Next generation communications demand better spectrum management, lower latency, and guaranteed quality-of-service (QoS). Recently, artificial intelligence (AI) has been widely introduced to advance these aspects in next generation wireless systems. However, such AI applications suffer from limited training data, low robustness, and poor generalization capabilities. To address these issues, we introduce a model-driven deep unfolding (DU) algorithm in this paper to address the gap between traditional model-driven communication algorithms and data-driven deep learning. Focusing on the QoS-aware rate-splitting multiple access (RSMA) resource allocation problem in multi-user communications, a conventional fractional programming (FP) algorithm is first applied as a benchmark. The solution is further refined using projection gradient descent (PGD). DU is employed to further accelerate convergence, thereby improving the efficiency of PGD. Moreover, the feasibility of results is guaranteed by designing a low-complexity projection based on scale factors, and adding violation control mechanisms into the loss function that minimizes error rates. Finally, we provide a detailed analysis of the computational complexity and analysis design of the proposed DU algorithm. Extensive simulations are conducted and the results demonstrate that the proposed DU algorithm can reach the optimal communication efficiency with only 1.1% violation rate for the five-layer DU. The DU algorithm also exhibits robustness in out-of-distribution tests and can be effectively trained with as few as 50 samples.

Precisely modeling radio propagation in complex environments has been a significant challenge, especially with the advent of 5G and beyond networks, where managing massive antenna arrays demands more detailed information. Traditional methods, such as empirical models and ray tracing, often fall short, either due to insufficient details or because of challenges for real-time applications. Inspired by the newly proposed 3D Gaussian Splatting method in the computer vision domain, which outperforms other methods in reconstructing optical radiance fields, we propose RF-3DGS, a novel approach that enables precise site-specific reconstruction of radio radiance fields from sparse samples. RF-3DGS offers high efficiency, requiring only a few minutes for training and achieving fast inference for any arbitrary receiver pose within milliseconds. Furthermore, RF-3DGS can provide fine-grained Spatial Channel State Information (Spatial-CSI) of these paths, including the channel gain, the delay, the angle of arrival (AoA), and the angle of departure (AoD). Our experiments, calibrated through real-world measurements, demonstrate that RF-3DGS not only significantly improves reconstruction quality, training efficiency, and rendering speed compared to state-of-the-art methods, but also holds great potential for supporting wireless communication and advanced applications such as Integrated Sensing and Communication (ISAC). Code and dataset are available athttps://github.com/SunLab-UGA/RF-3DGS

Terahertz (THz) communication is a key enabler for 6G systems, offering ultra-wide bandwidth and unprecedented data rates. However, THz signal propagation differs significantly from lower-frequency bands due to severe free space path loss, minimal diffraction and specular reflection, and prominent scattering, making conventional channel modeling and pilot-based estimation approaches inefficient. In this work, we investigate the feasibility of applying radio radiance field (RRF) framework to the THz band. This method reconstructs a continuous RRF using visual-based geometry and sparse THz RF measurements, enabling efficient spatial channel state information (Spatial-CSI) modeling without dense sampling. We first build a fine simulated THz scenario, then we reconstruct the RRF and evaluate the performance in terms of both reconstruction quality and effectiveness in THz communication, showing that the reconstructed RRF captures key propagation paths with sparse training samples. Our findings demonstrate that RRF modeling remains effective in the THz regime and provides a promising direction for scalable, low-cost spatial channel reconstruction in future 6G networks.

The proliferation of mobile devices and online interactions have been threatened by different cyberattacks, where phishing attacks and malicious Uniform Resource Locators (URLs) pose significant risks to user security. Traditional phishing URL detection methods primarily rely on URL string-based features, which attackers often manipulate to evade detection. To address these limitations, we propose a novel graph-based machine learning model for phishing URL detection, integrating both URL structure and network-level features such as IP addresses and authoritative name servers. Our approach leverages Loopy Belief Propagation (LBP) with an enhanced convergence strategy to enable effective message passing and stable classification in the presence of complex graph structures. Additionally, we introduce a refined edge potential mechanism that dynamically adapts based on entity similarity and label relationships to further improve classification accuracy. Comprehensive experiments on real-world datasets demonstrate our model's effectiveness by achieving F1 score of up to$\text{9 8. 7 7 \%}$. This robust and reproducible method advances phishing detection capabilities, offering enhanced reliability and valuable insights in the field of cybersecurity.

Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require complicated iterative algorithms, which cannot meet the stringent latency requirement by users with limited resources. Recently, data-driven methods are explored to alleviate this issue. However, they suffer from poor generalizability and scarce training data to achieve satisfactory performance. In this paper, we propose a fractional programming (FP) based deep unfolding (DU) approach to address resource allocation problem for a weighted sum rate optimization in RSMA. By carefully designing the penalty function, we couple the variable update with projected gradient descent algorithm (PGD). Following the structure of PGD, we embed a few learnable parameters in each layer of the DU network. Through extensive simulation, we have shown that the proposed model-based neural networks can yield similar results compared to the traditional optimization algorithm for RSMA resource management but with much lower computational complexity, less training data, and higher resilience to out-ofdistribution (OOD) data.

Federated learning (FL) has emerged as a distributed machine learning (ML) technique that can protect local data privacy for participating clients and improve system efficiency. Instead of sharing raw data, FL exchanges intermediate learning parameters, such as gradients, among clients. This article presents an efficient wireless communication approach tailored for FL parameter transmission, especially for Internet of Things (IoT) devices, to facilitate model aggregation. Our study considers practical wireless channels that can lead to random bit errors, substantially affecting FL performance. Motivated by empirical gradient value distribution, we introduce a novel received bit masking method that confines received gradient values within prescribed limits. Moreover, given the intrinsic error resilience of ML gradients, our approach enables the delivery of approximate gradient values with errors without resorting to extensive error correction coding or retransmission. This strategy reduces computational overhead at both the transmitter and the receiver and minimizes communication latency. Consequently, our scheme is particularly well-suited for resource-constrained IoT devices. Our simulations demonstrate that our proposed scheme can effectively mitigate random bit errors in FL performance, achieving similar learning objectives but with the 50% air time required by existing methods involving error correction and retransmission.

In this article, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT’s accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whether to transmit physical network information and optimizing spectrum allocation. Simulation results show that our GRU-based and VDN-based algorithm improves the weighted sum of data rates and the similarity between the status of the DNT and the physical network by up to 28.96%, compared to a baseline method combining GRU with the independent Q learning (IQL).

Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing. Link state map (LSM) is one particular type of CKM that aims to learn the location-specific line-of-sight (LoS) link probability between the transmitter and the receiver at all possible locations, which provides the prior information to enhance the communication quality of dynamic networks. This paper investigates the LSM construction for cellular-connected unmanned aerial vehicles (UAVs) by utilizing both the expert empirical mathematical model and the measurement data. Specifically, we first model the LSM as a binary spatial random field and its initial distribution is obtained by the empirical model. Then we propose an effective binary Bayesian filter to sequentially update the LSM by using the channel measurement. To efficiently update the LSM, we establish the spatial correlation models of LoS probability on the location pairs in both the distance and angular domains, which are adopted in the Bayesian filter for updating the probabilities at locations without measurements. Simulation results demonstrate the effectiveness of the proposed algorithm for LSM construction, which significantly outperforms the benchmark scheme, especially when the measurements are sparse.

Asynchronous federated learning aims to solve the straggler problem in an environment with heterogeneity, where certain clients may possess limited computational capacities, potentially leading to model aggregation delay. The core concept behind asynchronous federated learning is to empower the server to aggregate the model as soon as it receives an update from any client without waiting for updates from multiple clients or adhering to a predetermined waiting time, which is typical in synchronous mode. Because of the asynchronous setting, a potential concern is the emergence of a stale model issue, wherein slow clients might employ an outdated local model for their data training. Consequently, when these locally trained models are uploaded to the server, they may impede the convergence of the global training. Therefore, effective model aggregation strategies play a significant role in updating the global model. Besides, client scheduling is critical when heterogeneous clients with diversified computing capacities participate in the federated learning process. This work first investigates the impact of the convergence of asynchronous federated learning mode when adopting the aggregation coefficient in synchronous mode. Effective aggregation solutions that can achieve the same convergence result as in the synchronous mode are proposed, followed by an improved aggregation method with client scheduling. The simulation results in various cases demonstrate that the proposed algorithm converges with a similar level of accuracy as the classical synchronous federated learning algorithm but effectively accelerates the learning process, especially in its early stage.

