The integration of movable antenna (MA) technique into unmanned aerial vehicle (UAV) communications offers a promising solution to reliable and energy-efficient non-terrestrial networking for low-altitude economy. To characterize multi-MA-assisted UAV-to-UAV wideband fading channels, we propose a three-dimensional arbitrary-elevation two-concentric-cylinders reference model. Based on this model, we derive the space-time-frequency correlation function (STF-CF) in closed form. From the STF-CF, we also obtain the space-Doppler power spectral density (SD-PSD) and the power space-delay spectrum (PSDS). The excellent agreement between the theoretical PSDS and some previously reported measurement data demonstrates the utility of the reference model. We then establish corresponding simulation models, which produce consistent results with the derived expressions. By leveraging the closed-form correlation function, the gradient of the log-determinant of spatial correlation matrix with respect to the MA positions can be conveniently obtained, which may serve as a method to maximize the ergodic capacity of the multi-MA assisted wideband UAV-to-UAV channels.

Reconfigurable antennas (RAs) can actively adjust their radiation patterns to meet the demands of the communication network. In this paper, we investigate precoding and radiation pattern design in a multi-user multi-input single-output (MUMISO) downlink system. To fully exploit RAs, we introduce pattern sample vectors corresponding to each user's sub channel to assess the impact of pattern reconfiguration across all scattering paths. Based on this, we formulate the design problem by adopting closed-form precoding to optimize the radiation patterns for maximizing the sum rate. Specifically, we propose two methods: a heuristic approach using singular value optimization (SVO) and a successive convex approximation (SCA)-based minimization strategy to achieve desired patterns. These methods effectively address the non-convexity in the objective function. Numerical results demonstrate that the reconfigurable radiation pattern can strategically manipulate the wireless channel, leading to significant enhancements in system performance.

Simultaneous perception of human behavior and location in indoor environments is a significant challenge. Multi-task Learning (MTL) frameworks have been shown to effectively address this problem by leveraging correlations between tasks. However, traditional MTL models usually rely on a fixed parameter sharing mechanism, which can limit model learning capabilities and lead to substantial accuracy variations across tasks. To address these issues, we propose a novel joint perception framework that utilizes Channel State Information (CSI) finger-printing. First, we introduce a selective sharing method based on sparse parameters to mitigate the problems associated with fixed parameter sharing in MTL. This approach dynamically allocates shared parameters according to the specific needs of each task, thereby enhancing the flexibility of the model. Second, to further balance the model performance between two tasks, we introduce an adaptive loss weight adjustment approach. This approach dynamically adjusts the loss weights based on the performance of each task, ensuring good accuracy for both behavior recognition and location estimation. Experimental results demonstrate that our proposed framework significantly enhances accuracy in both behavior recognition and location estimation.

This paper designs a parallel solution framework for constructive interference based symbol-level precoding (CI-SLP) in the downlink of a multi-user multiple-input single-output (MU-MISO) system. Most existing works on SLP have considered the sum-power constraint, while in practical systems each transmit antenna is equipped with its dedicated power amplifier. Therefore, it is more realistic to design SLP approaches that incorporate the per-antenna power constraint (PAPC). In this paper, we focus on two specific PAPC-based problems: the constructive interference per-antenna power constraint signal to interference plus noise ratio (SINR) balancing (CI-PASB) problem and the constructive interference per-antenna peak power minimization (CI-PAPM) problem. Similar to sum-power constraint, for the CI-PASB problem, we demonstrate that it is separable, allowing the existing parallel proximal Jacobian alternating direction method of multipliers (PJ-ADMM) algorithm to be directly used. As for the CI-PAPM problem, although it is unseparable, we can leverage the established duality to obtain its solution based on the solution of the corresponding CI-PASB problem. Numerical results verify our proposed parallel methods and show that they are more efficient than the existing centralized schemes, which showcases the advantages of parallel computing and promotes the implementation of CI precoding under practical PAPC scenarios.

This paper presents a graph signal-based approach for channel spatial extrapolation to tackle the increasing challenge of obtaining precise channel estimates, especially in large-scale data contexts. The objective of this method is to infer channel characteristics for the entire scenario by extrapolating locally observed channel data. Graph signals are constructed based on spatial locations, and smooth, low-rank spatio-temporal features are employed to capture spatial consistency principles and scatterer distributions. This strategy transforms the channel spatial extrapolation issue into a graph signal sampling recovery model. The proposed solution leverages the Alternating Direction Method of Multipliers (ADMM) algorithm. Furthermore, to alleviate computational overhead, we introduce an ADMM-Net framework, which integrates model-driven neural networks, thus reducing computation time without compromising accuracy. Simulation results demonstrate a significant decrease in extrapolation errors for the optimized model compared to traditional interpolation methods, particularly in scenarios with limited channel information.

In this paper, we propose distributed symbol-level precoding (SLP) schemes to manage inter-cell interference in downlink multi-user multi-cell coordination systems. Initially, we introduce the constructiveness as a novel metric to quantify the received signal quality. We then define the square of the constructiveness-over-noise ratio (CoNR) to represent the equivalent signal-to-noise ratio (SNR). Subsequently, we propose a distributed precoding scheme where each base station (BS) aims to maximize the CoNR obtained by the worst user within its cell while ensuring inter-cell interference remains constructive. To further manage interference, we propose an additional distributed precoding scheme, where each BS enhances overall system performance by maximizing the sum of the minimum constructiveness from both intra-cell and inter-cell interference. Simulation results demonstrate that our proposed distributed precoding schemes surpass traditional block-level precoding in performance.

WiFi-sensing systems that can accomplish multiple tasks simultaneously are attracting significant attention due to their potential for large-scale commercial applications. However, different WiFi sensing scenarios may often rely on various task-specific features, posing a challenge in balancing these different, or asymmetrical, characteristics across tasks. In this paper, we propose a system that aims to address the asymmetrical problems in the joint recognition of users’ locations and activities. First, we define activity recognition as a high-level task and location recognition as a low-level task based on their respective difficulty levels. Then, the proposed system employs cascading attention-based modules to transfer prior knowledge between different tasks. The key insight of the proposed architecture is to mimic skilled learners in similar situations, who often tackle easier problems first to enable them to solve more challenging problems later on. Based on this behavioral strategy, the proposed attention-based modules are designed to generate masks that select specific characteristics from the low-level task to help the high-level task learn respective features more effectively. Finally, extensive experimental results based on two open datasets demonstrate the superiority of our system in accuracy compared to other state-of-the-art methods.

