proposed cache allocation.

While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.

One key feature of massive multiple-input multiple-output systems is the large number of antennas and users. As a result, reducing the computational complexity of beamforming design becomes imperative. To this end, the goal of this paper is to achieve a lower complexity order than that of existing beamforming methods, via the parallel accelerated random coordinate descent (ARCD). However, it is known that ARCD is only applicable when the problem is convex, smooth, and separable. In contrast, the beamforming design problem is nonconvex, nonsmooth, and nonseparable. Despite these challenges, this paper shows that it is possible to incorporate ARCD for multicast beamforming by leveraging majorization minimization and strong duality. Numerical results show that the proposed method reduces the execution time by one order of magnitude compared to state-of-the-art methods.

While backscatter communication emerges as a promising solution to reduce power consumption at IoT devices, the transmission range of backscatter communication is short. To this end, this work integrates unmanned ground vehicles (UGVs) into the backscatter system. With such a scheme, the UGV could facilitate the communication by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper proposes a joint graph mobility and backscatter communication model. With the proposed model, the total energy minimization at UGV is formulated as a mixed integer nonlinear programming (MINLP) problem. Furthermore, an efficient algorithm that achieves a local optimal solution is derived, and it leads to automatic trade-off between spending energy on moving versus on communication. Numerical results are provided to validate the performance of the proposed algorithm.

This paper studies a wireless-powered amplify-and- forward (AF) relaying system, where the relay harvests energy from the source or itself using an antenna switching protocol. At the first time slot, each antenna of the relay either receives information or harvests energy from the source and, then, a subset of antennas is chosen to forward information while the remaining antennas continue to harvest energy at the second time slot, which enables energy recycling. To maximize the energy efficiency of the relaying system, a mixed- integer non- linear fractional-combinatorial problem is formulated, which is however mathematically intractable. Accordingly, a suboptimal low-complexity iterative algorithm is developed, including joint power allocations at the source and relay and antenna switching at the relay. In particular, the joint power allocations is first performed by the BCD and Dinkelbach algorithms for a given antenna combination in the inner iteration while a low-complexity greedy algorithm is adopted to select the optimal antenna subset in the outer iteration. Simulation results demonstrate that energy recycling benefits higher energy efficiency.

This paper studies the end-to-end performance of dual-hop amplify-and-forward (AF) relaying systems, where the relays have no constant power supplies but can harvest energy from both the desired signal and nearby co-channel interferences (CCIs). A hybrid energy harvesting (EH) protocol, which is a combination of the existing time switching (TS) and power splitting (PS) protocols, is adopted at the relays. To enhance system performance, an opportunistic relay selection is exploited and the extreme value theory is applied to analyze the asymptotic throughput of the system. Our analysis reveals that when the number of relays (N) is sufficiently large, the system through- put scales with ln ln N. Moreover, the hybrid EH protocol is demonstrated to outperform both TS and PS protocols while it is equivalent to PS protocol in the high signal-to-noise-ratio (SNR) region.

In this paper, we consider minimum bit-error rate (BER) transceiver design for multiple-input multiple-output (MIMO) decode-and-forward (DF) relay systems employing single-carrier transmission with frequency-domain equalization (SC-FDE). The problem is formulated as the minimization of the end to-end (e2e) BER subject to a joint source and relay transmit power constraint. Since the e2e-BER is highly non-convex in terms of the complex matrix optimization variables, solving the optimization problem directly is challenging. By resorting to an upper bound on the e2e-BER and by assuming an optimal sum power budget splitting for the source and relay, we show that the problem can be reduced to the optimization of two equivalent point-to-point MIMO systems. This enables us to derive the optimal eigen-structure of the precoders and the matrix optimization problem simplifies into a convex power allocation problem involving real scalar variables. Primal decomposition is further applied to solve the resulting convex problem in a layered manner, where closed-form solutions are obtained for the inner subproblems. Simulation results are provided to confirm the BER performance of the proposed transceiver design for SC-FDE based MIMO DF relay systems.

Mobile edge computing helps healthcare Internet of Things (IoT) devices with energy harvesting provide satisfactory quality of experiences for computation intensive applications. We propose a reinforcement learning (RL)-based privacy-aware offloading scheme to help healthcare IoT devices protect both the user location privacy and the usage pattern privacy. More specifically, this scheme enables a healthcare IoT device to choose the offloading rate that improves the computation performance, protects user privacy, and saves the energy of the IoT device without being aware of the privacy leakage, IoT energy consumption, and edge computation model. This scheme uses transfer learning to reduce the random exploration at the initial learning process and applies a Dyna architecture that provides simulated offloading experiences to accelerate the learning process. A post-decision state learning method uses the known channel state model to further improve the offloading performance. We provide the performance bound of this scheme regarding the privacy level, the energy consumption, and the computation latency for three typical healthcare IoT offloading scenarios. Simulation results show that this scheme can reduce the computation latency, save the energy consumption, and improve the privacy level of the healthcare IoT device compared with the benchmark scheme.