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

This paper explores how a flying drone can autonomously navigate while constructing a narrowband radio map for signal localization. As flying drones become more ubiquitous, their wireless signals will necessitate new wireless technologies and algorithms to provide robust radio infrastructure while preserving radio spectrum usage. A potential solution for this spectrum-sharing localization challenge is to limit the bandwidth of any transmitter beacon. However, location signaling with a narrow bandwidth necessitates improving a wireless aerial system’s ability to filter a noisy signal, estimate the transmitter’s location, and self-pilot to improve the location estimate. By showing results through simulation, emulation, and a final drone flight experiment, this work provides an algorithm using a Gaussian process for radio signal estimation and Bayesian optimization for drone automatic guidance. This research supports advanced radio and aerial robotics applications in critical areas such as search-and-rescue, last-mile delivery, and large-scale platform digital twin development.

Offloading time-sensitive, computationally intensive tasks—such as advanced learning algorithms for autonomous driving—from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via vehicle-to-vehicle (V2V) communication enhances service efficiency. However, whence traversing the path to the destination, the vehicle’s mobility necessitates frequent handovers among the access points (APs) to maintain continuous and uninterrupted wireless connections to maintain the network’s Quality of Service (QoS). These frequent handovers subsequently lead to task migrations among the edge servers associated with the respective APs. This paper addresses the joint problem of task migration and access-point handover by proposing a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A joint allocation method of communication and computation of APs is proposed to minimize computational load, service latency, and interruptions with the overarching goal of maximizing QoS. We implement and evaluate our proposed framework on simulated experiments to achieve smooth and seamless task switching among edge servers, ultimately reducing latency.

Most of works on backscatter communication (BackCom) assume the availability of a direct link from the BackCom transmitter to its receiver and the long-packet transmission for BackCom. However, the above assumptions may be inapplicable in some practical Internet of Things (IoT) applications, e.g., industrial automation. Motivated by this, this paper proposes and studies a relay assisted short-packet BackCom network, where a full-duplex relay (R) with two antennas and an energy self-sustaining IoT node form a monostatic short-packet BackCom paradigm in the first phase while R forwards the IoT’s short-packet information to the information receiver (IR) in the second phase via a decode-and-forward (DF) or amplify-and-forward (AF) relaying protocol. For a given DF or AF relaying protocol, we propose to optimize the power allocation factor of two antennas at R in the second phase to minimize the IoT node’s average block error rate (BLER), and derive the optimal solution in the closed form. With the optimal power allocation factor, we derive the analytical expressions of the IoT node’s average BLER under the DF and AF protocols while considering the residual self-interference at R and the energy causal constraint at the IoT node. Simulation results verify the correctness of the derived expressions and reveal the impacts of various parameters such as the IoT node’s short-packet blocklength and power reflection coefficient on the average BLER. It is found that when the short-packet blocklength increases, the average BLER decreases until it reaches a certain value.

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart.

To address the limitation on the rate of secondary users (SUs) in traditional mutualistic symbiotic radio (SR) systems, this paper proposes a new mutualistic SR with hybrid active-passive communications. Unlike the traditional mutualistic SR, where SUs only perform passive backscatter communications (BC), in the proposed mutualistic SR, each SU conveys information via passive BC and active communications (AC) alternatively. To exploit the potential advantages of the proposed SR, we formulate a non-convex problem to maximize the total rate of all SUs by jointly optimizing the transmit power of the primary user (PU), the reflection coefficient and transmit power of each SU, and the time allocation for each SU to perform passive BC and AC. With proof by contradiction, auxiliary variables and successively convex approximation (SCA), we transform the original problem into a convex one and solve it by proposing an iterative algorithm. The simulation results demonstrate that the proposed iterative algorithm converges quickly. Furthermore, the performance evaluation of the proposed mutualistic SR system demonstrates its superiority over the traditional mutualistic SR in terms of the rate achieved by SUs under the same constraints.