In multicell cellular networks, coordinated multiuser scheduling (CMUS) based on block diagonalization precoding, which jointly selects the scheduled users among multiple base stations (BSs), is an efficient method to eliminate inter-user interference. However, most existing CMUS algorithms require global channel state information and many iterations, which are impractical in dynamic wireless networks due to its heavy information overhead and computational complexity. In this paper, we propose a distributed adaptive CMUS algorithm based on multi-agent reinforcement learning (MARL) to maximize the sum rate with less information overhead and computational complexity. In the proposed scheme, an adaptive multiple Deep Q-Network (DQN) architecture is deployed at each BS, aiming to reduce information overhead and computation complexity by identifying suitable CMUS policies with fewer DQNs. Each BS trains its own multi-DQNs and performs appropriate CMUS actions based on local information. Simulation results show that, by executing a subset of the DQNs, the proposed adaptive multi- DQN architecture reduces at least 50% of the information overhead and the computational complexity of the scheme without adaptive multi-DQN architecture. Additionally, compared to the centralized iterative approach, the proposed scheme costs 9.44% of the information overhead and 3.33%0 of the running time of the centralized iterative approach while achieving marginally superior performance.

Recently, fingerprint-based localization has become a promising solution for indoor positioning because of its great performance in complex multipath environments. However, the extensive time and labor effort of constructing the radio map has become the bottleneck that hinders the adaptation of fingerprint-based localization in practice. In this article, we propose a novel cost-efficient radio map construction scheme, which relies on the fingerprint measurements from only a small number of reference points (RPs) via graph signal sampling and recovery techniques. First, using the topological characteristics of RPs, we model the radio map as a graph and design the angle fingerprint for the band-limited graph signal. Subsequently, the radio map is built based on graph clustering, sampling set selection and signal recovery. Extensive simulations are performed in a geometry-based ray tracing signal propagation model, which demonstrates that the proposed method can recover the radio map with low-collection cost and outperform existing solutions in terms of fingerprint accuracy and localization performance.

Indoor location-based services (LBSs) are critical for enhancing social and commercial activities that require accurate and efficient localization techniques. Existing deep-learning-based indoor localization methods mainly focus on predefined global features to learn local discriminative representations, which increases learning difficulty and is not efficient or robust to scenarios with small variations. To address the above issues, we propose a novel fine-grained temporal features-based localization (FT-Loc) framework that utilizes multiple subsignal features to provide accurate location estimation, and each subsignal represents a piece of clue for a specific position. Specifically, the proposed framework takes multiple local signal sequences as input, and deep networks considering temporal correlations are designed for extracting features from the corresponding location clues, respectively. Then, a lightweight attention generation scheme is used to learn the importance of each temporal representation. Guided by the obtained attention values, we fuse multiple local features to generate more distinguishing ones for accurate localization. The experimental results show that FT-Loc significantly outperforms existing localization schemes with accuracy improvements of at least 43.36%.

This article investigates block-level interference exploitation (IE) precoding for multiuser multiple-input-single-output (MU-MISO) downlink systems. To overcome the need for symbol-level IE precoding to frequently update the precoding matrix, we propose to jointly optimize all the precoders or transmit signals within a transmission block. The resultant precoders only need to be updated once per block, and while not necessarily constant over all the symbol slots, we refer to the technique as block-level slot-variant IE precoding. Through a careful examination of the optimal structure and the explicit duality inherent in block-level power minimization (PM) and signal-to-interference-plus-noise ratio (SINR) balancing (SB) problems, we discover that the joint optimization can be decomposed into subproblems with smaller variable sizes. As a step further, we propose block-level slot-invariant IE precoding by adding a structural constraint on the slot-variant IE precoding to maintain a constant precoder throughout the block. A novel linear precoder for IE is further presented, and we prove that the proposed slot-variant and slot-invariant IE precoding share an identical solution when the number of symbol slots does not exceed the number of users. Numerical simulations demonstrate that the proposed precoders achieve a significant complexity reduction compared against benchmark schemes, without sacrificing performance.

An arbitrary-elevation two-sphere reference model is utilized to mimic the unmanned aerial vehicle (UAV) air-to-air fading channels. The model considers the line-of-sight (LoS), the single-bounced transmit (SBT), the single-bounced receive (SBR), and the double-bounced (DB) rays. Based on this model, the closed-form expression of the space-time correlation function (ST-CF) is obtained for the first time under the widely-used assumption of von Mises-Fisher (vMF) distributed scatterers. To further improve the model’s adaptability to realistic scattering environments, the distribution of the scatterers is generalized from a single unimodal vMF density into a mixture that can possess multimodality. Using the single-vMF ST-CF, the ST-CF under the mixture is also written in closed-form. Corresponding to the reference channel model, both the deterministic and the stochastic simulation models are provided, which yield consistent results with the respective derived expressions. This validates the correctness of the suggested closed-form ST-CFs. Moreover, a detailed analysis of the outage probability and the outage capacity is reported, which offers revealing insights into the behaviors of the system performance with respect to change of some key model parameters under multimodal distributions of the scatterers.

A 3-D arbitrary-elevation two-cylinder reference model is proposed for multiple-input-multiple-output (MIMO) air-to-air communications in unmanned aerial vehicle (UAV) channels. This model accounts for not only the Line-of-Sight (LoS) but also the single-bounced at the transmitter (SBT) and the single-bounced at the receiver (SBR), as well as the double-bounced (DB) rays. Therefore, it is endowed with a high adaptability to various UAV-to-UAV communication scenarios. From the reference model, a closed-form expression of the space-time correlation function (ST-CF) is derived. This expression is shown to be the generalizations of many existing correlation functions from the 2-D one-ring, the 3-D low-elevation one-cylinder, the 2-D two-ring, and the 3-D low-elevation two-cylinder model. Corresponding deterministic and stochastic simulation models are also developed in addition to the reference model. The well agreements between the channel capacities obtained from the simulation models and those from the derived ST-CF not only display the usefulness of the simulators but also confirm the correctness of the derivations. Based on the derived closed-form ST-CF, the effects of some model parameters on the capacity are evaluated in a computationally efficient manner.

WiFi-sensing systems that can accomplish multiple relative tasks simultaneously are attracting significant attention due to their potential for large-scale commercial applications. However, different WiFi sensing scenarios may often rely on various task-specific features, posing a challenge in balancing these different, or asymmetrical, characteristics across tasks. In this article, we propose a system called WiADN which aims to address the asymmetrical problems in the joint recognition of users’ locations and activities. Our system is composed of two critical parts: 1) an asymmetrical network architecture and 2) an adaptive weight loss (AWL) module employed during the training phase. First, we define activity recognition as a high-level task and location recognition as a low-level task based on their respective difficulty levels. Then, the proposed architecture leverages the cascading attention-based modules to transfer the prior knowledge between different tasks. The key insight of the proposed architecture is to mimic the skilled learners in similar situations, who often tackle easier problems first to enable them to solve more challenging problems later on. Based on this behavioral strategy, the proposed attention-based modules are designed to generate masks to select specific characteristics from the low-level task to help the high-level task to learn respective features more effectively. Additionally, the AWL module based on the task uncertainty theory is employed to balance two tasks’ asymmetry from the perspective of loss optimization. Furthermore, extensive experiment results based on two open data sets demonstrate the superiority of our system in the accuracy with other state-of-the-art methods. At last, the effectiveness and the robustness of our system are also verified through the comparative studies and ablation experiments. Our source codes are available athttps://github.com/jzhoujg/WiADN.