With the development of the maritime networks, the data of vessel users is growing exponentially, and more and more resource intensive tasks, such as multimedia applications, high-definition video playback and games, appear in the daily demands. These changes have greatly increased the energy consumption and bandwidth requirements of vessel terminals and networks. In order to meet the requirements of high bandwidth and low delay for the high-speed development of mobile network, and reduce the network load, the concept of mobile edge computing (MEC) is proposed and has been widely supported by the academia and industry. It is considered to be one of the key technologies of the next generation networks. Inspired by this idea, this paper introduces computing offloading technology to maritime mobile cloud networks. Maritime mobile cloud network is the product of the continuous development of cloud computing technology and mobile Internet technology. In this paper, we studied the issue of computation task offloading for vessel terminals, focusing on minimizing the energy consumption of vessel terminals and the execution delay of computation task. First, it determines that whether if it should be offloaded to the cloud server. Second, the server should be selected to run the computation task. The goal of the optimization is to minimize the energy consumption of vessel terminals and the execution delay of computation task taking into account of different weights. To reduce the execution latency and device energy consumption, we proposed a multivessel computation offloading algorithm based on improved Hungarian algorithm in maritime MEC network. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Two-way massive MIMO amplify-and-forward relaying systems with non-ideal transceivers are investigated in this paper. To be general, multiple-antenna nodes and antenna correlation at both the user equipments (UEs) and the relay are considered, which differentiates the analysis from the prior ones. The achievable rate is analyzed and derived deterministically in closed-form. Joint scaling of the transmission powers and hardware impairments is then particularly investigated. Feasible scaling speeds for the transmission powers and hardware impairments are discovered when the number of relay antennas grows large. It is shown that down scaling of the transmission powers at the UEs and the relay and up scaling of the hardware impairment at the relay with the number of relay antennas are tolerable without reducing the expected rate. However, UE hardware impairment is a key limiting factor to the achievable rate and is not allowed to scale up with the number of relay antennas in order to achieve a non-vanishing rate. Moreover, ceiling effect on the achievable rate is still observable and the ceiling rate varies among different scaling cases. More interestingly, scalings of the UEs transmission power and the relay hardware impairment are found to be offsettable, which means that the relay hardware cost and the UE transmission power are tradable. It is found that the best tradeoff is achieved in the medium scalings of both the relay hardware impairment and UE transmission power. Numerical results are provided to verify the analysis and the tradability between the relay hardware cost and the UE transmission power. The analytical results thus provide solid foundation for flexible system designs under various cost and energy constraints.

Activity detection in machine-type communication (MTC) has been recognized as an effective way to support massive connectivity of the Internet-of-Things (IoT) devices. However, due to the sporadic traffic pattern of the MTC, only a small portion of the massive potential devices are active, making the activity detection a challenging large-scale sparsity-constrained problem. On the other hand, since the low-cost IoT devices are commonly equipped with cheap crystal oscillators, the resulting frequency offsets would intensify the multi-user interference during the activity detection and invalidate existing detection methods that are designed under ideal frequency synchronization. To fill this gap, this paper proposes two methods for activity detection under unknown frequency offsets: a Lasso-based method and a sparsity-constrained method. Both the methods are first-order algorithms, making them suitable for large-scale IoT systems. Furthermore, the sparsity-constrained method can be executed in parallel and is proved to converge to a set of critical points. The simulation results show that both the proposed methods achieve much better detection performance than a two-stage approach that separately performs frequency synchronization and activity detection. Moreover, the proposed sparsity-constrained method is shown to perform better than two competing algorithms exploiting hierarchical sparsity.

As the demand for supporting hybrid multicast and unicast services is rapidly increasing, a non-orthogonal multiplexing transmission scheme called layered-division multiplexing (LDM) has been recognized as an effective way to provide high spectrum efficiency (SE). However, high SE is not necessarily equivalent to high energy efficiency (EE). In fact, it is still unclear how much benefit LDM would provide for hybrid multicast and unicast services under EE maximization, which belongs to the more challenging class of fractional programs. To fill this gap, we formulate the problem of energy-efficient precoding design for the LDM-based multi-user multi-input-multi-output downlink system, under both multicast and unicast multi-stream data rate constraints of each user. Although the problem is nonsmooth and nonconvex, we propose a first-order algorithm for finding both the initial point and the final solution. Since the proposed first-order algorithm involves only gradient information, it achieves very low complexity. The simulation results demonstrate that, compared with the orthogonal transmission schemes, the LDM transmission under the proposed precoding can provide a much higher EE. Moreover, the proposed first-order algorithm achieves the same EE as that of a second-order based approach, but requires much shorter computation time.

Collecting data from the massive Internet of Things (IoT) devices is a challenging task since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in the IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this paper proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem, unfortunately, involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. The simulation results show that if the noise power is small (e.g., ≤-100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., -60 dBm), the UGV should spend more motion energy to get closer to the IoT users.

Maritime intelligent transportation system provides intelligent, safe and efficient maritime transport services, which greatly facilitates the applications related to monitoring, safety, infotainment and cargo online management. The Internet of Things (IoT) is especially suitable for the networked communication environment at sea, and drives the development of maritime intelligent transportation on the trend. However, the existing data processing and forwarding methods pose great challenges to Intelligent Transportation Systems (ITS). In particular, realtime multi-type of data adopts different access technologies in wireless communication systems or use the same wireless access technology but belong to different wireless carriers. Utilizing the existing multi-type wireless communication systems, the architecture of the heterogeneous network through inter-system convergence makes multi-system complement to meet the demand of mobile communication services, so as to comprehensively play their respective advantages. In this paper, we consider a multiattribute decision-making method based on Analytic Hierarchy Process (AHP) and Rough Set based on the architecture of maritime wideband communication system with software defined network (SDN) and fog computing architecture. This paper aims to select a feasible network routing scheme for this heterogeneous network, based on multi-attribute of different networks. Finally, we simulate a communication network selection case based on the future maritime communications architecture, and solve the architecture optimization problem through our proposed algorithm. The issue of such network choice necessarily exists in maritime communications architecture, and our tentative assumptions and solutions will be an important basis for such issues.

