Current empirically driven research on semantic communication lacks a unified theoretical foundation, preventing quantifiable Quality of Service guarantees, particularly for transmitting minimal structural semantics in emergency scenarios. This deficiency limits its evolution into a predictable engineering science. To address this, we establish a complete theoretical axiomatic basis for this problem. We propose four axioms and rigorously prove that the family of pairwise rank-Copulas is the minimal sufficient representation for minimal structural semantics. Based on this, we construct a semantic distortion metric, centered on the Jensen-Shannon divergence. We then establish the core theoretical boundaries of the framework: sample complexity bounds; rate-distortion bounds; an end-to-end Service Level Agreements theorem; and a semantic source-channel separation theorem, which provides a provable Quality of Service guarantee. Finally, we validate our framework through decoupled experiments, empirically demonstrating that our core metric strictly adheres to our foundational axioms while standard perceptual metrics fail to do so.

In this article, a flexible and lightweight filtering wearable antenna without extra circuits is presented. The proposed antenna starts from a flexible directional antenna with lightweight structure, which includes a layer of ultra-thin flexible substrate, a metal ground layer, and a flexible foam layer sandwiched between them. Then, by introducing two pairs of vertical slots to the radiation patch printed on the flexible substrate, two radiation nulls are realized at both band edges without extra circuits. Moreover, to mitigate the deterioration of in-band radiation under different curvature, a pair of inverted F-shaped slots are loaded on the radiation patch. The coupling of F-shaped slots suppresses non-radiated lateral current components along the curvature direction, maintaining stable performance after bending. In addition, deformation analysis of the proposed antenna with a three-layer human tissue model under different bending radii is carefully carried out, showing stable bandwidth, effective out-of-band radiation suppression, and low specific absorption rate (SAR) value. To verify this method, a prototype is fabricated. Measurements are conducted both in free space and conformal on the curved body tissue. The results show that the proposed antenna achieves a −10 dB impedance bandwidth from 2.7 GHz to 3 GHz, an out-of-band radiation suppression more than 11 dB with maxmium suppression of 23 dB, and an average gain of 8.5 dBi. As a flexible wearable antenna with stable performance and integrated reliable filtering features, it has several advantages including flexible wearable structure, stable filtering properties, lightweight characteristic, and low SAR. This makes it an excellent candidate for wearable IoT applications.

With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks. This paper proposes an energy-scheduling approach that jointly optimizes energy efficiency, task completion rate, and task waiting time for power transfer from low Earth orbit satellites to terrestrial mobile user devices (UDs). To address scheduling challenges caused by satellite and UD mobility and channel uncertainty from stochastic propagation effects, we decompose the problem into three subproblems within a three-layer predictive framework: 1) a state prediction layer forecasts UD and satellite states; 2) an interaction mapping layer uses a graph neural network (GNN) to model energy transfer efficiency; and 3) a decision-making layer determines the energy allocation plan. Distinct machine learning (ML) methods are tailored to each layer. To balance the competing objectives, we adopt a multi-objective reinforcement learning (MORL) technique that scalarizes them into a weighted-sum reward, transforming the multi-objective problem into a tractable single-objective problem. We further introduce a multi-agent deep learning model integrating self-attention with multi-agent proximal policy optimization (MAPPO) to improve objective balancing. Simulation results show that the proposed approach achieves a better overall trade-off than baseline methods, maintaining competitive task completion rates and energy efficiency while reducing task waiting times, and remains robust under highly variable conditions.

In this paper, we consider a synthetic aperture secure beamforming approach for a virtual multiple-input multiple-output (MIMO) broadcast channel in the presence of hybrid wiretapping environments. Our goal is to design the flight node deployment constructed by a single-antenna mobile autonomous aerial vehicle (AAV), corresponding transmission symbol strategy, transmit precoding, and received beamforming to maximize the system channel capacity. Leveraging the synthetic aperture beamforming, we aim to provide spatial gain along a predefined angle in free space while reducing it in others and thus enhance physical layer (PHY) security. To this end, we analyze the expression of the asymptotic channel eigenvalues to optimize the AAV flight node deployment. For the optimal precoding design, an energy-efficient method that minimizes the transmit power consumption is studied based on the given virtual MIMO channel, while meeting the quality of service (QoS) for the base station (BS), leakage tolerance of eavesdroppers (Eves), and per-node power constraints. The power minimization problem is a non-convex program, which is then reformulated as a tractable form after some mathematical manipulations. Moreover, we design the received beamforming by applying the linearly constrained minimum variance (LCMV) method such that the jamming can be effectively suppressed. Numerical results demonstrate the superiority of the proposed method in promoting capacity.

Magnetic induction (MI) communication is an effective underground emergency communication technique after disasters such as landslides, mine collapses, and earthquakes, due to its advantages in mediums such as soil, concrete, and metals. However, the propagation mediums in practical MI based underground emergency communications are usually diverse and composed randomly due to the impact of disasters, which poses a challenge for MI communication in practical applications. In this paper, we formulate a statistical fading channel model, which reflects the random composition of diverse mediums and is shown to follow a lognormal distribution. To mitigate the impact of diverse medium fading, Multi-frequency Resonating Compensation (MuReC) based coils are used to achieve multi-band transmission. Then, we analyze the performance of MuReC based multi-band MI communication with diverse medium fading and derive the expressions of signal-to-noise ratio (SNR) probability density functions, ergodic capaciteis, average bit error rates (BERs), and outage probabilities for both multiplexing and diversity cases. Numerical results show that MuReC based multi-band transmission schemes can effectively reduce the impact of diverse medium fading and enhance the performance.