In this article, we propose a novel system model for a wireless-powered mobile edge computing (MEC) network, where the Internet of Things (IoT) nodes perform partial offloading to the MEC server via hybrid backscatter communication (BackCom) and active radio (AR) following an orthogonal frequency division multiple access protocol, and maximize the system computation bits (SCBs). For the case of the system having more subchannels than IoT nodes, we formulate the SCB maximization problem that requires the joint optimization of the transmit power and time, subchannel allocation, computation frequency, and time of the MEC server, as well as the IoT nodes’ BackCom time and reflection coefficients, transmit power and time for AR-based offloading, local computing time and frequencies, subject to the MEC server’s computation capacity and the Quality of Service (QoS) and energy-causality constraints of each IoT node. By applying the proof by contradiction and time-sharing relaxation, we transform the formulated problem into a convex one and then solve it by using the existing convex tools. For the case of the system having less subchannels than IoT nodes, we propose a dynamic subchannel allocation scheme that allows each IoT node to choose one task-offloading mode from three modes: 1) HAPR; 2) BackCom only; and 3) AR only, while ensuring that no more than one IoT node occupies a subchannel at any time. The SCB is maximized by first determining the subchannel allocation and mode selection of each IoT node and then optimizing the remaining resource allocation for the MEC server and all IoT nodes under the obtained subchannel assignment and mode selection. Simulations validate the superior performance of the proposed schemes over several benchmark schemes from the SCB perspective.

Motivated by the ever-increasing concerns on per-sonal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learning setting. An FL system is comprised of a central parame-ter server and multiple local clients. It keeps data at local clients and learns a centralized model by sharing the model parameters learned locally. No local data needs to be shared, and privacy can be well protected. Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients. Furthermore, it is challenging to identify malicious clients since no local client data is available on the server. Besides, membership inference attacks can still be performed by using the uploaded model to estimate the client's local data, leading to privacy disclosure. In this work, we first propose a model update based federated averaging algorithm to defend against Byzantine attacks such as additive noise attacks and sign-flipping attacks. The individual client model initialization method is presented to provide further privacy protections from the membership inference attacks by hiding the individual local machine learning model. When combining these two schemes, privacy and security can be both effectively enhanced. The proposed schemes are proved to converge experimentally under non-IID data distribution when there are no attacks. Under Byzantine attacks, the proposed schemes perform much better than the classical model based FedAvg algorithm.

In this article, we propose a wireless powered opportunistic cooperative backscatter communication network, where an Internet of Things (IoT) node conveys information to its associated receiver via backscatter communications with the help of a hybrid access point (HAP) in each transmission block. The HAP provides energy signals for the IoT node in the first half transmission block, and continues to do or relays the IoT node’s signal to the receiver via decode-and-forward protocol in the second half transmission block. We investigate under which condition the HAP serves as the relay node in the second half transmission block in terms of the achievable throughput. To this end, a mixed integer non-convex optimization is formulated to maximize the throughput of the IoT node by optimizing the power reflection coefficient (PRC) of the IoT node and the operation mode of the HAP during the second half transmission block, while meeting the energy-causality constraint of the IoT node. We derive the closed-form expressions for the optimal PRC and operation mode, based on which a scheme is proposed to determine the optimal operation mode of the HAP. Simulations validate the derived results and study the impacts of various parameters on the optimal operation mode and throughput.

A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process. FL not only reduces the communication needs but also helps to protect the local privacy. Although FL has these advantages, it can still experience large communication latency when there are massive edge devices connected to the central parameter server (PS) and/or millions of model parameters involved in the learning process. Over-the-air computation (AirComp) is capable of computing while transmitting data by allowing multiple devices to send data simultaneously by using analog modulation. To achieve good performance in FL through AirComp, user scheduling plays a critical role. In this paper, we investigate and compare different user scheduling policies, which are based on various criteria such as wireless channel conditions and the significance of model updates. Receiver beamforming is applied to minimize the mean-square-error (MSE) of the distortion of function aggregation result via AirComp. Simulation results show that scheduling based on the significance of model updates has smaller fluctuations in the training process while scheduling based on channel condition has the advantage on energy efficiency.

Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications especially in wireless communications. There can be a large number of distributed edge devices connected to a central parameter server (PS) and iteratively download/upload data from/to the PS. Due to limited bandwidth, only a subset of connected devices can be scheduled in each round. There are usually millions of parameters in the state-of-art machine learning models such as deep learning, resulting in a high computation complexity as well as a high communication burden on collecting/distributing data for training. To improve communication efficiency and make the training model converge faster, we propose a new scheduling policy and power allocation scheme using non-orthogonal multiple access (NOMA) settings to maximize the weighted sum data rate under practical constraints during the entire learning process. NOMA allows multiple users to transmit on the same channel simultaneously. The user scheduling problem is transformed into a maximum-weight independent set problem that can be solved using graph theory. Simulation results show that the proposed scheduling and power allocation scheme can help achieve a higher FL testing accuracy in NOMA based wireless networks than other existing schemes within the same learning time.

Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existing attackers, it is very challenging to offload computation tasks securely and efficiently. In this paper, we study the task offloading and resource allocation problem in a non-orthogonal multiple access (NOMA) assisted MEC network with security and energy efficiency considerations. To tackle the problem, a dynamic secure task offloading and resource allocation algorithm is proposed based on Lyapunov optimization theory. A stochastic non-convex problem is formulated to jointly optimize the local-CPU frequency and transmit power, aiming at maximizing the network energy efficiency, which is defined as the ratio of the long-term average secure rate to the long-term average power consumption of all users. The formulated problem is decomposed into the deterministic sub-problems in each time slot. The optimal local CPU-cycle and the transmit power of each user can be given in the closed-from. Simulation results evaluate the impacts of different parameters on the efficiency metrics and demonstrate that the proposed method can achieve better performance compared with other benchmark methods in terms of energy efficiency.

Millimeter-wave (mmWave) communications is an emerging technology expected to bring unprecedented data rates and throughput. WiFi operating at unlicensed 60 GHz range is envisioned to become an ubiquitous technology and the IEEE 802.11ad standard is an initial attempt in that direction. Al-though spatial and temporal resolution of mmWave signals make them suitable for location estimation, a variety of hardware-related issues and commonly encountered difficulties in extract-ing channel measurements from commercial chipsets, challenge opportunistic use of commercial mmWave WiFi chips for indoor localization. We propose in this paper an indoor localization method that fingerprints transmit beam indices that a pair of WiFi transceivers employ to establish a mmWave link, as well as the resulting received signal strength (RSS). In particular, we develop an algorithm that learns possible probabilistic models from the fingerprint data and leverages them to perform indoor localization in the online stage. The proposed algorithm is experimentally evaluated using commercial 60 GHz WiFi routers in an office space area and localization error of around 30 cm is demonstrated.

Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements from the MAC layer. In this paper, we propose to use an intermediate channel measurement - spatial beam signal-to-noise ratios (SNRs) that are inherently available during the beam training phase as defined in the IEEE 802.11ad standard - to construct the feature space for location-and-orientation-dependent fingerprinting database. We build a 60GHz experimental platform consisting of three access points and one client using commercial-off-the-shelf routers and collect realworld beam SNR measurements in an office environment during regular office hours. Both position/orientation classification and coordinate estimation are considered using classic machine learning approaches. Comprehensive performance evaluation using real-world beam SNRs demonstrates that the classification accuracy is 99.8% if the location is only interested, while the accuracy is 98.6% for simultaneous position-and-orientations classification. Direct coordinate estimation gives an average root-mean-square error of 17.52 cm and 95% of all coordinate estimates are less than 26.90 cm away from corresponding true locations. This concept directly applies to other mmWave band (e.g., 5G) devices where beam training is also required.

This paper studies a multiple-input single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non-linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user (SU). Since it is difficult to obtain perfect channel state information (CSI) in practice, instead either a bounded or Gaussian CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for two problems with different objectives, namely, that of minimizing the transmission power of the cognitive base station and that of maximizing the total harvested energy of the SUs, respectively. The optimization problems are challenging to solve, mainly because of the non-linear structure of the energy harvesting and CSI errors models. We converted them into convex forms by using semi-definite relaxation. For the minimum transmission power problem, we obtain the rank-2 solution under the bounded CSI error model, while for the maximum energy harvesting problem, a two-loop procedure using a 1-D search is proposed. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart. Furthermore, the performance using the Gaussian CSI error model is generally better than that using the bounded CSI error model.

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. However, resource allocation strategies for maximizing the computation efficiency have not been fully investigated. In this paper a computation efficiency maximization problem is formulated in the wireless-powered MEC network under a practical non-linear energy harvesting model. The energy harvesting time, the local computing frequency, the offtoading time, and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. The problem is non-convex and challenging to solve. An iterative algorithm is proposed to solve this problem. Simulation results show that our proposed resource allocation scheme outperforms the benchmark schemes in terms of the computation efficiency and verify the efficiency of our proposed algorithm. A tradeoff is elucidated between the achievable computation efficiency and the computation bits.

This paper studies a multiple-input-single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non- linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user. Since it is difficult to obtain the perfect channel state information (CSI) in practice, a bounded CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for minimizing the transmission power of the cognitive base station. The original non-convex optimization problem is then converted into convex forms by using semi- definite relaxation. For the minimum transmission power problem, we prove that the optimal solution has a limited rank of less than or equal to 2. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart.