In this paper, the transmission optimization of a dual-hop decode-and-forward relaying system is investigated, where the relay capable of energy harvesting from ambient environment can work in hybrid half-duplex (HD) and/or full-duplex (FD) mode. To maximize the throughput from source to destination, the relay's working mode is optimized under the constraint of random energy arrival. In particular, upon the availability of channel state information (CSI), two cases are sequentially studied: one is that CSI is unavailable to the transmitter and the other means CSI is available to the transmitter. In the former case, a dynamic programming (DP) algorithm is proposed to find the optimal working mode of the relay; moreover, to reduce the computational complexity, a linear programming (LP)-based heuristic algorithm is developed, which performs similar to the DP algorithm. In the latter case, the optimal mode of the relay is also obtainable by the DP algorithm and an approximate DP algorithm is further developed for lower computational complexity. Simulation results demonstrate that the hybrid mode outperforms pure HD and FD modes given that self-interference is efficiently suppressed.

In multimedia-rich communication scenarios, popular contents are requested by many users. This calls for the communication system design perspective transferring from user-centric to content-centric. To realize the content-centric paradigm, one of the dominant approaches is the multi-group multicast transmission. However, different content groups may cause interference with each other, and the quality of service is difficult to be guaranteed without coordination. Fortunately, a cloud radio access network (C-RAN) perfectly fills this gap as all the computations in the network are off-loaded to the computation center, making the central coordination possible. But a major challenge that C-RAN faces is that the resultant problem size could be extremely large, invalidating many existing second-order algorithms. In this paper, content-centric sparse multicast beamforming in a large-scale C-RAN is studied. In addition to the large-scale nature, this problem is further complicated by the discontinuity and non-convexity of the cost function and constraints. Despite the challenges, a first-order algorithm is proposed. Not only is the proposed algorithm guaranteed to converge to a critical point, but its complexity order is only linear with respect to the problem size. This is in sharp contrast to the cubic order of an existing solution, making the proposed algorithm indispensable for large-scale C-RAN with hundreds or thousands of users.

To prolong the lifetime of energy constrained devices in Internet of Things, devices can harvest wireless energy from the control signal multicast from the access point. Unfortunately, hampered by the path-loss, the efficiency of such multicast wirelessly powered network is low. While large-scale antennas at access point can be used to improve the efficiency, the beamforming design problem in multicast wirelessly powered network is known to be NP-hard, and the traditional difference of convex programming becomes prohibitively time consuming in large-scale settings. On the other extreme, by using the assumption of infinite number of antennas and applying the law of large numbers, simple beamforming solution is possible. However, when applied to scenarios with finite number of antennas, the performance of such asymptotic solution is far from that of difference of convex programming. To resolve this apparent complexity-performance dilemma, this paper develops an algorithm which reduces the computation time by orders of magnitude, while still guaranteeing the same performance compared with the difference of convex programming. In particular, the proposed algorithm consists of two fast-convergent iterative procedures and is guaranteed to obtain a Karush-Kuhn-Tucker solution. Furthermore, in each iteration, the algorithm only requires the computation of inner products between channel vectors and can be run in parallel for all the users. Thus, the complexity scales linearly with the number of antennas at access point. Finally, numerical results validate the performance and the speed of the proposed scheme.

For Poisson-Delaunay triangulations in d-dimensional Euclidean space ℝd, a structured and computationally efficient form of the probability density function (PDF) of the volume of a typical cell is analytically derived in this paper. In particular, the ensuing PDF and the corresponding cumulative density function are exact and unified, applicable to spaces of arbitrary dimension (d ≥ 1). Then, the special cases and shape characteristics of the resulting PDF are thoroughly examined. Finally, various applications of the obtained distribution functions are outlined and, in particular, a novel coordinated multi-point transmission scheme based on Poisson-Delaunay triangulation is developed and the pertinent void cell effect is precisely evaluated by using the obtained distribution functions.

Depending on the actual demand of maritime security, this paper analyzes the specific requirements of video encryption algorithm for maritime monitoring system. Based on the technology of Internet of things, the intelligent monitoring system of unmanned surface vessels (USV) is designed and realized, and the security technology and network technology of the Internet of things are adopted. The USV are utilized to monitor and collect information on the sea, which is critical to maritime security. Once the video data were captured by pirates and criminals during the transmission, the security of the sea will be affected awfully. The shortcomings of traditional algorithms are as follows: the encryption degree is not high, computing cost is expensive, and video data is intercepted and captured easily during the transmission process. In order to overcome the disadvantages, a novel encryption algorithm, i.e., the improved Hill encryption algorithm, is proposed to deal with the security problems of the unmanned video monitoring system in this paper. Specifically, the Hill algorithm of classical cryptography is transplanted into image encryption, using an invertible matrix as the key to realize the encryption of image matrix. The improved Hill encryption algorithm combines with the process of video compression and regulates the parameters of the encryption process according to the content of the video image and overcomes the disadvantages that exist in the traditional encryption algorithm and decreases the computation time of the inverse matrix so that the comprehensive performance of the algorithm is optimal with different image information. Experiments results validate the favorable performance of the proposed improved encryption algorithm.

In this paper, the performance of an underlay multiple-input multiple-output (MIMO) cognitive radio system is analytically studied. In particular, the multiple antennas of the secondary transmitter operate in a spatial multiplexing transmission mode, while a zero-forcing (ZF) detector is employed at the secondary receiver. Additionally, the secondary system is interfered by multiple randomly distributed single-antenna primary users (PUs). To enhance the performance of secondary transmission, optimal power allocation is performed at the secondary transmitter with a constraint on the interference temperature (IT) specified by the PUs. To mitigate instantaneous excessive interference onto PUs caused by the time-average IT, an iterative antenna reduction algorithm is developed for the secondary transmitter and, accordingly, the average number of transmit antennas is analytically computed. Extensive numerical and simulation results corroborate the effectiveness of our analysis.