In virtual reality (VR) applications, 360° images are crucial for delivering immersive and panoramic experiences. However, the substantial data volumes create significant challenges for network storage and bandwidth. Additionally, transmission channel noise can further degrade the user experience by introducing visual distortions. To address these challenges, in this paper we propose a condensed semantic communication framework, specifically the channel denoising-based latent consistency model (CDLCM), designed for efficient 360° image transmission. The CDLCM compresses transmission data by employing deep neural networks (DNNs) to extract multiscale semantic features, which are then condensed via vector quantization (VQ). While this approach reduces transmission overhead, it may result in the loss of crucial image details and increase vulnerability to noise. To counteract these effects, the CDLCM integrates a spherical attention mechanism to detect and correct VQ errors caused by noise during inverse quantization. Additionally, the latent consistency model (LCM) iteratively denoises and restores lost image details, ensuring high-quality reconstruction. The framework also adapts to estimated channel noise by dynamically adjusting the diffusion steps to improve computational efficiency. Numerical experiments verify that the CDLCM not only reduces transmission overhead but also achieves superior reconstruction quality for 360° images compared to state-of-the-art methods. Overall, CDLCM offers an effective solution that balances compression efficiency and reconstruction fidelity, making it well-suited for immersive 360° image transmission.

Evaluating network-wide fairness is challenging because it is not a static property but one highly sensitive to Service Level Agreement (SLA) parameters. This paper introduces a complete analytical framework to transform fairness evaluation from a single-point measurement into a proactive engineering discipline centered on a predictable sensitivity landscape. Our framework is built upon a QoE-Imbalance metric whose form is not an ad-hoc choice, but is uniquely determined by a set of fundamental axioms of fairness, ensuring its theoretical soundness. To navigate the fairness landscape across the full spectrum of service demands, we first derive a closed-form covariance rule. This rule provides an interpretable, local compass, expressing the fairness gradient as the covariance between a path’s information-theoretic importance and its parameter sensitivity. We then construct phase diagrams to map the global landscape, revealing critical topological features such as robust “stable belts” and high-risk “dangerous wedges”. Finally, an analysis of the landscape’s curvature yields actionable, topology-aware design rules, including an optimal “Threshold-First” tuning strategy. Ultimately, our framework provides the tools to map, interpret, and navigate the landscape of system sensitivity, enabling the design of more robust and resilient networks.

Reconfigurable Intelligent Surfaces (RIS) represent a transformative technology for sixth-generation (6G) wireless communications, but it suffers from a significant limitation, namely the double-fading attenuation. Active RIS has emerged as a promising solution, effectively mitigating the attenuation issues associated with conventional RIS-assisted systems. However, the current academic work on active RIS focuses on the system-level optimization of active RIS, often overlooking the development of models that are compatible with its electromagnetic (EM) and physical properties. The challenge of constructing realistic, EM-compliant models for active RIS-assisted communication, as well as understanding their implications on system-level optimization, remains an open research area. To tackle these problems, in this paper we develop a novel EM-compliant model with mutual coupling (MC) for active RIS-assisted wireless systems by integrating the developed scattering-parameter (S-parameter) based active RIS framework with multiport network theory, which facilitates system-level analysis and optimization. To evaluate the performance of the EM-compliant active RIS model, we design the joint optimization scheme based on the transmit beamforming at the transmitter and the reflection coefficient at the active RIS to maximize the achievable rate of EM-compliant active RIS-assisted MIMO system. To tackle the inherent non-convexity of this problem, we employ the Sherman-Morrison inversion and Neumann series (SMaN)-based alternating optimization (AO) algorithm. Simulation results verified that EM property (i.e., MC effect) is an indispensable factor in the optimization process of MIMO systems. Neglecting this effect introduces a substantial performance gap, highlighting its significance in the more pronounced the MC effect is, the greater the gap in achievable rates.

Although the emerging reconfigurable intelligent surface (RIS) paves a new way for next-generation wireless communications, it suffers from inherent flaws, i.e., double-fading attenuation effects and half-space coverage limitations. The state-of-the-art double-face active (DFA)-RIS architecture is proposed for significantly amplifying and transmitting incident signals in full-space. Despite the efficacy of DFA-RIS in mitigating the aforementioned flaws, its potential drawback is that the complex active hardware also incurs intolerable energy consumption. To overcome this drawback, in this paper we propose a novel dynamic energy-saving design for the DFA-RIS, called the sub-array based DFA-RIS architecture. This architecture divides the DFA-RIS into multiple sub-arrays, where the signal amplification function in each sub-array can be activated/deactivated dynamically and flexibly. Utilizing the above architecture, we develop the joint optimization scheme based on transmit beamforming, DFA-RIS configuration, and reflection amplifier (RA) operating pattern to maximize the energy efficiency (EE) of the DFA-RIS assisted multiuser multiple-input-single-output (MISO) system considering the imperfect channel state information (CSI) case. Then, the constrained stochastic majorization-minimization (CSMM) based AO algorithm address non-convex problems. Simulation results verified that our proposed sub-array based DFA-RIS architecture can benefit the EE of the system more than other RIS architectures.

Heterogeneous unmanned aerial vehicle (UAV) networks embedded with reconfigurable intelligent surfaces (RISs) present a promising paradigm for emergency wireless communications (EWC), offering enhanced coverage and resilience in harsh environments. However, extreme conditions in disaster areas necessitate robust performance evaluation under practical impairments, including outdated/imperfect channel state information (CSI) and discrete RIS phase shifts. Existing works lack a unified analytical framework for modeling CSI errors, employing inconsistent approaches that treat errors either as channel gain or as equivalent interference, leading to ambiguous benchmarks. To address this, we propose the $ζ$-Model, a unified receiver-equivalent signal-to-noise (SNR) framework that continuously parameterizes residual-error exploitability via $ζ$. This framework unifies the information-theoretic model (ITM) and the engineering baseline model (EBM) as the optimistic and pessimistic benchmark receiver treatments, while incorporating the simplified engineering model (SEM) as a tractable approximation. By employing the Fisher-Snedecor $\mathcal{F}$ distribution to capture severe fading and shadowing, we derive moment-matching-based closed-form or finite-sum approximate expressions and asymptotic expressions for average capacity (AC), effective capacity (EC), and outage probability (OP) under the proposed unified framework and its boundary cases. Validated by Monte Carlo simulations, our framework quantifies performance limits and provides crucial insights for designing robust and efficient EWC systems under various channel conditions and system impairments.