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are two promising technologies in the next generation wireless communication systems. The security of a NOMA CR network (CRN) is important but lacks of study. In this paper, a multiple-input single-output NOMA CRN relying on simultaneous wireless information and power transfer is studied. In order to improve the security of both the primary and secondary network, an artificial noise-aided cooperative jamming scheme is proposed. Different from the most existing works, a power minimization problem is formulated under a practical non-linear energy harvesting model. A suboptimal scheme is proposed to solve this problem based on semidefinite relaxation and successive convex approximation. Simulation results show that the proposed cooperative jamming scheme is efficient to achieve secure communication and NOMA outperforms the conventional orthogonal multiple access in terms of the power consumption.

With the emergence of diverse mobile applications (such as augmented reality), the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime. Mobile edge computing (MEC) and wireless power transfer are promising to address this issue. However, these two techniques are susceptible to propagation delay and loss. Motivated by the chance of short-distance line-of-sight achieved by leveraging unmanned aerial vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is studied. A power minimization problem is formulated subject to the constraints on the number of the computation bits and energy harvesting causality. The problem is non-convex and challenging to tackle. An alternative optimization algorithm is proposed based on sequential convex optimization. Simulation results show that our proposed design is superior to other benchmark schemes and the proposed algorithm is efficient in terms of the convergence.

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error model. The problems formulated are non-convex, hence they are challenging to solve. A pair of algorithms either using semidefinite relaxation (SDR) or a cost function are proposed for solving these problems. Our simulation results show that the proposed cooperative jamming scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access in terms of its power efficiency. Finally, we demonstrate that the cost function algorithm outperforms the SDR-based algorithm.

This paper applies non-orthogonal multiple access (NOMA) and relaying schemes in a mmWave based wireless heterogeneous system that aims to support Internet of Things (IoT) applications. The system consists of high power base stations, low-power relays, and low-power IoT devices. Due to the ad hoc deployment nature of low-power relays, they have very limited access to wireline power charging facilities. Furthermore, IoT devices normally have limited power and short battery life. The study assumes low-power relays and IoT devices are capable of energy harvest functionality. With the help of relays or IoT devices, downlink NOMA transmission consists of two phases. In the first phase, the BS sends a composite signal to a UE and a selected relay simultaneously by applying NOMA. After receiving the signal, relay or the IoT device split the signal into two parts. One part is for information decoding and the other part is for energy harvesting. In the second phase, the BS sends another message to UE 1 while the relay sends the decoded message to UE 2 by using the harvested energy in phase 1. The outage problem of the proposed scheme is analyzed and simulations results are presented to verify the theoretical results.

In this paper two different non-orthogonal multiple access (NOMA) relay schemes are analyzed, namely NOMA cooperative scheme and NOMA TDMA scheme. Both schemes apply NOMA in the first stage by sending NOMA superimposed signals to relays. Relays then forward the messages to two UEs in the second stage. In the second stage of NOMA cooperative scheme, two relays form a cooperative pair to simultaneously transmit the decoded signals to their respective recipients. Dirty paper coding is used as precoding to cancel out inter-user interference. The second stage of NOMA TDMA scheme uses TDMA to send to two users in two separate time slots. The outage probability is analyzed for both schemes and the impact of error propagation in the NOMA successive interference cancellation is analyzed and evaluated. Performance study shows that the theoretical analysis matches the simulation results very well. NOMA cooperative scheme achieves an overall better outage performance than NOMA TDMA scheme.

To address the ever increasing high data rate and connectivity requirements in the next generation 5G wireless network, novel radio access technologies (RATs) are actively explored to enhance the system spectral efficiency and connectivity. As a promising RAT for 5G cellular networks, non-orthogonal multiple access (NOMA) has attracted extensive research attentions. Compared with the orthogonal multiple access (OMA) that has been widely applied in existing wireless communication systems, NOMA possesses the potential to further improve the system spectral efficiency and connectivity capability. This paper develops analytical frameworks for NOMA downlink and uplink multi-cell wireless systems to evaluate the system outage probability and average achievable rate. In the downlink NOMA system, two different NOMA group pairing schemes are considered, based on which theoretical results on outage and achievable data rates are derived. In the uplink NOMA, revised back-off power control scheme is applied, and outage probability and per UE average achievable rate are derived. As wireless networks turn into more and more densely deployed, inter-cell interference has become a dominant capacity limiting factor but has not been addressed in most of the existing NOMA studies. In this paper, a stochastic geometry approach is used to model a dense wireless system, that supports NOMA on both uplink and downlink, based on which analytical results are derived either in pseudo-closed forms or succinct closed forms and are further validated by simulations. Numerical results demonstrate that NOMA can bring considerable system-wide performance gain compared with OMA on both uplink and downlink when properly designed.