The performance of reactive decode-and-forward multi-branch relaying in the presence of co-channel interference and Nakagami fading is analytically investigated. Intermediate relays that successfully decode the received signals from the source node form a decoding set, from which the relay whose corresponding branch results in the highest signal-to-interference-plus-noise ratio (SINR) at the destination node is chosen to serve as the best relay. The selected relay re-encodes the source message and forwards it to the destination while the remaining relays keep idle. For this relaying scheme, we first obtain the exact end-to-end SINR expression by considering the general case of Nakagami-m fading channels. Then, the exact unconditional probability density function (PDF) of the end-to-end SINR is explicitly derived. With the resulting PDF, exact closed-form expressions for the outage and error probabilities are obtained. Moreover, to gain insights into the system performance, asymptotic analysis of the error probability is performed. Finally, Monte-Carlo simulation results are presented to corroborate the analysis, and comparative numerical results are discussed.

In this paper, we consider the robust transceiver design for a single-carrier frequency-domain equalization (SC-FDE) based two-way amplify-and-forward (AF) relay system with imperfect channel state information (CSI). We formulate the optimization problem for the relay filter and destination equalizers as the maximization of the achievable bit rate (ABR) of the system subject to a relay transmit power constraint. Due to the lack of analytical tractable expression for the system ABR under imperfect CSI, solving the optimization problem directly is challenging. Thereby, a lower bound on the link ABR is first derived and adopted in the objective function. Based on the lower bound, the optimal equalizers at the two terminal nodes can be determined in closed form. Subsequently, the optimization of the relay filter is transformed into a convex power allocation problem and an efficient algorithm is proposed to find its global optimal solution. Numerical results are provided to confirm the ABR performance of the proposed robust two-way AF relaying strategy.

Energy harvesting (EH) is a promising technique for cognitive relaying transmission (CRT) where secondary users (SUs) and relaying nodes do not have a constant power supply each. Unlike conventional CRT where the end‐to‐end data rate is usually maximised without taking into account the energy consumption at the source and relay, in this study, the energy consumption is characterised by means of energy efficiency, defined as the achievable data rate per Joule. In particular, the energy states at each node (either at a SU or a relay) is modelled as a finite‐state Markov chain and the transmit power at a node is optimally allocated by jointly accounting for the interference threshold prescribed by primary users (PUs), the maximum allowable transmit power and the harvested energy at the node. To maximise the energy efficiency, a best relay selection criterion is proposed and the subsequent optimal transmit power allocation is initially formulated as a non‐linear fractional programming problem and, then, equivalently transformed into a parametric programming problem and, finally, solved analytically by using the classic Karush–Kuhn–Tucker conditions. With extensive Monte‐Carlo simulation results, the effectiveness of the proposed relay selection algorithm and corresponding optimal power allocation strategy are corroborated.

In this paper, outage performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) is analyzed. Unlike prior analyses, time-correlated Nakagami-m fading channel is considered. The outage analysis thus involves the probability distribution analysis of a product of multiple correlated shifted Gamma random variables and is more challenging than prior analyses. Based on the findings of the conditional independence of the received signal-to-noise ratios, the outage probability is exactly derived by using conditional Mellin transform. Specifically, the outage probability of HARQ-IR under time-correlated Nakagami-m fading channels can be written as a weighted sum of outage probabilities of HARQ-IR over independent Nakagami fading channels, where the weightings are determined by a negative multinomial distribution. This result enables not only an efficient truncation approximation of the outage probability with uniform convergence but also asymptotic outage analysis to further extract clear insights, which have never been discovered for HARQ-IR even under fast fading channels. The asymptotic outage probability is then derived in a simple form, which clearly quantifies the impacts of transmit powers, channel time correlation, and information transmission rate. It is proved that the asymptotic outage probability is an inverse power function of the product of transmission powers in all HARQ rounds, an increasing function of the channel time correlation coefficients, and a monotonically increasing and convex function of information transmission rate. The simple expression of the asymptotic result enables optimal power allocation and optimal rate selection of HARQ-IR with low complexity. Finally, numerical results are provided to verify our analytical results and justify the application of the asymptotic result for optimal system design.

While two-way communication can improve the spectral efficiency of wireless networks, distances from the relay to the two users are usually asymmetric, leading to excessive wireless energy at the nearby user. To exploit the excessive energy, energy harvesting at user terminals is a viable option. Unfortunately, the exact gain brought by wireless power transfer (WPT) in two-way communication is currently unknown. To fill this gap, in this paper, the achievable rate region of wirelessly powered two-way communication with a fixed relay is derived. Not only this newly established result is shown to enclose the existing achievable rate region of two-way relay channel without energy harvesting but also the gain is precisely quantified. On the other hand, it is well-known that a major obstacle to WPT is the path-loss. By endowing the relay with mobility, the distances between the relay and users can be varied, thus providing a potential solution to combat pathloss at the expense of energy for transmission. To characterize the consequence brought by such a scheme, a pair of inner and outer bounds to the achievable rate region of wirelessly powered two-way communication under a mobile relay is further derived. By comparing the exact achievable rate region for the fixed relay case and the achievable rate bounds for the mobile relay case, it is possible to quantify the relative advantage of spending energy on moving versus on transmission in wirelessly powered two-way communication.