In this paper, we study symbol-level precoding (SLP) design for time-domain peak-to-average power ratio (PAPR) reduction in a multi-user MISO-OFDM transmission through the idea of constructive interference (CI). Specifically, we design the precoded transmit signals that minimize the symbol-level transmit power subject to per-antenna time-domain PAPR constraint and CI condition, using the knowledge of both data information and channel state information (CSI), based on which a non-convex problem is established. This non-convex problem is transformed into a convex one by the vectorization and relaxation method. For the relaxed problem, we employ Lagrangian method and Karush-Kuhn-Tucker (KKT) conditions to obtain a closed-form expression on the precoded signals as a function of the Lagrangian multipliers. Subsequently, we study the dual problem and obtain the optimal Lagrangian multipliers via the proposed alternating iterative algorithm. We further consider the practical communication scenario with imperfect CSI, where the original CI constraint is transformed into a probabilistic constraint in order to achieve robustness against statistically CSI errors. Numerical results validate that the proposed low-complexity algorithm achieves an enhanced performance over existing methods in terms of transmit power, PAPR and computation complexity, both in ideal perfect CSI and practical imperfect CSI cases.

This paper addresses the constructive interference (CI) precoding problem under per-antenna power constraint (PAPC) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. In this setup, we extend the phase rotation metric and symbol scaling metric of CI precoding under sum power constraint (SPC) to the scenario of PAPC. Against SPC, the scenario of PAPC represents a practical constraint acknowledging that each antenna would have its dedicated power amplifier. Nevertheless, the optimization problem of CI-PAPC precoding becomes more challenging than that under SPC. By analyzing the KKT conditions and leveraging the generalized matrix inverse theory, we obtain a closed-form structure of the CI-PAPC precoder as a function of introduced variables, which facilitates a low-complexity solver. Since the power constraint of each antenna is not always active under PAPC, existing iterative schemes under CI-SPC are no longer applicable. Therefore, the primal-dual interior point method (IPM) is employed to solve the simplified problem with reduced complexity and fast convergence. Simulation results verify our mathematical derivations and demonstrate that our proposed method can reduce the complexity of solving CI-PAPC problem while preserving the error-rate performance, which promotes the practical implementation of CI precoding in real PAPC scenarios.

We propose a parallel constructive interference (CI)-based symbol-level precoding (SLP) approach for massive connectivity in the downlink of multiuser multiple-input single-output (MU-MISO) systems, with only local channel state information (CSI) used at each processor unit and limited information exchange between processor units. We explore and reveal the separability of the SLP model. By reformulating the power minimization (PM) SLP problem and exploiting the separability of the corresponding reformulation, the original problem is decomposed into several parallel subproblems via the ADMM framework with closed-form solutions, leading to a substantial reduction in computational complexity. The sufficient condition for guaranteeing the convergence of the proposed approach is derived, based on which an adaptive parameter tuning strategy is proposed to accelerate the convergence rate. To avoid the large-dimension matrix inverse operation, an efficient algorithm is proposed by employing the standard proximal term and by leveraging the singular value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted to fully eliminate the matrix inversion, and a parallel inverse-free SLP (PIF-SLP) algorithm is finally obtained. Numerical results validate our derivations above, and demonstrate that the proposed PIF-SLP algorithm can significantly reduce the computational complexity compared to the state-of-the-arts.

This paper proposes an indoor localization system by exploring the temporal and frequency features of complex Channel State Information (CSI) under the fifth-generation (5G) cellular network. In particular, we first acquire some successive raw CSIs from multiple base stations (BSs). Then, amplitude-based sequences are obtained by employing a sliding window moving over a consecutive time step on CSI amplitudes. Moreover, the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are adopted to learn robust time-frequency features from the constructed CSI sequences. To emphasize the contributions of the critical elements to final location estimations, we utilize an attention mechanism to assign the local learned features with different weights. We implement the proposed scheme and verify its performance with extensive experiments in some representative indoor scenes.

We investigate constructive interference (CI)-based symbol-level precoding (SLP) in large-scale systems with massive connectivity of users to minimize the transmit power subject to the instantaneous signal-to-interference-plus-noise-ratio (SINR) and CI constraints. By converting the considered problem into a novel separable formulation, we reveal the existence of separability in SLP, which is therefore well-suited for decomposition. The proximal Jacobian alternating direction method of multipliers (PJ-ADMM) framework is adopted to decompose the reformulated problem into multiple subproblems, which can be solved in parallel with closed-form solutions. We further linearize the second-order terms by approximation, which leads to a parallelizable first-order fast solution to SLP. Our derivations are validated by simulation results, which also show that our algorithm can provide optimal performance with substantially lower computational complexity than state-of-the-art algorithms.

This paper reveals the latent relation inherent in two typical problems in constructive interference (CI)-based symbol-level precoding (SLP). One is the power minimization (PM) problem subject to instantaneous signal-to-interference-plus-noise ratio (SINR) constraints, and the other is the weighted max-min SINR balancing (SB) problem with the symbol-level transmit power budget. In particular, we establish an explicit duality between the PM-SLP and SB-SLP problems, where we prove that one of the two problems can be uniquely mapped to the other. The proposed duality not only provides insights into the intrinsic structure of the problems and solutions but also facilitates obtaining the solution to the SB-SLP given the solution to the PM-SLP without the need for one-dimension search, and vice versa. We further propose a closed-form power scaling algorithm to solve the SB-SLP via PM-SLP, by which the separability of the PM-SLP can be leveraged to solve the two problems simultaneously. Numerical results demonstrate our derivations on the duality as well as the efficiency of the proposed power scaling algorithm.

Automatic modulation recognition (AMR) is one of the most essential techniques in non-cooperative orthogonal time frequency space (OTFS) and orthogonal frequency division multiplexing (OFDM) communication systems. Since coexistence of OTFS and OFDM is a potential and practical solution in the future wireless communication scenarios, classification of the OTFS scheme and the OFDM scheme will be a challenging and meaningful task. In this paper, we propose a deep learning-based method, including multi-layer convolution neural networks (CNNs) and an attention-based residual Squeeze-and-Excitation Module (SE), to extract effective characteristics of OTFS and OFDM signals in multi-path Doppler spread fading channel. To obtain comparable and convincing results, the design of OTFS transmitters is on the basis of OFDM systems and contains six different sub-carrier modulation modes (BPSK, QPSK, 8PSK, 16QAM, 64QAM and 256QAM). Meanwhile, data structures of the signals are all well-deigned for fair comparisons. In addition, datasets include five modulation modes (OTFS, OFDM and other commonly-used modulation modes) and different Doppler spread values to verify our proposed method. The simulations show that our proposed SE-CNN model performs better than other baseline methods. Moreover, extensive experiment results demonstrate the robustness of our proposed method.

Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint.