To achieve a significant boost on capacity performance in the next generation (5G) cellular network, novel radio access technologies (RAT) are demanded to make the system more spectrum efficient. As a promising multiple access scheme for 5G cellular network, non-orthogonal multiple access (NOMA) has attracted extensive research attention recently. Existing works show that NOMA posses the potential to further improve system spectrum efficiency compared with the orthogonal multiple access (OMA), which is predominantly adopted by existing wireless networks. In this paper, we develop the analytical framework on system coverage and average user achievable rate in a downlink NOMA system. We explicitly consider the inter-cell interference in the study, which is a capacity limiting factor in most wireless networks but less addressed in most existing analytical work for NOMA. Additional to NOMA, the analysis on an OMA access scheme, i.e., orthogonal frequency division multiple access (OFDMA), is also conducted for comparison. Owing to the tractability of Poisson Point Process (PPP) model used in this work, all the analytical results are derived and expressed in a pseudo-closed form or a succinct closed form. The analytical results are validated by simulations and demonstrate that NOMA can bring considerable performance gain compared to OMA when success interference cancellation (SIC) error is low.

The paper studies a scheme that jointly considers beamforming based MU-MIMO and NOMA in a downlink cellular network with underlaid D2D users. Two different MU-MIMO beamforming schemes are developed. The first beamforming scheme aims to eliminate the interference caused by different beams while the second one aims to cancel out the interference from base stations to D2D users. An optimization problem is formulated to maximize the total system sum throughput of both cellular users and D2D users. Since the optimization problem is NP hard and difficult to solve, we develop a suboptimal sequential solution by determining the zero-forcing beamforming matrix for MU-MIMO first. Then a user grouping and optimal power allocation algorithm is proposed in order to maximize the capacity of cellular users in each beam. Simulation results show that MUMIMO beamforming, NOMA and D2D together will significantly improve the overall system throughput.

Non-orthogonal multiple access (NOMA) is an emerging technology that can improve system spectral efficiency. In this paper, we propose a downlink multiple-input-multiple- output (MIMO) wireless system that incorporates NOMA. To make the system model more realistic, error propagation in successive interference cancellation (SIC) is considered during the decoding process. We formulate an optimization problem aiming at maximizing system throughput. The imposed error propagation increases the complexity to find a closed-form solution, so a heuristic way is proposed to solve the precoding matrix by an iterative algorithm. Case studies are performed to evaluate the impact of power allocation with residual interference afterwards. Simulation results show the superiority of the proposed precoding design and give an insight on power allocation.

In order to address the ever increasing high capacity demands, next generation wireless networks are required to revolutionize the infrastructure design and air interface technologies. In this paper, we introduce a cooperative non-orthogonal multiple access (NOMA) technique with successive interference cancellation (SIC) in wireless heterogeneous networks. Aiming to improve the system capacity, the cooperative NOMA scheme exploits both NOMA and dirty paper coding (DPC), based on which a resource scheduling optimization problem is formulated. The optimization problem is a combinatorial mixed-integer non-linear problem. A genetic algorithm is used to solve the problem with a low computational complexity. Simulation results show that the proposed cooperative NOMA scheme can significantly improve the network capacity.

A neurocomputational model is presented for figureground discrimination and target tracking. In the model, the elementary motion detectors of the correlation type, the computational modules of saccadic and smooth pursuit eye movement, an oscillatory neural-network motion perception module and a selective attention module are involved. It is shown that through the oscillatory amplitude and frequency encoding, and selective synchronization of phase oscillators, the figure and the ground can be successfully discriminated from each other. The receptive fields developed by hidden units of the networks were surprisingly similar to the actual receptive fields and columnar organization found in the primate visual cortex. It is suggested that equivalent mechanisms may exist in the primate visual cortex to discriminate figure-ground in both temporal and spatial domains.