Task-Oriented Semantic Communication (TOSC) has been regarded as a promising communication framework, serving for various Artificial Intelligence (AI) task driven applications. The existing TOSC frameworks focus on extracting the full semantic features of source data and learning low-dimensional channel inputs to transmit them within limited bandwidth resources. Although transmitting full semantic features can preserve the integrity of data meaning, this approach does not attain the performance threshold of the TOSC. In this paper, we propose a Task-oriented Adaptive Semantic Communication (TasCom) framework to effectively facilitate the inference of different AI tasks. Based on the Generative AI (GAI) techniques, we first propose a Joint Source-Channel Coding (JSCC) that which only extracts and fuses task-related semantic features, and then transmits them to achieve efficient task-oriented semantic transmission. Then, we propose a generative training algorithm to train the proposed JSCC for optimal performance. Furthermore, an Adaptive Coding Controller (ACC) is proposed to find the optimal coding scheme for the proposed JSCC, which allows the semantic features with significant contributions to the task inference to preferentially occupy limited bandwidth resources for wireless transmission. The simulation results show that the proposed TasCom outperforms the existing TOSC and traditional codec schemes on the object detection and instance segmentation tasks under all considered channel conditions.

The spectrum sharing and open nature of wireless channels enable integrated sensing and communication (ISAC) susceptible to hostile jamming attacks, particularly in the context of partial band/broadband jamming attacks. How to significantly improve the anti-jamming performance of ISAC systems with unknown jamming channel state information (CSI) and limited bandwidth is an urgent problem to be exploxied. Due to the intrinsic orthogonality and rich angular information of orbital angular momentum (OAM), vortex electromagnetic waves with helical phase fronts have shown great potential to achieve high-precision position estimation of radar and strong anti-jamming capability of wireless communication. Focusing on solving the anti-jamming problem of ISAC mentioned above, in this paper we propose a novel ISAC for anti-jamming with OAM scheme, where the ISAC transmitter can simultaneously sense the position of jammers with dynamic behavior and send data to multiple OAM legitimate users. Specifically, we develop the enhanced multiple-signal-classification (EMUSIC) based three-dimensional (3D) position estimation scheme with continuous sensing in both two-dimensional (2D) frequency and angular domains to accurately estimate the position of the jammer, thus acquiring the jamming CSI. According to the estimated jamming CSI, we design the joint transmit-receive beamforming and power allocation alternating optimization scheme, where the transmit and receive beamforming matrices are dynamically adjusted to significantly mitigate inter-mode interference, inter-user interference, and jamming, thus maximizing the achievable sum rates (ASRs) of all users. Numerical results demonstrate that our proposed scheme can significantly increase the ASR under broadband jamming attacks and achieve high-precision estimation of targets as compared with the conventional multiple-input-multiple-output (MIMO)-based ISAC.

Active reconfigurable intelligent surface (RIS) emerges as an effective technique to resist the double-fading attenuation of passive RIS. By embedding with power harvesting function, it further evolves to zero-power active RIS, which can effectively enhance the flexibility of RIS deployment without external power demand. Nevertheless, existing works neglected the inherent difficulty of channel estimation (CE) for RIS-assisted systems, and the discrete phase shift constraint in practical deployment. In this paper we design a new element-wise RIS architecture and propose a distributed location-aided transmission scheme with low complexity to enhance the reflected gain for channel state information (CSI)-limited RIS-assisted near-field communications. Specifically, the new element-wise RIS provides dynamic element selection capability with low hardware resources. Based on Fresnel diffraction theory, we construct the mapping from locations in space-domain to phase distributions of waves in phase-domain and reveal the priority of elements for harvesting and reflecting. Then, the distributed beamforming design with the phase of determine-then-align is proposed, where the estimation overhead reduction stems from exempted requirements of RIS-associated CE at base station (BS). The asymptotic analysis indicates that the proposed scheme can achieve the optimal gain with a fixed proportion of reflective elements when RIS is large, followed by simulations to verify its superiority to other protocols.

The exponential growth of wireless devices and stringent reliability requirements of emerging applications demand fundamental improvements in distributed channel access mechanisms for unlicensed bands. Current Wi-Fi systems, which rely on binary exponential backoff (BEB), suffer from suboptimal collision resolution in dense deployments and persistent fairness challenges due to inherent randomness. This paper introduces a multiagent reinforcement learning framework that integrates artificial intelligence (AI) optimization with legacy device coexistence. We first develop a dynamic backoff selection mechanism that adapts to real-time channel conditions through access deferral events while maintaining full compatibility with conventional CSMA/CA operations. Second, we introduce a fairness quantification metric aligned with enhanced distributed channel access (EDCA) principles to ensure equitable medium access opportunities. Finally, we propose a centralized training decentralized execution (CTDE) architecture incorporating neighborhood activity patterns as observational inputs, optimized via constrained multi-agent proximal policy optimization (MAPPO) to jointly minimize collisions and guarantee fairness. Experimental results demonstrate that our solution significantly reduces collision probability compared to conventional BEB while preserving backward compatibility with commercial Wi-Fi devices. The proposed fairness metric effectively eliminates starvation risks in heterogeneous scenarios.