In dense small cell networks, the coordinated multi-point noncoherent joint transmission (JT) is a key technique to mitigate inter-cell interference and enhance network capacity. However, the capacity-maximizing power control and beamforming problem subject to a total transmit power constraint at each individual small cell base station (BS) is inherently nonconvex and NP-hard. To solve this problem, most existing algorithms require global channel state information (CSI) and a lot of computations, which are infeasible and impractical in dynamic wireless networks with limited computing power and link capacity. In this paper, we characterize a low-dimensional solution structure for the power control of the sum-rate maximization problem in time division duplex (TDD) dense small cell networks. Taking advantage of this low-dimensional structure, a distributed noncoherent JT scheme based on multi-agent reinforcement learning (MARL) is proposed to maximize the sum-rate of the dense small cell networks with reduced information overhead. In the proposed scheme, each BS acts as an agent and makes decisions locally. It is proved that the optimal sum-rate can be achieved for single-transmit-antenna BSs by using the proposed scheme. Compared to the best method presently known, the proposed scheme achieves a similar sum-rate with considerably lower computational complexity and information overhead, which makes it more appealing for practical implementations.

Symbol-level precoding (SLP) can fully exploit the multi-user interference in the downlink. This paper investigates fast SLP algorithms for phase-shift keying (PSK) and quadrature amplitude modulation (QAM). In particular, we prove that the weighted max-min signal-to-interference-plus-noise ratio (SINR) balancing (SB) SLP problem with PSK signaling is not separable, which is contrary to the power minimization (PM) SLP problem, and accordingly, existing decomposition methods are not applicable. To tackle this issue, we establish an explicit duality between the SB-SLP and PM-SLP problems with PSK modulation. The proposed duality enables simultaneously obtaining the solutions to the SB-SLP and PM-SLP problems. We concurrently propose a closed-form power scaling algorithm to address the SB-SLP problem by the solution to the PM-SLP problem, via which the separability can be leveraged to decompose the problem. In terms of QAM signaling, a succinct model is used to formulate the PM-SLP problem and convert it into a separable equivalent. The new problem is decomposed into several simple parallel subproblems with closed-form solutions, employing the proximal Jacobian alternating direction method of multipliers (PJ-ADMM). We further prove that the proposed duality can be generalized to the multi-level modulation case, based on which a power scaling parallel inverse-free algorithm is proposed to solve the SB-SLP problem with QAM signaling. Numerical results show that the proposed algorithms offer optimal performance with lower complexity than the state-of-the-art.

For fingerprint-based localization, the time-consuming and labor-intensive construction of offline radio map is the bottleneck which hinders its large-scale implementation. In this paper, we propose a radio map extrapolation and localization algorithm by exploiting the angles and delays of specular multipath components. First, using the concept of virtual anchor nodes (VANs), we calculate the virtual transmitter (VT) of each multipath component according to angle and delay information measured at a known point. By estimating the positions of reflector with these VTs, the angles and delays of the uplink multipath components transmitted at other locations in the indoor environment are extrapolated. Then, a channel fingerprint composed of the extrapolated angles and delays is proposed to represent the channel response in the angle-delay domain, which is spatially unique and discriminative. Thus, the radio map constructed such can significantly reduce the labor and time costs. Lastly, a convolutional neural network (CNN) is applied for indoor localization. The performance of the proposed fingerprint extrapolation and localization method is validated through extensive simulations with a ray-tracing channel model, which exhibits promising localization performance for our proposed scheme with reduced construction costs.

In this paper, we propose a constructive interference (CI)-based block-level precoding (CI-BLP) approach for the downlink of a multi-user multiple-input single-output (MU-MISO) communication system. Contrary to existing CI precoding approaches which have to be designed on a symbol-by-symbol level, here a constant precoding matrix is applied to a collection of symbols within a given transmission block, thus significantly reducing the computational costs over traditional CI-based symbol-level precoding (CI-SLP) as the CI-BLP optimization problem only needs to be solved once per block. For both PSK and QAM modulation, we formulate an optimization problem to maximize the minimum CI effect over the block subject to a block- rather than symbol-level power budget. We mathematically derive the optimal precoding matrix for CI-BLP as a function of the Lagrange multipliers in closed form. By formulating the dual problem, the original CI-BLP optimization problem is further shown to be equivalent to a quadratic programming (QP) optimization. Numerical results validate our derivations, and show that the proposed CI-BLP scheme achieves improved performance over the traditional CI-SLP method, thanks to the relaxed power constraint over the considered block of symbol slots.

In this paper, the signal recovery problem of incomplete ocean data based on graph signal processing is studied. In order to properly process the sparse and inhomogeneous data, the ocean data set is defined as a graph signal and its spatio-temporal characteristics is analyzed. The data are analyzed using the spatial smoothness of the signal based on topology, the correlation of time-varying signals over time, and the low-rank nature of the signal. Based on that, a signal recovery optimization problem is established and the optimization problem is solved via the alternating optimization and alternating direction method of multipliers (ADMM) framework. Simulations based on real-world dataset are performed to reveal the performance gain of the proposed approach.

The WiFi-based fingerprint indoor-positioning system has attracted increasing interest from industry and academia, benefiting from the widespread deployment of the wireless local area network (WLAN) infrastructure. However, with the expansion of application scenarios, this system suffers from labor-intensive work for received signal strength (RSS) fingerprint construction. In this article, we propose a crowdsourcing-based radio map construction method and trajectory matching algorithm to solve the time-consuming preliminary fingerprint data collection in the offline phase, where the tedious collection work is replaced by the massive crowdsourcing sensor data. First, the latent information of the map is extracted by using some image processing methods, and all possible routes are obtained simultaneously with the depth-first traversal method. Then, considering the strict restrictions on walking in indoor environments, the crowdsourcing trajectories are produced by matching the result of pedestrian dead reckoning (PDR) with the candidate routes based on the Shape Context algorithm. Further, a crowdsourcing trajectory can be represented by uniformly distributed reference points based on step detection, and each point corresponds to a unique RSS of access points (APs). Since such a fingerprint construction method can be performed while smartphone holders are moving and not aware of their actual positions, it can be referred to the dynamic construction method of the radio map. The real scenario experiments reveal that the proposed solution can significantly reduce the time and manpower consumption to build the radio map. Moreover, compared with traditional schemes, our crowdsourcing-based radio map construction method for the WiFi fingerprinting localization (CRCLoc) system can improve the accuracy and robustness of the indoor-positioning results.

A parameter extraction method for the LC circuit of a dual-band bandpass filter (DB-BPF) based on the fully connected network is proposed in this article. The network learning process consists of two stages: 1) pretraining and 2) fine-tuning. The network after pretraining has the ability to output the inaccurate values of LC circuit parameters with the desired${S}$-parameters (obtained according to the filter design requirements) as input. The precise LC circuit parameters can be obtained from the network during the subsequent fine-tuning process. This parameter extraction method can be applied to LC circuits of various bandpass filters. In addition to the amplitude of S-parameter, the phase of S-parameter is also used as learning data for the pretraining network. Comparative experiments show that the most accurate LC circuit parameters can be predicted using the network trained with phase information compared with the network trained without phase information. Two 7-order narrowband and two 9-order wideband DB-BPFs with four transmission zeros at different center frequencies are designed to demonstrate the validity of the proposed method, and the experimental results show that the proposed method is valid and effective.