In this paper, we consider the joint optimization of relay transmit-receive beamformers, users' transmit powers, and users' power splitting ratios in wirelessly-powered two-way relay channel under data-rate quality-of-service constraints. In order to solve the problem, we first establish that the uplink data-rate constraints would be active at the global optimum. Then we transform it into an equivalent problem by introducing slack variables and applying the linear matrix inequalities. Based on the transformed problem, the global optimal solution is derived. Numerical results on network power consumption versus circuit power and data-rate QoS show that the proposed algorithm outperforms existing algorithms.

When energy harvesting (EH) technique is applied in Internet of Things (IoT) to replenish energy for low power consumption sensing nodes, e.g., sensors and radio frequency identification (RFID) tags, the end-to-end (e2e) data rate is usually maximized without accounting for the energy consumption at the nodes. In this paper, however, the energy consumption at secondary users (SUs) along a cognitive relaying link is characterized by means of energy efficiency, defined as the achievable data rate per Joule. In particular, the energy states at each node is modelled as a finite-state Markov chain and the transmit power at a node is optimally allocated by jointly accounting for the interference threshold prescribed by primary users (PUs), the maximum allowable transmit power and the harvested energy at the node. To maximize the energy efficiency, a best relay selection criterion is proposed and the subsequent optimal transmit power allocation is initially formulated as a nonlinear fractional programming problem and, then, equivalently transformed into a parametric programming problem and, finally, solved analytically by using the classic Karush-Kuhn-Tucker (KKT) conditions. With extensive Monte-Carlo simulation results, the effectiveness of the proposed relay selection algorithm and corresponding optimal power allocation strategy are corroborated, in terms of the energy efficiency of SUs.

This paper analyzes the spectral efficiency of massive multiple-input multiple-output (MIMO) systems in both centralized and distributed configurations, referred to as C-MIMO and D-MIMO, respectively. By accounting for real environmental parameters and antenna characteristics, namely, path loss, shadowing effect, multipath fading, and antenna correlation, a novel comprehensive channel model is first proposed in closed-form, which is applicable to both types of MIMO schemes. Then, based on the proposed model, the asymptotic behavior of the spectral efficiency of the MIMO channel, under both the centralized and distributed configurations is analyzed and compared in exact forms, by exploiting the theory of very long random vectors. Afterwards, a case study is performed by applying the obtained results into MIMO networks with circular coverage. In such a case, it is attested that for the D-MIMO of cell radius rcand circular antenna array of radius ra,the optimal value of rathat maximizes the average spectral efficiency is accurately established by raopt= rc/1.31. Monte Carlo simulation results corroborate the developed spectral-efficiency analysis.

In coverage areas overlapped by the mobile satellite system (MSS) and the ancillary terrestrial component (ATC) of integrated MSS/ATC networks, users can suffer severe co-channel interference (CCI) due to the coexistence of MSS and ATC signals. The vast difference between the characteristics of the desired channels for a user, depending on whether it is connected to the satellite or to the terrestrial station, makes the corresponding performance evaluation very challenging. This paper tackles this issue by using the powerful generalized-K distribution, and offers a unified closed-form analysis for the system performance of both types of connections. In particular, it is revealed that the user's diversity gain depends only upon the minimum between the fading parameter and the shadowing parameter of the desired channel, regardless of the CCI. Also, the coding gain increases with the diversity gain. The effectiveness of the analysis is corroborated by Monte Carlo simulations.

To account for antenna physical parameters and real propagation conditions encountered by large-scale multipleinput multiple-output (MIMO) antenna systems in practical deployment, this paper develops a comprehensive MIMO channel model in an analytical way. In particular, major parameters including path loss, shadowing effect, multi-path fading, channel polarization, channel correlations, antenna cross-polarization discrimination and environmental cross-polar coupling, are integrated in a mathematically tractable way. Then, an upper bound on the ergodic capacity of the comprehensive MIMO channel is derived asymptotically, i.e. as the number of transmit and/or receive antennas of the MIMO system approaches infinity. Finally, Monte Carlo simulation results corroborate the effectiveness of the proposed model and the accuracy of the resulting capacity bound. Thanks to its high generality and compactness, the proposed model can serve as the kernel for the design and performance evaluation of large-scale MIMO in real propagation environments.

Far-field wireless power transfer (WPT) is a promising technique to resolve the painstaking power-charging of wireless user terminals (UTs). However, the main issue hindering the implementation of this technique in practice is its limited efficiency. This paper aims to improve WPT efficiency in terms of the time-average direct current (DC) output power at UTs in support of simultaneous data and power transfer. In particular, the power scaling law when opportunistic scheduling strategy is performed among N UTs is analytically attained, by using the extreme value theory. Our results reveal that, the opportunistic technique has a scheduling gain of N times that of the round-robin scheduling policy, thereby improving the power transfer efficiency significantly.

This paper develops a general and unified multi-input multi-output (MIMO) channel model applicable to both mobile satellite systems (MSS) and ancillary terrestrial component (ATC) links of integrated MSS with ATC. Major channel parameters pertaining to the MSS and ATC links, such as large-scale path loss, shadowing effect, small-scale multi-path fading, satellite elevation angle, channel polarization and temporal correlations, antenna cross-polarization discrimination and environment cross-polar coupling, are all taken into account in a compact, flexible and fully parameterized way. Moreover, a step-by-step methodology used for Monte-Carlo simulation of the proposed channel model is explicitly provided. Further, numerical results illustrating the effects of several channel parameters are presented. The proposed model is general and constitutes a fundamental kernel for the design and performance evaluation of MSS-ATC networks, and is also suitable for the modeling of other hybrid networks such as heterogeneous networks.