IEEE 802.11be (Wi-Fi 7) introduces Multi-Link Operation (MLO) as a While MLO offers significant parallelism and capacity, realizing its full potential in guaranteeing strict delay bounds and optimizing Quality of Service (QoS) for diverse, heterogeneous traffic streams in complex multi-link scenarios remain a significant challenge. This is largely due to the limitations of static Enhanced Distributed Channel Access (EDCA) parameters and the complexity inherent in cross-link traffic management. To address this, this paper investigates the correlation between overall MLO QoS indicators and the configuration of EDCA parameters and Acess Catagory (AC) traffic allocation among links. Based on this analysis, we formulate a constrained optimization problem aiming to minimize the sum of overall packet loss rates for all access categories while satisfying their respective overall delay violation probability constraints. A Genetic Algorithm (GA)-based MLO EDCA QoS optimization algorithm is designed to efficiently search the complex configuration space of AC assignments and EDCA parameters. Experimental results demonstrate that the proposed approach’s efficacy in generating adaptive MLO configuration strategies that align with diverse service requirements. The proposed solution significantly improves delay distribution characteristics, and enhance QoS robustness and resource utilization efficiency in high-load MLO environments.

In emergency scenarios, the dynamic and harsh conditions necessitate timely trajectory adjustments for drones, leading to highly dynamic network topologies and potential task failures. To address these challenges, a collaborative computing strategy based strapdown inertial navigation system (SINS) prediction for emergency UAVs network (EUN) is proposed, where a two-step weighted time expanded graph (WTEG) is constructed to deal with dynamic network topology changes. Furthermore, the task scheduling is formulated as a Directed Acyclic Graph (DAG) to WTEG mapping problem to achieve collaborative computing while transmitting among UAVs. Finally, the binary particle swarm optimization (BPSO) algorithm is employed to choose the mapping strategy that minimizes end-to-end processing latency. The simulation results validate that the collaborative computing strategy significantly outperforms both cloud and local computing in terms of latency. Moreover, the task success rate using SINS is substantially improved compared to approaches without prior prediction.

Due to their flexibility and dynamic coverage capabilities, Unmanned Aerial Vehicles (UAVs) have emerged as vital platforms for emergency communication in disaster-stricken areas. However, the complex channel conditions in high-speed mobile scenarios significantly impact the reliability and efficiency of traditional communication systems. This paper presents an intelligent emergency communication framework that integrates Orthogonal Time Frequency Space (OTFS) modulation, semantic communication, and a diffusion-based denoising module to address these challenges. OTFS ensures robust communication under dynamic channel conditions due to its superior anti-fading characteristics and adaptability to rapidly changing environments. Semantic communication further enhances transmission efficiency by focusing on key information extraction and reducing data redundancy. Moreover, a diffusion-based channel denoising module is proposed to leverage the gradual noise reduction process and statistical noise modeling, optimizing the accuracy of semantic information recovery. Experimental results demonstrate that the proposed solution significantly improves link stability and transmission performance in high-mobility UAV scenarios, achieving at least a 3dB SNR gain over existing methods.

The vortex electromagnetic wave carried by multiple orthogonal orbital angular momentum (OAM) modes in the same frequency band can be applied to the field of wireless communications, which greatly increases the spectrum efficiency. The uniform circular array (UCA) structure is widely used to generate or receive vortex electromagnetic waves with multiple OAM-modes. However, the maximum number of orthogonal OAM-modes based on UCA is usually limited to the number of array-elements of the UCA antenna, leaving how to utilize more OAM-modes to achieve higher spectrum efficiency given a fixed number of array-elements as an intriguing question. In this paper, we propose an N-dimensional quasi-fractal UCA (ND QF-UCA) antenna structure in different fractal geometry layouts to break through the limits of array-elements number on OAM-modes number. We develop the N-dimensional OAM modulation (NOM) and demodulation (NOD) schemes for OAM multiplexing transmission with the OAM-modes number exceeding the array-elements number, which is beyond the traditional concept of multiple antenna based wireless communications. Then, we investigate different dimensional multiplex transmission schemes based on the corresponding QF-UCA antenna structure with various array-elements layouts. Simulation results show that our proposed schemes can obtain a higher spectrum efficiency.

The harsh environment and scarce resources post-disaster drive the equipment to be miniaturized and portable. Based on this, integrated sensing and communication (ISAC) systems play a significant role in providing emergency wireless networks. In order to reduce the hardware cost, a hybrid beamforming (HBF) assisted millimeter-wave (mmWave) ISAC system, which exploits the limited number of radio frequency (RF) chains, is considered in this paper. However, the HBF structure reduces the spatial degrees of freedom, thus leading to increased interference among communication users and radar sensing. To solve this problem, a rate-splitting multiple access (RSMA) strategy is adopted to enhance the emergency mmWave-ISAC system. We formulate the weighted sum rate (WSR) maximization objective by jointly designing common rate allocation and HBF. Then, we propose the penalty dual decomposition (PDD) coupled with the weighted mean squared error (WMMSE) method to solve this high-dimensional non-convex problem. Numerical results demonstrate the effectiveness of the proposed algorithm and show that the RSMA-ISAC scheme outperforms other benchmark schemes.

Due to the flexibility and superior Line-of-Sight (LoS), Unmanned Aerial Vehicles (UAVs) have shown significant potential in Internet of Things (IoT) data collection. As the scale of IoT expands rapidly, higher demands are placed on energy efficiency and data transmission reliability. However, the limited battery life of UAVs restricts the application of a single UAV in large-scale, high-density wireless networks for data collection and data being sent to cloud for processing leads to poor Quality of Service (QoS) in traditional networks. To address these challenges, this paper aims to minimize UAV energy consumption while ensuring data collection reliability, proposing an energy-efficient data collection scheme in a cooperative multi-UAV scene. This scheme divides the non-convex problem into three subproblems for solution. First, to ensure the reliability of data transmission, introducing the guarantee of outage probability as a constraint, hovering altitude of the UAV is optimized to achieve the maximum coverage radius in the target area. Second, an Affinity Propagation (AP) clustering algorithm is introduced to partition the geographical area into the clusters with the minimum number which corresponds to the number of UAV movements. Finally, the set of horizontal position of the UAV hovering points can be optimized. And then, based on the three-dimensional (3D) coordinates, a hierarchical path planning algorithm for multiple UAVs is proposed which is formulated as a minimize maximum multiple traveling salesman problem (min-max MTSP) and solved effectively. Simulation results demonstrate that compared to existing methods, the proposed multi-UAV data collection scheme can ensure the reliability and decrease energy consumption.