This paper investigates the security problem of a multi-eavesdrop multiple-input-single-output (MISO) wiretap channel, where an N-antenna transmitter communicates with a single-antenna legitimate user in the presence of multiple single-antenna smart eavesdroppers. To overcome the security risk of the traditional secure constructive interference-based (CI-based) scheme when facing the smart eavesdroppers, we propose a novel deception scheme (DS) via a random transmission strategy, where the eavesdroppers are expected to decode the deception symbols correctly but unable to distinguish the authenticity of the decoded symbol. Then, an efficient algorithm is proposed for the deception signal-to interference-plus-noise (SINR)-balancing problem when perfect channel state information (CSI) is assumed. Furthermore, we consider a practical scenario where only imperfect CSI is available, and explore two different methods for the deception optimization problem, i.e., convexification relaxation approach (CRA) and Lagrangian relaxation approach (LRA), respectively. For both CSI cases, a closed-form solution to the considered CI-based deception scheme is obtained. Simulation results validate the superiority of the proposed approach over traditional secure precoding schemes, and also demonstrate the significant computation efficiency improvements for the proposed algorithms.

In this paper, we focus on the physical layer security for a $K$ -user multiple-input-single-output (MISO) wiretap channel in the presence of a malicious eavesdropper, where we propose several interference exploitation (IE) precoding schemes for different types of the eavesdropper. Specifically, in the case where a common eavesdropper decodes the signal directly and Eve's full channel state information (CSI) is available at the transmitter, we show that the required transmit power can be further reduced by re-designing the `destructive region' of the constellations for symbol-level precoding and re-formulating the power minimization problem. We further study the SINR balancing problems with the derived `complete destructive region' with full, statistical and no Eve's CSI, respectively, and show that the SINR balancing problem becomes non-convex with statistical or no Eve's CSI. On the other hand, in the presence of a smart eavesdropper using maximal likelihood (ML) detection, the security cannot be guaranteed with all the existing approaches. To this end, we further propose a random jamming scheme (RJS) and a random precoding scheme (RPS), respectively. To solve the introduced convex/non-convex problems in an efficient manner, we propose an iterative algorithm for the convex ones based on the Karush-Kuhn-Tucker (KKT) conditions, and deal with the non-convex ones by resorting to Taylor expansions. Simulation results show that all proposed schemes outperform the existing works in secrecy performance, and that the proposed algorithm improves the computation efficiency significantly.

In this paper, we focus on the constructive interference (CI) precoding for the scenario when the number of streams simultaneously transmitted by the base station (BS) is larger than that of transmit antennas at the BS, and derive the optimal precoding structure by employing the pseudo inverse. We show that the optimal pre-scaling vector in IE precoding is equal to a linear combination of the right singular vectors that correspond to zero singular values of the coefficient matrix. By formulating the dual problem, we further show that the optimal precoding matrix can be expressed as a function of the dual variables in a closed form, and an equivalent quadratic programming (QP) formulation is derived for computational complexity reduction. Numerical results validate our analysis and demonstrate significant performance improvements for interference exploitation precoding in the considered scenario.

In this paper, we study the secrecy performance of multiple-input single-output (MISO) wiretap channel with random beamforming (RB), where the eavesdropper is equipped with multiple antennas. In traditional RB schemes, the transmitter utilizes only one antenna to emit information signal and adopts the rest antennas to produce a random beamforming vector for security. To make full use of the power resource and improve the secrecy performance, we propose a power-minimizing and signal-splitting random beamforming (PM-SSRB) scheme, where the random beamforming vector is generalized with arbitrary number of transmit antennas based on power-minimizing. To evaluate the secrecy performance of the proposed scheme, we analyze the ergodic secrecy rate and derive the closed-form expression of the ergodic rate of the MISO wiretap channel. Simulation results show that, compared with the traditional hybrid artificial fast-fading scheme (AFF) and artificial noise (AN) scheme, the proposed SSRB scheme and PM-SSRB scheme perform much better in terms of the ergodic secrecy rate in all power regimes. More importantly, when the eavesdropper has more antennas than the transmitter, our schemes always outperform the AFF and AN schemes. The PM-SSRB scheme is also shown to be superior to the secret-key AFF scheme.

In this paper, we propose an advanced direction calibration method for the smartphone-based indoor localization system on the basis of map information and reinforcement learning (RL). Currently, the direction estimated by pedestrian dead reckoning (PDR) is biased due to the low-precision sensor in smartphone and magnetic field distortion in indoor environment. Thus, the direction calibration methods draw increasing attention. Since the movement of pedestrian is restricted by the indoor environment, the map information could be used to correct the heading of pedestrian and then improve the localization performance. Furthermore, since the tracking of pedestrian can be modeled as a Markov decision process. we propose a novel direction calibration algorithm based on deep Q-network (DQN). Different from the traditional direction calibration algorithms that usually rely on image processing, the proposed method use DQN to find an optimal policy to determine the moving direction. We conduct experiments in a realistic representative office environment to reveal the validity of the proposed direction calibration algorithm. The experiment results indicate that the proposed algorithm can remarkably alleviate the cumulative error, and improve the accuracy, stability and robustness of the indoor positioning system.

In this paper, a novel mobile tracking method based on pedestrian dead reckoning (PDR) and wireless local area network (WLAN) RSS fingerprint is proposed, which estimates the real-time location of pedestrian continuously using the improved particle filter. The existing PDR systems mostly focus on the condition that sensor axes are relatively fixed to user. However, the sensor axes may be changing during walking period in several motion patterns, for example that the smartphone is swinging with hand or kept in bag. Therefore, we propose a novel PDR algorithm for different handheld patterns, which detects the steps based on multimode finite-state machine (MFSM) with adaptive updating thresholds and estimates the heading direction with principal component analysis (PCA) and ambiguity resolution. On the other hand, for a long continuous walking process, localization error will accumulate and lead to the particle filter losing tracking of the target device. To deal with this problem, we design an improved particle filter with advanced resampling strategy for recovery when localization fails. We conduct experiments for four motion patterns in two realistic representative indoor environments: office building and shopping mall. Experiment results reveal the proposed localization system could achieve an average localization accuracy within 2m even in the toughest motion pattern.

We investigate the secure degrees of freedom (SDoF) for the two-way 2×2×2 MIMO interference channel (IC) under three wiretap models, i.e., the confidential messages (CM) model, the untrusted relays (UR) model, and the combined CM and UR (CM-UR) model. For the general case of arbitrary antenna configuration under each wiretap model, we derive the upper bound on SDoF with Markov chain and secrecy constraints, and obtain the achievability schemes with designed interference neutralization, cooperative jamming, and interference alignment schemes. To gain insight on these bounds, we further consider the special case where each user node has M antennas and each relay node has N antennas, and highlight the modification process when achieving the maximum SDoF. For such special case, we show that the optimum SDoF of the CM model is achieved in the regimes M ≥ N and M2M, and N = M.