In spectrum-sharing cognitive radio systems, the transmit power of secondary users has to be very low due to the restrictions on the tolerable interference power dictated by primary users. In order to extend the coverage area of secondary transmission and reduce the corresponding interference region, multi-hop amplify-and-forward (AF) relaying can be implemented for the communication between secondary transmitters and receivers. This paper addresses the fundamental limits of this promising technique. Specifically, the effect of major system parameters on the performance of spectrum-sharing multi-hop AF relaying is investigated. To this end, the optimal transmit power allocation at each node along the multi-hop link is firstly addressed. Then, the extreme value theory is exploited to study the limiting distribution functions of the lower and upper bounds on the end-to-end signal-to-noise ratio of the relaying path. Our results disclose that the diversity gain of the multi-hop link is always unity, regardless of the number of relaying hops. On the other hand, the coding gain is proportional to the water level of the optimal water-filling power allocation at secondary transmitter and to the large-scale path-loss ratio of the desired link to the interference link at each hop, yet is inversely proportional to the accumulated noise, i.e. the product of the number of relays and the noise variance, at the destination. These important findings do not only shed light on the performance of the secondary transmissions but also benefit system designers improving the efficiency of future spectrum-sharing cooperative systems.

In this contribution, two-way cooperative amplify-and-forward (AF) relaying technique is integrated into spectrumsharing wireless systems to improve spectral efficiency of secondary users (SUs). In order to share the available spectrum resources originally dedicated to primary users (PUs), the transmit power of a SU is optimized with respect to the average tolerable interference power at primary receivers. By analyzing outage probability and achievable data rate at the base station and at a cell-edge SU, our results reveal that the uplink performance is dominated by the average tolerable interference power at primary receivers, while the downlink always behaves like conventional one-way AF relaying and its performance is dominated by the average signal-to-noise ratio (SNR). These important findings provide fresh perspectives for system designers to improve spectral efficiency of secondary users in next-generation broadband spectrum-sharing wireless systems.

In this paper, we consider the robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems. THP is adopted at the source to mitigate the spatial inter-symbol interference and then a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. Based on the elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the optimization problem is greatly simplified and can be efficiently solved. Finally, the performance advantage of the proposed robust design is assessed by simulation results.

When analyzing system performance of conventional one-way relaying or advanced two-way relaying, these two techniques are always dealt with separately and, thus, their performance cannot be compared efficiently. Moreover, for ease of mathematical tractability, channels considered in such studies are generally assumed to be subject to Rayleigh fading or to be Nakagami-m channels with integer fading parameters, which is impractical in typical urban environments. In this paper, we propose a unified moments-based framework for general performance analysis of channel-state-information (CSI) assisted amplify-and-forward (AF) relaying systems. The framework is applicable to both one-way and two-way relaying over arbitrary Nakagami-m fading channels, and it includes previously reported results as special cases. Specifically, the mathematical framework is firstly developed under the umbrella of the weighted harmonic mean of two Gamma-distributed variables in conjunction with the theory of Padé approximants. Then, general expressions for the received signal-to-noise ratios of the users in one-way/two-way relaying systems and the corresponding moments, moment generation function, and cumulative density function are established. Subsequently, the mathematical framework is applied to analyze, compare, and gain insights into system performance of one-way and two-way relaying techniques, in terms of outage probability, average symbol error probability, and achievable data rate. All analytical results are corroborated by simulation results as well as previously reported results whenever available, and they are shown to be efficient tools to evaluate and compare system performance of one-way and two-way relaying.

In this paper, robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems is investigated. At source node, THP is adopted to mitigate the spatial intersymbol interference. However, due to its nonlinear nature, THP is very sensitive to channel estimation errors. In order to reduce the effects of channel estimation errors, a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. With novel applications of elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the transceiver design problem reduces to a much simpler one with only scalar variables which can be efficiently solved. Finally, the performance advantage of the proposed robust design over non-robust design is demonstrated by simulation results.

Since the electromagnetic spectrum resource is becoming more and more scarce, improving spectral efficiency is becoming extremely important for the sustainable development of wireless communication systems and services. Integrating cooperative relaying techniques into spectrum-sharing cognitive radio systems sheds new light on higher spectral efficiency. In this paper, we analyze the end-to-end performance of cooperative amplify-and-forward (AF) relaying in spectrum-sharing systems. In order to achieve the optimal end-to-end performance, the transmit powers of the secondary source and the relays are optimized with respect to average interference power constraints at primary users and Nakagami-m fading parameters of interference channels (for mathematical tractability, the desired channels from secondary source to relay and from relay to secondary destination are assumed to be subject to Rayleigh fading). Also, both partial and opportunistic relay-selection strategies are exploited to further enhance system performance. Based on the exact distribution functions of the end-to-end signal-to-noise ratio (SNR) obtained herein, the outage probability, average symbol error probability, diversity order, and ergodic capacity of the system under study are analytically investigated. Our results show that system performance is dominated by the resource constraints and it improves slowly with increasing average SNR. Furthermore, larger Nakagami-m fading parameter on interference channels deteriorates system performance slightly. On the other hand, when interference power constraints are stringent, opportunistic relay selection can be exploited to improve system performance significantly. All analytical results are corroborated by simulation results and they are shown to be efficient tools for exact evaluation of system performance

For cooperative amplify-and-forward (AF) relaying in spectrum-sharing wireless systems, secondary users share spectrum resources originally licensed to primary users to communicate with each other and, thus, the transmit power of secondary transmitters is strictly limited by the tolerable interference powers at primary receivers. Furthermore, the received signals at a relay and at a secondary receiver are inevitably interfered by the signals from primary transmitters. These co-channel interferences (CCIs) from concurrent primary transmission can significantly degrade the performance of secondary transmission. This paper studies the effect of CCIs on outage probability of the secondary link in a spectrum-sharing environment. In particular, in order to compensate the performance loss due to CCIs, the transmit powers of a secondary transmitter and its relaying node are respectively optimized with respect to both the tolerable interference powers at the primary receivers and the CCIs from the primary transmitters. Moreover, when multiple relays are available, the technique of opportunistic relay selection is exploited to further improve system performance with low implementation complexity. By analyzing lower and upper bounds on the outage probability of the secondary system, this study reveals that it is the tolerable interference powers at primary receivers that dominate the system performance, rather than the CCIs from primary transmitters. System designers will benefit from this result in planning and designing next-generation broadband spectrum-sharing systems.