As the most driven force of future 6G communications, Internet of Everything (IoE) requires energy-constrained IoE devices (IDs) to realize interconnection among users, information, and things. Wireless-powered orbital angular momentum (OAM) communications can be used for IDs to achieve simultaneous wireless power transfer and multiple independent information transmissions by different OAM modes. However, the practical blockage and access capability enhancement impose crucial challenges for wireless-powered OAM communications. Therefore, for blocked line-of-sight scenario, this paper proposes non-orthogonal multiple access (NOMA)-enhanced intelligent reflecting surface (IRS) for wireless-powered OAM communications by jointly optimizing power allocation and passive beamforming. First, we formulate the information capacity and harvested energy optimization problem under the constraints of unit-modulus reflecting phase shift, minimum power transfer requirement, and minimum information transmission demand. Then, the harvested energy is converted to the achievable capacity by the energy utilization for information transmission, thus forming the sum capacity maximization problem. Finally, we decompose the sum capacity maximization problem into three subproblems, and obtain the optimal power allocation and passive beamforming by iteration. Simulation results validate the proposed NOMA-enhanced IRS of wireless-powered OAM communications, and demonstrate that the joint power allocation and passive beamforming achieves better capacity performance than the other optimization schemes.

The vortex electromagnetic wave carrying multiple orthogonal orbital angular momentum (OAM) modes in the same frequency band can be applied to the field of wireless communications, which greatly increases the spectrum efficiency. The uniform circular array (UCA) is widely used to generate and receive vortex electromagnetic waves with multiple OAM-modes. However, the maximum number of orthogonal OAM-modes based on UCA is usually limited to the number of array-elements of the UCA antenna, leaving how to utilize more OAM-modes to achieve higher channel capacity with a fixed number of array-elements as an intriguing question. In this paper, we propose anN-dimensional quasi-fractal UCA (ND QF-UCA) antenna structure in different fractal geometry layouts to break through the limits of array-elements number on OAM-modes number. We develop theN-dimensional OAM modulation (NOM) and demodulation (NOD) schemes for OAM multiplexing transmission with the OAM-modes number exceeding the array-elements number, which is beyond the traditional concept of multiple antenna based wireless communications. Then, we investigate different dimensional multiplexing transmission schemes based on the corresponding QF-UCA antenna structure with various array-element layouts and evaluate the optimal layout type and dimension to obtain the highest channel capacity with a fixed number of array-elements. Simulation results show that our proposed schemes can obtain a higher spectrum efficiency, surpassing those of alternative array-element layouts of QF-UCA and the traditional multiple antenna systems.

Transmission at millimeter-wave (mmWave) frequencies is promising in the sixth generation (6G) and beyond systems due to abundant spectrum resources. Reconfigurable intelligent surface (RIS) assisted mmWave communications has been envisioned as an effective technique to effectively compensate for propagation loss and increase the capacity of line-of-sight (LOS) multiple-input-multiple-output (MIMO) wireless communication. To decrease the complexity of transceivers, the uniform circular array (UCA) is an effective antenna structure for RIS-assisted mmWave MIMO wireless communications when the transmit and receive UCAs are aligned. However, the complexity increases when the transceivers are not perfectly aligned and it is difficult to ensure the strict alignment in practice. To achieve low-complexity transceivers and increase the spectrum efficiency for RIS-assisted mmWave MIMO wireless communications, in this paper we investigate the concentric UCAs based spherical RIS-assisted mmWave MIMO wireless communications. The channel model is derived for the concentric UCAs based spherical RIS-assisted mmWave MIMO wireless communications. Then, a beamforming scheme at the transmit UCA, a phase compensation scheme at the spherical RIS, and a predetection scheme at the receive UCA, which can decrease the complexity of transceivers, are developed in the misaligned scenario. Our proposed fast symbol-wise maximum likelihood (ML) detection scheme can recover the signals with low complexity. Furthermore, we propose an algorithm to achieve the optimal spherical RIS design for increasing the spectrum efficiency of the concentric UCAs based spherical RIS-assisted mmWave MIMO wireless communications. Simulation results are presented to illustrate the theory for the concentric UCAs based spherical RIS-assisted mmWave MIMO wireless communications.

The advent of massive ultra-reliable and low-latency communications (mURLLC) has introduced a critical class of time- and reliability-sensitive services in next-generation wireless networks. This shift has attracted significant research attention, driven by the need to meet stringent quality-of-service (QoS) requirements. In this context, non-orthogonal multiple access (NOMA) systems have emerged as a promising solution to enhance mURLLC performance by providing substantial enhancements in both spectral efficiency and massive connectivity, particularly through finite blocklength coding (FBC) techniques. Nevertheless, owing to the dynamic nature of wireless network environments and the complex architecture of FBC-enhanced NOMA systems, the research on the efficient design of optimizing the system performance for maximizing system capacity while guaranteeing the tail distributions in terms of new statistical QoS constraints for delay and error-rate is still in its infancy. In an effort to address these challenges, we put forth the formulation and solution of$\epsilon $-effective capacity problems tailored for uplink FBC-enhanced NOMA systems, specifically catering to ensure statistical delay and error-rate bounded QoS requirements. In particular, we establish uplink two-user FBC-enhanced NOMA system models by applying the hybrid successive interference cancellation (SIC). We also develop the concept of the$\epsilon $-effective capacity and propose the optimal power allocation policies to maximize the$\epsilon $-effective capacity and$\epsilon $-effective energy efficiency while upper-bounding both delay and error-rate. We conduct a set of simulations to validate and evaluate our developed optimization schemes over FBC-enhanced NOMA systems.