Energy harvesting (EH) technique has attracted great attention in Internet of things (IoT) system as it may significantly increase the network lifetime by using renewable energy sources. In this paper, we consider a simple uplink system composed of one base station (BS) and multiple EH user equipments (UEs), where the system control is modeled as a Markov decision process without any prior knowledge assumed on the energy dynamics. The central controller is the BS, which is in charge of scheduling a subset of UEs to access the limited orthogonal channels and regulating transmission power for the scheduled UEs. We propose an actor-critic deep Q-network based (DQN) reinforcement learning (RL) algorithm to handle such a technically challenging problem with continuous state and action spaces. Experiment results show that the proposed RL algorithm can achieve better performances compared with the existing benchmarks.

Recently, the demand for indoor positioning has gradually increased. Considering that people walk indoors with a serious restriction, the map information is extremely significant, which can be used as an aid in indoor positioning. In order to exploit map information thoroughly and automatically, and obtain a high- precision positioning result, we propose a map- aided particle filter (PF) algorithm based on WiFi and Pedestrian Dead Reckoning (PDR) in this paper, which exploits WiFi RSS fingerprint, inertial sensors and indoor map information comprehensively. Before the online localization, some specific image processing methods which are Morphological operation, Skeleton extraction and Line detection, are introduced to extract the latent information of indoor map, such as the skeleton of passageway in buildings, the possible forwarding directions, etc. Using the extracted features of the floor plan, the particle filter can adjust the estimated heading direction from the PDR module based on the areas of particle distribution. The real scenario experiments reveal the validity of candidate direction matching. The results also indicate that the proposed algorithm can remarkably alleviate the cumulative error, and effectively solve the problem of trajectory drift and particle deactivation during indoor positioning. Thus, compared with traditional schemes, our proposed algorithm can improve the accuracy, stability and robustness of the indoor positioning system.

Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable operations for Internet of Things (IoT) systems. In this paper, we study the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. First, to handle the access control problem assuming causal battery and channel state information, we propose a scheduling algorithm that maximizes the uplink transmission sum rate based on reinforcement learning (RL) with deep Q -network enhancement. Second, for the battery prediction problem, with a fixed round-robin access control policy adopted, we develop an RL-based algorithm to minimize the prediction loss (error) without any model knowledge about the energy source and energy arrival process. Finally, the joint access control and battery prediction problem is investigated, where we propose a two-layer RL network to simultaneously deal with maximizing the sum rate and minimizing the prediction loss: the first layer is for battery prediction, the second layer generates the access policy based on the output from the first layer. Experiment results show that the three proposed RL algorithms can achieve better performances compared with existing benchmarks.

The Internet of Things (IoT) application has a crucial need for long-term and self-sustainable operations. Energy harvesting (EH) technique has attracted great attention in IoT as it may significantly increase the network lifetime by using renewable energy sources. In this paper, we study a simple IoT system composed of one base station (BS) and multiple EH user equipments (UEs), where the system control is modeled as a Markov decision process without any prior knowledge assumed on the energy dynamics. The central controller, i.e., the BS, is in charge of scheduling a subset of UEs to access the limited orthogonal channels and regulating transmission power for the scheduled UEs. Applying reinforcement learning (RL) methods in this situation is technically challenging since the state and action spaces are continuous. With a long short-term memory (LSTM)-based algorithm to predict the UEs' battery states, we propose an actor-critic deep Q-network (DQN) RL algorithm to simultaneously deal with the access and continuous power control problem, by considering both the sum rate and prediction loss. The experimental results show that the proposed RL algorithm can achieve better performances when compared with the existing benchmarks.

We investigate the secure degrees of freedom (SDoF) for the two-user multiple-input multiple-output (MIMO) interference channel with a cooperative jammer, where each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the jammer is equipped with K antennas. For each one of the four regions of parameters (M, N), i.e., 1 ≤ N/M2M. Moreover, we quantify the SDoF gap of the network and reveal it as a function of the number of the antennas. For large K, the upper and lower bounds coincide in all regimes, and the exact SDoF can be achieved.

In this paper, we study an uplink wireless system including N energy harvesting (EH) user equipments (UEs) and one base station (BS) with limited access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. To handle such an access control problem, we propose a scheduling algorithm that maximizes the expected sum discounted uplink transmission rate based on reinforcement learning (RL) with deep Q-network (DQN) enhancement. Different from the traditional access control solutions that usually assume strong model knowledges, our goal is to achieve a more stable and balanced transmission over a long time horizon in a data-driven fashion. Finally, experiment results show that the proposed RL algorithm can achieve better performances compared with existing benchmarks.

In this paper, we study simultaneous wireless information and power transfer (SWIPT) in relay interference channels (ICs), where energy-constrained relays harvest energy from sources' radio-frequency signals and use the harvested energy to forward the information to destinations. We adopt the power splitting (PS) relay protocol and the interference alignment (IA) technique to jointly realize energy transfer and interference management. We propose two novel transmission schemes for the SWIPT in relay IC networks, namely, one-stage PS IA scheme and two-stage PS IA scheme. For both schemes, we investigate the optimal PS ratios that maximize the network sum rate. We obtain the closed-form optimal PS ratios for the two-stage PS IA scheme and develop a distributed and iterative algorithm to derive the optimal PS ratios for the one-stage PS IA scheme. We further study the optimal design of the IA precoding and decoding matrices. Based on the Grassmann manifold approach, we present an algorithm to obtain the optimal IA matrices for both schemes. The optimality of the designs of both the PS ratios and the IA matrices is then verified by simulations. Our results show that the proposed schemes effectively improve the performance of the network and significantly outperform benchmark schemes.

This paper investigates the secrecy performance of amplified-and-forward (AF) relay wiretap model by using energy harvesting (EH) and signal alignment technique. All nodes equipped with multi antennas play different roles in terms of source, destination, relay, eavesdropper and jamming node. The jamming node designs artificial noise based on signal alignment principle, and then transmits it to the eavesdropper with harvested energy. Due to different power limitations, we propose power allocation schemes called Local Power Constraints scheme (LPCS) and Global Power Constraints scheme (GPCS), which maximize the secrecy rate of the system and obtain better secrecy performance when compared with the non-EH scheme and the partial nodes power allocation EH scheme. Furthermore, we also analyze the effect of the energy harvesting on the secure degrees of freedom (SDOF) in the high signal-to-noise (SNR) region, and simulate the secrecy performance with convex optimization method by proving the convexity of the optimization problem. Results show that the proposed schemes outperform the compared schemes on the secrecy performance, and achieve more secrecy rate when the destination and the relay have more antennas.

In this paper, we study a multi-user multi-relay interference-channel network, where energy- constrained relays harvest energy from sources' radio frequency (RF) signals and use the harvested energy to forward the information to destinations. We adopt the interference alignment (IA) technique to address the issue of interference, and propose a novel transmission scheme with the IA at sources and the power splitting (PS) at relays. A distributed and iterative algorithm to obtain the optimal PS ratios is further proposed, aiming at maximizing the sum rate of the network. The analysis is then validated by simulation results. Our results show that the proposed scheme with the optimal design significantly improves the performance of the network.