Aiming to achieve the sum-rate capacity in multi-user multi-antenna systems where Ntantennas are implemented at the transmitter, opportunistic beamforming (OBF) generates Ntorthonormal beams and serves Ntusers during each channel use, which results in high scheduling delay over the users, especially in densely populated networks. Non-orthogonal OBF with more than Nttransmit beams can be exploited to serve more users simultaneously and further decrease scheduling delay. However, the inter-beam interference will inevitably deteriorate the sum-rate. Therefore, there is a tradeoff between sum-rate and scheduling delay for non-orthogonal OBF. In this context, system performance and implementation of non-orthogonal OBF with N >; Nt beams are investigated in this paper. Specifically, it is analytically shown that non-orthogonal OBF is an interference-limited system as the number of users K → ∞. When the inter-beam interference reaches its minimum for fixed Ntand N, the sum-rate scales as N In (N/(N-Nt)) and it degrades monotonically with the number of beams N for fixed Nt. On the contrary, the average scheduling delay is shown to scale as1/NK ln K channel uses and it improves monotonically with N. Furthermore, two practical non-orthogonal beamforming schemes are explicitly constructed and they are demonstrated to yield the minimum inter-beam interference for fixed Ntand N. This study reveals that, if user traffic is light and one user can be successfully served within a single transmission, non-orthogonal OBF can be applied to obtain lower worst-case delay among the users. On the other hand, if user traffic is heavy, non-orthogonal OBF is inferior to orthogonal OBF in terms of sum-rate and packet delay.

In this paper, linear beamforming design for uplink amplify-and-forward relaying cellular networks, in which multiple mobile terminals rely on one relay station to communicate with the base station, is investigated. In particular, the base station, relay station and mobile terminals are all equipped with multiple antennas. Based on linear minimum mean-square-error (LMMSE) criterion and exploiting a hidden convexity in the problem, the precoder matrices at multiple mobile terminals, forwarding matrix at relay station and equalizer matrix at base station are jointly designed. Furthermore, several existing linear beamforming designs for multi-user (MU) MIMO systems and AF MIMO relaying systems can be considered as special cases of the proposed solution. Simulation results are presented to demonstrate the performance advantage of the proposed algorithm.

Aiming to achieve the sum-rate capacity in multi user multi-input multi-output (MIMO) channels with Ntantennas implemented at the transmitter, opportunistic beamforming (OBF) generates Ntorthonormal beams and serves Nt users during each transmission, which results in high scheduling delay over the users, especially in densely populated wireless networks. Non-orthogonal OBF with more than Nttransmit beams can be exploited to serve more users simultaneously and further decreases scheduling delay. However, the inter-beam interference will inevitably deteriorate the sum-rate. Therefore, there is a tradeoff between the sum-rate and the increasing number of transmit beams. In this context, the sum-rate of non-orthogonal OBF with N >; Ntbeams are studied, where the transmitter is based on the Grassmannian beamforming. Our results show that non-orthogonal OBF is an interference-limited system. Moreover, when the inter-beam interference reaches its minimum for fixed Nt and N, the sum-rate scales as N ln (N/N-Nt) and it decreases monotonically with N for fixed Nt. Numerical results corroborate the accuracy of our analyses.

Relay transmission is promising for future wireless systems due to its significant cooperative diversity gain. The performance of dual-hop semi-blind amplify-and-forward (AF) relaying systems was extensively investigated, for transmissions over Rayleigh fading channels or Nakagami-m fading channels with integer fading parameter. For the general Nakagami-m fading with arbitrary m values, the exact closed-form system performance analysis is more challenging. In this paper, we explicitly derive the moment generation function (MGF), probability density function (PDF) and moments of the end-to-end signal-to-noise ratio (SNR) over arbitrary Nakagami-m fading channels with semi-blind AF relay. With these results, the system performance evaluation in terms of outage probability, average symbol error probability, ergodic capacity and diversity order, is conducted. The analysis developed in this paper applies to any semi-blind AF relaying systems with fixed relay gain, and two major strategies for computing the relay gain are compared in terms of system performance. All analytical results are corroborated by simulation results and they are shown to be efficient tools to evaluate system performance.

The zero-padded block transmission with linear time-domain pre-equalizer is studied in this paper. A matched filter is exploited to guarantee the stability of the zero-forcing (ZF) and minimum mean square error (MMSE) pre-equalization. Then, in order to compute the pre-equalizers efficiently, an asymptotic decomposition is developed for the positive-definite Hermitian banded Toeplitz matrix. Compared to the direct matrix inverse methods or the Levinson-Durbin algorithm, the computational complexity of the proposed algorithm is significantly decreased and there is no bit error rate degradation when data block length is large.