In this paper, we propose a virtual full-duplex (VFD) technique with zero-interval modulation and sampling (ZIMS), where two half-duplex (HD) transceivers can simultaneously transmit signals and each transceiver can effectively receive the desired information. In ZIMS-VFD, the transceiver inserts a zero-interval for each symbol in the transmit signal and provides self-interference (SI)-free intervals for itself. Meanwhile, it samples the receive signal in the provided SI-free intervals and restores the desired symbols. Based on orthogonal frequency division multiplexing (OFDM), we formulate the system model and show the transmit signal structure. Then, we give the transceiver design for single input single output (SISO) ZIMS-VFD and extend it to multiple input multiple output (MIMO) communications. Numerical results verify our theoretical analyses and show that ZIMS-VFD can effectively increase the capacity and approach the FD without SI.

In recent years, reconfigurable intelligent surfaces (RIS) have garnered significant attention for their ability to control the phase shifts in reflected signals. By intelligently adjusting these phases, RIS can establish seamless direct paths between communication devices obstructed by obstacles, eliminating the need for forwarding and significantly reducing system overhead associated with relaying. This capability is crucial in multi-hop ad hoc networks requiring multiple relay steps. Consequently, the concept of incorporating multi-hop RIS into wireless multi-hop relay networks has emerged. In this paper, we propose a novel network model where each UAV communication node is equipped with a RIS, facilitating seamless connections in multi-hop relay wireless networks. We analyze the performance of this model by integrating RIS-assisted physical layer modeling into the seamless connection network framework and conducting a detailed comparative analysis of RIS-assisted and conventional connections. At the medium access layer, we introduce a RIS-DCF MAC protocol based on the IEEE 802.11 distributed coordination function (DCF), modeling the medium access process as a two-hop access scenario. Our results demonstrate that the seamless connections and diversity gain provided by RIS significantly enhance the performance of multi-hop relay wireless networks.

Due to the broadcast and open characteristics of wireless channels, near-field physical layer security has attracted much attention to facilitate wireless information security against illegitimate eavesdropping. However, highly correlated channels between the legitimate transceivers and eavesdroppers for existing near-field line-of-sight multiple-input multiple-output (MIMO) systems with low degrees of freedom make it difficult to efficiently distinguish eavesdropping channels and legitimate channels in the angular domain, thus resulting in low secrecy rates. Fortunately, orbital angular momentum (OAM) with rich phase information shows great potential to enhance the physical layer security. To significantly increase the secrecy rates of near-field wireless communications, in this paper we propose the double-reconfigurable-intelligent-surface (RIS) assisted OAM secure scheme, where RISs with few reflecting elements are easily deployed to reconstruct the direct links blocked by obstacles between the legitimate transceivers, mitigate the inter-mode interference caused by the misalignment of legitimate transceivers, and adjust the OAM beams direction to interfere with eavesdroppers. Meanwhile, due to the unique orthogonality among OAM modes, the OAM-based joint index modulation and artificial noise scheme is proposed to weaken the information acquisition by eavesdroppers while increasing the achievable rate with the low cost of legitimate communications. To maximize the secrecy rate of our proposed scheme, we develop the Riemannian manifold conjugate gradient (RMCG)-based alternative optimization (AO) algorithm to jointly optimize the transmit power allocation of OAM modes and phase shifts of double RISs. Numerical results show that our proposed double-RIS-assisted OAM near-field secure scheme outperforms the existing works in terms of the secrecy rate and the eavesdropper’s bit error rate.

Orbital angular momentum (OAM) in electromagnetic (EM) waves can significantly enhance spectrum efficiency in wireless communications without requiring additional power, time, or frequency resources. Different OAM modes in EM waves create orthogonal channels, thereby improving spectrum efficiency. Additionally, OAM waves can more easily maintain orthogonality in line-of-sight (LOS) transmissions, offering an advantage over multiple-input and multiple-output (MIMO) technology in LOS scenarios. However, challenges such as divergence and crosstalk hinder OAM’s efficiency. Additionally, channel modeling for OAM transmissions is still limited. A reliable channel model with balanced accuracy and complexity is essential for further system analysis. In this paper, we present a quasi-deterministic channel model for OAM channels in the 5.8 GHz and 28 GHz bands based on measurement data. Accurate measurement, especially at high frequencies like millimeter bands, requires synchronized RF channels to maintain phase coherence and purity, which is a major challenge for OAM channel measurement. To address this, we developed an 8-channel OAM generation device at 28 GHz to ensure beam integrity. By measuring and modeling OAM channels at 5.8 GHz and 28 GHz with a modified 3D geometric-based stochastic model (GBSM), this study provides insights into OAM channel characteristics, aiding simulation-based analysis and system optimization.

In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled secure communication scenario that a cluster of UAVs performs a virtual non-uniform linear array (NULA) to communicate with a base station (BS) in the presence of eavesdroppers (Eves). Our goal is to design the UAV topology, trajectory, and precoding to maximize the system channel capacity. To this end, we convert the original problem into equivalent two-stage problems. Specifically, we first try to maximize the channel gain by meticulously designing the UAV topology. We then study the joint optimization of the trajectory and precoding for total transmit power minimization while satisfying the constraints on providing quality of service (QoS) assurance to the BS, the leakage tolerance to Eves, the per-UAV transmit power, the initial/final locations, and the cylindrical no-fly zones. For the UAV topology design, we prove that the topology follows the Fekete-point distribution. The design of trajectory and precoding is formulated as a non-convex optimization problem which is generally intractable. Subsequently, the non-convex constraints are converted into convex terms, and a double-loop search algorithm is proposed to solve the transmit power minimization problem. Introduce random rotation offsets so as to perform a dynamic stochastic channel to enhance the security. Numerical results demonstrate the superiority of the proposed method in promoting capacity.