In this paper, we model and analyze a transmission strategy for Device-to-Device (D2D) terminals with ambient radio-frequency (RF) energy harvesting (EH). The D2D transmitters are powered by the ambient RF energy sources, and reuse the frequency bands with cellular users to communicate with corresponding receivers. Due to the duplex- constrain, D2D transmitters adopt time-switching mode for energy harvesting and data transmission. Stochastic geometry framework is used to model the distributions of D2D users and RF energy sources, and the expression for coverage probability of D2D link and cellular uplink are derived. Then we design the optimal harvestratio by maximizing the average coverage probability of D2D link under the constrain of guaranteeing the communication quality of the cellular link. Based on numerical results, the tradeoff between energy harvesting and data transmission is discussed under different system parameters, such as energy source density and D2D user density.

In this paper, a multiuser wireless powered communication network is considered where all users harvest energy from power beacons by wireless power transfer to support their uplink information transmission. A frequency-division duplex transmission scheme is adopted, where downlink power transfer and uplink information transmission are separated in different frequency bands. Compared with the time-division duplex scheme considered in most of existing literature, the frequency-division duplex scheme has more freedom to optimize the charging time of different cells respectively, and more suitable for distributed deployment of power beacons. The transmission slots and power allocation problem can be formulated as an optimization problem, which is proved to be convex. We derive the optimal slots allocation algorithm with the purpose of fair sum-throughput maximization. Moreover, the asymptotic expression of throughput is obtained, which provides useful insight on the deployment of power beacons. Simulation results demonstrate the proposed scheme can achieve significant performance gains compared to existing scheme from the literature.

WiFi fingerprinting method is an attractive indoor positioning method due to widely deployed WiFi access points (APs) and easily measured received signal strength (RSS). However, WiFi fingerprinting positioning results are unstable because of the fluctuation of RSS. Besides, pedestrian dead reckoning (PDR) method relying on inertial sensors has been widely used in real-time tracking. Since PDR has accumulated errors in long distances tracking, this paper proposes a hybrid algorithm that integrates PDR approach with WiFi fingerprinting approach to further improve positioning accuracy. There are two key points in our algorithm. The first is utilizing dynamic subarea to restrict the searching region of WiFi fingerprinting method. The second is determining particular weights to fuse the positioning results of the above two approaches according to the distances between the current positioning results and the previous hybrid location. Further, we improve our hybrid algorithm which is based on the adjacent estimated positions of WiFi fingerprinting method. Experiment results showed that the average errors of our hybrid algorithm and improved hybrid algorithm were 2.22m and 1.64m respectively, which were reduced by 42% and 57% compared with the pure PDR method. Therefore, the proposed algorithms can provide stable and high positioning accuracy in real environment.

In this paper, we study the degree of freedom(DoF) for the two-cell downlink multiple-input multiple-output(MIMO) interference multiple access channel(IMAC) with the delayed channel state information at the transmitters(CSIT). We propose a two-stage interference alignment scheme(TSIA) which makes full use of both the outdated and the current CSIT to align not only the interference from the adjacent cell onto the zero space, but also the intra-cell interference onto the subspace spanned by the previously received interference signals, respectively. Based on the TSIA method, we characterize the sum DoF of the network and the result shows the significant gain compared with the traditional TDMA-ZF scheme. Furthermore, the trend of the sum rate of the network well coincides with the analysis of DoF, which verifies our work.

This paper investigates a four-node wiretap network including one source (S), one relay (R), one legitimate receiver (D) and one eavesdropper (E) with cooperative jamming in a two-hop relay transmission. In the first phase, S transmits an useful signal to R while D transmits an interference signal with the purpose of confounding the eavesdropper. In the second phase, R broadcasts the mixture signal together to D. In this paper, based on large-scale fading, we propose two system models named One-link Wiretap Model and Three-link Wiretap Model to distinguish the receiving links at E. Then, a power allocation scheme is proposed to maximize the average secrecy capacity through two-dimension optimization. Meanwhile, we solve the optimization problem by two different methods called IBO and IAO. Simulation results show that the proposed power allocation strategy can achieve a higher average secrecy capacity than the reference scheme RJPA (Rate-optimal jamming power allocation) in the two models. Besides, a larger security range can be obtained by the proposed scheme.

This paper focuses on the problem of throughput maximization in a self-powered point-to-point Multi-Input Multi-Output (MIMO) wireless communication system, the transmitter of which is powered only by energy harvested from ambient radio signals. The transmitter follows a save-then-transmit protocol in transmission frames. The protocol requires the system first carry out the energy harvesting process which lasts for a fraction of time (referred to as save-ratio), then the rest of time is utilized to transmit data packets. It is assumed that the channel state information and energy harvesting rate are known in advance. The throughput maximization problem in this scenario is formulated, where the save-ratio and power allocation must be optimized simultaneously, and it is converted to an equivalent form which can be easier tackled through the traditional Water-Filling power allocation algorithm. By proving the concavity of the problem, the optimal save-ratio is solved as a function of energy harvesting rate and channel state matrix in closed form. The numerical results show how optimal save-ratio and maximal achievable throughput vary with energy harvesting rate.

Femtocells have attracted more and more attention in academia, industry and standardization forums. Besides, energy efficiency has been widely discussed in recent years. However, most of the existing works focus on the energy efficiency of macro base stations, the energy efficiency of femto base stations is neglected. In this paper, we study the maximization of energy efficiency of downlink OFDMA macro-femto networks. Both the transmit power constraint of femto base stations and the SINR thresholds of femto users and macro users are considered. We model the subchannel and power allocation problem as a non-cooperative game and a price function is introduced. To decrease the computational complexity, joint subchannel and power allocation are decomposed into two steps and a distributed resource allocation scheme is proposed to resolve the resource allocation problem. Simulation results show that the proposed algorithm has better performance in terms of energy efficiency compared with equal power allocation and an energy-efficient power control algorithm.

Femtocell technology, which aims at improving indoor signal coverage and offloading traffic from macro base stations (MBSs), has attracted interest in wireless industry. Among all the access methods, hybrid access proves to be the most promising one. However, it is difficult to promote the hybrid access mode since all the femto holders (FHs) merely care about their own benefits. In this paper, we propose a price discount strategy for wireless service provider (WSP) to promote the hybrid access mode of femtocell in which WSP provides a price discount in exchange for the femto holders to share part of their resource to macro users. The problem is formulated and analyzed as a Stackelberg game where WSP acts as the leader and femto holders as the followers. The optimal resource allocation ratio for each FH is decided independently and the optimal price discount factor for WSP is also obtained. Furthermore, we analyze how the bandwidth allocation strategy will affect the utility of WSP and an optimal bandwidth allocation ratio is decided. Numerical results show that both WSP and femto holders can benefit from the price discount strategy.