Abstract A cross‐level pre‐RAKE combining (PRC) scheme for time hopping pulse amplitude modulation ultra wideband (TH‐PAM UWB) transmitter is studied in this paper. A two‐stage cross‐level PRC (CL‐PRC) scheme is proposed. The conventional PRC schemes suppress all the chip‐wise interference. However, the proposed scheme suppresses only the specific frame‐wise inter‐symbol interference (ISI) by exploiting the characteristic that the information bits are transmitted only at ultra short time slots. This results in a low complexity pre‐equalizer without bit error rate (BER) performance degradation. Furthermore, an order selection rule is presented to achieve the tradeoff between signal‐to‐interference ratio (SIR) and computational complexity. Simulation results illustrate the superior SIR and BER performance of our proposal. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, the uplink of a multi-user distributed antenna system (DAS) with antenna elements deployed on a circle is investigated. We address the problem of calculating the sum-rate capacity with per-user power constraints. Based on system scale-up, we derive a good approximation of the sum-rate capacity by adopting random matrix theory. We also propose an iterative method to calculate the unknown parameters in the approximation. The approximation is illustrated to be quite accurate and the iterative method is verified to be quite efficient by Monte Carlo simulations.

In this paper, the downlink performance of distributed antenna systems (DAS) with random antenna layout is investigated. We consider the composite channel including large-scale fading and small-scale fading. When the large-scale channel state information, which usually varies slowly and is easy to be obtained, is available at the transmitter, the problem of power allocation among distributed antennas with the target of downlink capacity maximization is formulated. Based on system scale-up, we derive a precise approximation of the downlink ergodic capacity by adopting random matrix theory. We also propose an iterative method to calculate the unknown parameter in the approximation. Moreover, the approximation is proved to be concave on the transmit powers of the distributed antennas. Consequently, a simple sub-optimal power allocation scheme is proposed, with which the system capacity is illustrated to be quite close to the optimal one obtained by numerical optimizations.

In this paper, the downlink performance of a distributed antenna system (DAS) with random antenna layout is investigated. We address the problem of characterizing the downlink capacity with the generalized assumptions: (al) per distributed antenna power constraint, (a2) generalized mobile terminals equipped with multiple antennas, (a3) a multi-cell environment. Based on system scale-up, we derive a good approximation of the ergodic downlink capacity by adopting random matrix theory. We also propose an iterative method to calculate the unknown parameter in the approximation. The approximation is illustrated to be quite accurate and the iterative method is verified to be quite efficient by Monte Carlo simulations.

Different from the opportunistic beamforming system (OBS) randomizing both amplitude and phase of its beamforming vector/matrix, the opportunistic cophasing system (OCS) randomizes the phase only. It significantly reduces the demands on the dynamic range of power amplifiers and even can be implemented just by a set of phase shifters. We first address the single-beam OCS in terms of its analytical system throughput. Then, we design a multi-beam OCS by exploiting the Fourier matrix and finally, we derive its closed-form upper bound on throughput, which is very tight with the Monte Carlo simulation results.

This paper presents a timing synchronization method with shift-orthogonal constant amplitude zero auto correlation (CAZAC) sequences for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Utilizing the unique properties of CAZAC sequences, at the receivers, unit pulses can be obtained by the symmetrical correlation to detect time offsets and differentiate inter-transmitter delays (ITDs). The performance of the proposed method is compared with traditional methods at different values of ITDs. Simulations demonstrate that the proposed CAZAC sequence-based method provides high accuracy in detecting the different time offsets caused by the distributed transmitters of the MIMO OFDM systems.

In orthogonal frequency division multiplexing (OFDM) systems, time- and frequency-selective (or doubly-selective) fading leads to the loss of subcarrier orthogonality and the occurrence of inter-carrier interference (ICI), which increases an irreducible error floor in proportional to the normalized Doppler frequency offset. Channel estimation (CE) in rapidly time-varying multi-path scenarios is critical for ICI cancellation and coherent demodulation. In this paper, we introduce an iterative CE scheme to estimate time-varying channel parameters and a low-complexity equalization method to cancel ICI and detect data. The proposed CE technique performs an initial CE based on a piece-wise linear model. Then ICI is reconstructed and cancelled from the received signals. Estimating channel again by using signals of less interference, refined CE can be obtained. Meanwhile, a low-complexity equalizer is proposed to further improve BER performance. Finally, simulation results show good performance of the proposed CE method in doubly-selective fading channels.

In this paper, we propose an accurate and efficient timing synchronization method for distributed multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in both AWGN and multi-path Rayleigh fading channels. A modified cyclic delay synchronization pattern (MCDSP) is proposed and it is verified to be especially suitable for distributed system which needs to separate the signal arriving time of each transmit antenna. At the receiver, two additional methods termed antenna cluster separation window (ACSW) and multi-path backward searching (MPBS) are employed to combat the multi-path effect.

A joint timing synchronization and channel estimation algorithm is proposed for orthogonal frequency division multiplexing (OFDM) system. In the proposed scheme, the shift delay characteristic of synchronization sequence is revealed in channel estimation process. Through utilizing this characteristic, the correct symbol timing offset (STO) is jointly optimized with channel estimation via the minimum mean square error (MMSE) criterion. Simulation results demonstrate that the proposed scheme could bring almost ideal performance improvement for both channel and timing offset estimation.

To avoid poor efficiency of the power amplifiers, the opportunistic cophasing communication system randomises only the phase of beamforming vector elements, instead of randomising both amplitude and phase. The opportunistic cophasing system with multi-beam transmission is focused. The cophasing matrix with a rotation matrix is first constructed. Then, the upper and lower bounds of asymptotic system throughput are analytically derived, based on the theory of order statistics. Finally, simulation results demonstrate the effectiveness of the proposed method in terms of system throughput.