To enable cost-effective universal access and the enhancement of current communication services, the space-air-ground integrated networks (SAGINs) have recently been developed due to their exceptional 3D coverage and the ability to guarantee rigorous and multidimensional demands for quality-of-service (QoS) provisioning, including delay and reliability across vast distances. The integration of spatial, aerial, and terrestrial dimensions is thus regarded as a critical facilitator for accommodating massive Ultra-Reliable Low-Latency Communications (mURLLC) applications. In response to the complex, heterogeneous, and dynamic serving scenarios and stringent performance expectations for 6G SAGINs, it is crucial to undertake modeling, assurance, and analysis of the key technologies, aligned with the diverse demands for QoS provisioning in the non-asymptotic regime, i.e., when implementing finite blocklength coding (FBC) as a new dimension for error-rate bounded QoS metric. However, how to design new statistical QoS-driven performance modeling approaches that accurately delineate the complex and dynamic behaviors of networks, particularly in terms of constraining both delay and error rate, persists as a significant challenge for implementing mURLLC within 6G SAGINs in the finite blocklength regime. To overcome these difficulties, in this paper we propose to develop a set of analytical modeling frameworks for 6G SAGIN in supporting statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish the SAGIN system architecture model. Second, the aggregate interference and decoding error probability functions are modeled and examined by using Laplace transform. Third, we introduce modeling techniques aimed at defining the$\epsilon $-effective capacity function as a crucial metric for facilitating statistical QoS standards with respect to delay and error-rate. To validate the effectiveness of the developed performance modeling schemes, we have executed a series of simulations over SAGINs.

Existing deep learning-based semantic communication (DeepSC) systems are typically trained for specific single-channel condition, which restricts the overall adaptability and resilience to interference. To address this limitation, we propose an innovative semantic adaptive feature extraction (SAFE) network that dynamically generates and fuses multiple sub-semantics, each characterized by unique features that can be tailored to different channel conditions. This paper also introduces three advanced learning algorithms to refine and enhance the generated sub-semantics, optimizing the semantic successive refinement performance of the SAFE network. Furthermore, we integrate a novel interference-aware semantic transmission method based on non-orthogonal multiple access (NOMA) into this framework. This approach enables users to adaptively select appropriate subsets for efficient transmission and image reconstruction, tailored to the prevailing channel interference conditions. Through extensive simulation experiments, we demonstrate the framework’s capability to generate and transmit semantics under diverse channel interference scenarios adaptively, and verify the effectiveness through both objective and subjective quality evaluations.

Reconfigurable intelligent surface (RIS) has attracted much attention to assist millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) wireless communications over line-of-sight (LOS) channels for performance improvement due to low hardware cost and low power consumption. The uniform circular array (UCA) is an effective antenna structure with low-complexity transceivers for RIS-assisted mmWave MIMO wireless communications where the transceivers are aligned. However, it is difficult to achieve strict alignment between the transceivers in practice. To address the challenges, in this paper we investigate the UCA based sphere RIS-assisted mmWave MIMO wireless communications with misaligned transceivers and sphere RIS. The channel model is investigated for the UCA based sphere RIS-assisted mmWave MIMO wireless communications. Furthermore, a precoding scheme at the transmitter, a phase compensation scheme at the sphere RIS, and a predecoding scheme at the receiver, which can convert the channel matrix into an equivalent circulant matrix, are developed. Then, our proposed fast symbol-wise maximum likelihood (ML) detection scheme can recover the signals with low complexity. Simulation results are presented to illustrate the theory for the UCA based sphere RIS-assisted mmWave MIMO wireless communications.

In virtual reality (VR) applications, 360-degree images play a pivotal role in crafting immersive experiences and offering panoramic views, thus enhancing the visual experience of the user. However, the voluminous data generated by 360-degree images poses challenges in network storage and bandwidth. To address these challenges, we propose a novel Activation Map-based Vector Quantization (AM-VQ) framework, which is designed to reduce communication overhead for wireless transmission. The proposed AM-VQ scheme uses the Deep Neural Networks (DNNs) with vector quantization (VQ) to extract and compress semantic features. Particularly, the AM-VQ framework utilizes an activation map to adaptively quantize semantic features, thereby reducing data distortion caused by quantization. To further enhance the reconstruction quality of the 360-degree image, adversarial training with a Generative Adversarial Networks (GANs) discriminator is incorporated. Numerical results show that our proposed AM-VQ scheme achieves better performance than the existing Deep Learning (DL) based coding and the traditional coding schemes under the same transmission symbols.

Given the imperative of 6G networks’ ubiquitous connectivity, along with the inherent mobility and cost-effectiveness of unmanned aerial vehicles (UAVs), UAVs play a critical role within 6G wireless networks. Despite advancements in enhancing the UAV-enabled communication systems’ throughput in existing studies, there remains a notable gap in addressing issues concerning user fairness and quality-of-service (QoS) provisioning and lacks an effective scheme to depict the trade-off between system throughput and user fairness. To solve the above challenges, in this paper we introduce a novel fairness control scheme for UAV-enabled wireless communication systems based on a new weighted function. First, we propose a throughput combining model based on a new weighted function with fairness considering. Second, we formulate the optimization problem to maximize the weighted sum of all users’ throughput. Third, we decompose the optimization problem and propose an efficient iterative algorithm to solve it. Finally, simulation results are provided to demonstrate the considerable potential of our proposed scheme in fairness and QoS provisioning.