The energy efficiency (EE) has become an increasingly important issue in wireless communications because of the increasing energy cost and concern over the environmental issues. In this paper, the EE is focused and analyzed in device-to-device (D2D) communication underlaying cellular networks when the resources are shared by D2D and cellular users in three modes, namely, Non-Orthogonal Sharing mode (NOS), Orthogonal Sharing mode (OS), and Cellular Mode (CM). In addition, energy efficient power allocation schemes in the three modes are discussed under the maximum transmission power constraint. Numerical simulations are performed in a single-cell environment and the EE performance in these modes are simulated by varying three system parameters: a) the location of the cellular user, b) the distance between D2D users, c) the distance between the D2D pair and the Base Station (BS). The results demonstrate that it is more preferable that the D2D users communicate with each other directly in terms of the energy efficiency.

In this paper, the energy efficiency (EE) is focused and analyzed in device-to-device (D2D) communication underlaying cellular networks when the resources are shared by D2D and cellular users in three modes, namely, Non-Orthogonal Sharing mode (NOS), Orthogonal Sharing mode (OS), and Cellular Mode (CM). In addition, energy efficient power allocation schemes in the three modes are given under the maximum transmission power constraint. Numerical simulations are performed in a single-cell environment and the EE performance in these modes are simulated by varying three system parameters: a) the distance between D2D users, b) the distance between the D2D pair and the Base Station (BS), c) the location of the cellular user. The results demonstrate that the cross effect of the EE between NOS and OS exists and it is more preferable that the D2D users communicate with each other directly in terms of the EE.

An amplify-and-forward relay network composed of a source (S), N relays and a destination (D) is considered, where the relays are untrusted in the sense that they may eavesdrop on the transmission from S to D, that is they may decode messages of the source. As a part of the system, these untrusted relays are willing to help the communication from S to D. To prevent the relays from decoding the source message, a secure spacetime code with full diversity is designed at the source node. In this paper, no secret information is exchanged between S and D in advance. Training symbols are transmitted by S and D respectively. Based on the assumption of channel reciprocity, S and D can obtain the equivalent channels between them, which are unavailable to the relays. By exploiting the equivalent channels and random source antenna selection, random phase rotation is designed for each space-time code block at S to prevent the relays from eavesdropping. Simulation results are presented to verify the performance of the proposed secure spacetime coding scheme.

In this paper, we investigate a joint resource allocation problem for two-way OFDM multi-relay network, where two transceivers exchange OFDM signal with the help of multiple relays. Considering a constraint on the total transmit power consumed in the whole network, the assignment of subcarriers to multiple relays and power allocation at both transceivers as well as relays are jointly optimized to maximize the sum-rate. The maximization problem turns out a mixed integer programming problem, which can be asymptotically solved through Lagrange dual decomposition approach. Further, we propose a suboptimal algorithm to trade off performance for complexity. Finally, simulation results are provided to demonstrate the performance gain of the proposed algorithms.

In this paper, we study the joint resource allocation problem in multi-carrier multi-relay aided dual hop single-user communication. We adopt orthogonal frequency division multiplexing (OFDM) as the transmission modulation and consider amplify-and-forward (AF) relaying scheme. The optimization is performed over power allocation at source node, beamforming at relay nodes and subcarrier pairing at two hops, such that the overall system throughput is maximized under limited power budget at the source and relay nodes. The optimization is a mixed integer programming problem which is solved through dual decomposition approach. To further reduce the complexity, we propose a suboptimal algorithm which sacrifices very little on the performance. Finally, simulation results are provided to corroborate the proposed studies.

The best beam directions for Q coexisted 60GHz radio systems are jointly determined to maximize the sum rate. Capturing the directional characteristic of 60GHz indoor interference channel, a joint discrete optimization problem is properly established with the objective function evaluated from the noisy channel estimates. In addition to the conventional exhaustive search (ES) method, we propose a simulation based sequential stochastic approximation (SSA) algorithm with segmented indicator to solve the problem in an adaptive way. Although only the sub-optimal solution can be guaranteed in practice, the proposed algorithms are demonstrated to be more efficient and practical to implement than beam search in brute force, especially in dense network. Also, they exhibit good flexibility and feasibility for different usages in tradeoff between performance and complexity.

There is a strong need to explore green and harvestable energy in computer communications. However, adapting wireless network performance to harvested energy has largely been ignored in literature. In this paper, we propose a new resource allocation scheme to improve data delivery quality in energy harvesting enabled wireless networks. In the proposed approach, packet Automatic Repeat reQuest (ARQ) limit of each wireless node is adaptively adjusted according to harvested energy. To achieve such optimal retry adaptation, energy neutrality constraint is considered in the overall optimization process. Simulation results show that the proposed retry adaptation approach significantly improves packet delivery ratio by exploring the harvested energy.

In cognitive radio systems, secondary users should continuously sense the licensed spectrum in case a primary user starts to transmit. Whether or not the secondary users can sense the spectrum during the secondary communication is an attractive problem. In this paper, we consider a particular wireless cooperative communication system acting as a secondary system, and demonstrate that by exploiting the cooperative property of the cooperative communication, the secondary system dose sensing while communicating. Compared with the conventional periodical sensing scheme, the proposed method achieves better sensing performance without any overhead. The theoretical analysis is given, and the sensing performance of the secondary system is investigated. Simulation results show that the sensing performance of the proposed method is improved significantly as opposed to that of the conventional spectrum sensing.

In this paper, we develop a simple blind channel estimation algorithm for two-way relay network (TWRN) that consists of two terminal nodes and one relay node. We consider the frequency selective channels and adopt the orthogonal frequency-division multiplexing (OFDM) modulation to compensate the inter-symbol interference (ISI). By applying a non-redundant linear precoding at both terminals, we propose an algorithm that is effective of estimating two cascaded channels and is consistent with the two-phase two-way transmission protocols. The method to remove the inherent ambiguity of the blind channel estimation is discussed. Finally, the numerical results are provided to corroborate the proposed studies.

In this paper, we study the performance of the multiple-input multiple-output macrodiversity transmission with limited feedback. The downlink scenario, where multiple transmitters serve the user simultaneously, is considered. We find it necessary to modify the model of the quantized channel by Jinal (2006) such that the phase ambiguity in the vector quantization procedure can be characterized. Using the modified model, we show that the conventional limited feedback methods cannot obtain the macrodiversity gain even when the size of the codebook is asymptotically large, and that the macrodiversity gain can be attained by adding only one bit of phase feedback. Our analysis can apply to both random vector quantization and any fixed codebook setup, and is verified by the simulation results.

In order to improve the downlink capacity of a multiuser multiple input multiple output system exploiting spatial multiplexing, this paper proposes two grouped multiuser diversity schemes. Aiming at the suppression of co-channel interference, both schemes select a group of users to share the same time-frequency channel unit in two steps. The first scheme iteratively selects the users based on a traditional multiuser scheduler - the PFS method. The second scheme first groups the users according to the cross-correlation of their channel response matrixes, then selects the group that maximizes the system capacity. Simulation results show that both schemes can provide fair scheduling and enhance the capacity performance. Moreover, the second scheme achieves higher capacity with more computational complexity comparing with the first one.