In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients’ data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm.

Exploiting the reconfigurable intelligent surface (RIS) to mitigate the severe path loss for millimeter-wave (mmWave) communications is a promising approach to meet the demand of ultra-high rates for future wireless communications, benefiting from its energy-efficient and cost-effective features. Especially, the near-field gain can be attained to improve the communication performance with its expanded aperture. However, achieving such superiority necessitates the acquisition of full channel state information (CSI) of RIS-associated channels, which poses a significant challenge in the near-field region, hindering the deployment of RIS. To solve this problem, in this paper we propose a location based scheme as an alternative to mitigate the large overhead of channel estimation for rotatable RIS-assisted near-field communications. In particular, we leverage the properties of Fresnel zone to construct the parametric equations of the Fresnel ellipsoids to characterize the propagation geometry. Then, in the presence of inevitable location uncertainty, we give the criterion of practical discrete phase shift design based on the derived parametric curve, resembling the minimum distance criterion for signal detection. Also, the rotation of RIS is exploited to mitigate the impact of location error. Such processing can further improve the performance of the proposed scheme, verified by analytical and numerical results.

Offloading services to UAV swarms for delay-sensitive tasks in Emergency UAV Networks (EUN) can greatly enhance rescue efficiency. Most task-offloading strategies assumed that UAV s were location-fixed and capable of handling all tasks. However, in complex disaster environments, UAV locations often change dynamically, and the heterogeneity of on-board resources presents a significant challenge in optimizing task scheduling in EUN to minimize latency. To address these problems, a Finite state machines-based Path-following Collaborative computation strategy (FPC) for emergency UAV swarms is proposed. First, an Extended Finite State Machine Space-time Graph (EFSMSG) model is constructed to accurately characterize on-board resources and state transitions while shielding the EUN dynamic characteristic. Based on the EFSMSG, a mathematical model is formulated for the FPC strategy to minimize task processing delay while facilitating computation during transmission. Finally, the Constraint Selection Adaptive Binary Particle Swarm Optimization (CSABPSO) algorithm is proposed for the solution. Simulation results demonstrate that the proposed FPC strategy effectively reduces task processing delay, meeting the requirements of delay-sensitive tasks in emergency situations.

Due to maneuverability and Line-of-Sight (LoS) path, unmanned aerial vehicle (UAV) can serve as aerial base station to provide communication coverage and data collection for emerging Internet of Things (IoT) in the hotspot. However, limited spectrum resource and different Quality-of-Service (QoS) requirement impose critical challenges for UAV-aided IoT communications to cover massive IoT equipments (IEs). In this article, we propose the QoS-guaranteed multi-UAV coverage (QMC) scheme with interference management, which determines UAV deployment and spectrum resource allocation, to cover all ground IEs that have different QoS requirements. First, the interference management-based spectrum resource allocation (IMSA) algorithm, which utilizes the tabu search method of graph coloring, is proposed to avoid the interference between UAVs that have same IEs in their coverage. Then, we obtain the QoS-guaranteed single UAV placement (QSUP) algorithm to maximize the capacity of UAV while satisfying different QoS requirements for ground IEs. Finally, based on the IMSA and QSUP algorithms, the QMC scheme with interference management, which serves all ground IEs, is proposed to optimize the deployment of multiple UAVs for maximization of average UAV capacity. Simulation results demonstrate that compared with traditional schemes, the proposed QMC scheme achieves smaller UAV number and higher average UAV capacity due to the efficient interference management.

The goal of this paper is to address the physical layer (PHY) security problem for multi-user multi-input single-output (MU-MISO) Internet of Things (IoT) systems in the presence of passive eavesdroppers (Eves). To this end, we propose an artificial noise (AN)-aided rate-splitting (RS)-based secure beamforming scheme. Our design considers the dual use of common messages and places the research emphasis on hiding the private messages for secure communication. In particular, leveraging AN-aided RS-based beamforming, we aim to maximize the focused secrecy sum-rate (F-SSR) by jointly designing transmit information and AN beamforming while satisfying the desired received constraints for the private messages at IoT devices (IoDs), and per-antenna transmit power constraint at base station. Then, we proposed a two-stage algorithm to iteratively find the optimal solution. By transforming non-convex terms into linear terms, we first reformulate the original problem as a convex program. Next, we recast the optimization problem to an unconstrained problem to obtain the global optimal solutions. Utilizing the duality framework, we further develop an efficient algorithm based on a barrier interior point method to solve the reformulated problem. Simulation results validate the superior performance of our proposed schemes.

Orbital angular momentum (OAM) carried electromagnetic waves have the potential to improve spectrum efficiency in optical and radio-frequency communications due to the orthogonal wavefronts of different OAM modes. However, OAM beams are vortically hollow and divergent, which significantly decreases the capacity of OAM transmissions. In addition, unaligned transceivers in OAM transmissions can result in a high bit error rate (BER). The Talbot effect is a self-imaging phenomenon that can be used to generate optical or radio-frequency OAM beams with periodic repeating structures at multiples of a certain distance along the propagation direction. These periodic structures make it unnecessary for the transceiver antennas to be perfectly aligned and can also alleviate the hollow divergence of OAM beams. In this paper, we propose Talbot-effect-based fractal OAM generation and detection schemes using a uniform circular array (UCA) to significantly improve capacity and BER performance in unaligned OAM transmissions. We first provide a brief overview of fractal OAM. Then, we propose the fractal OAM beam generation and detection schemes. Numerical analysis and simulations verify the effectiveness of our proposed fractal OAM generation scheme and also demonstrate improved capacity and BER performance compared to normal OAM transmissions. We also analyze how the receive UCA radius and the distance between the UCAs impact the capacity and BER performances.

Metaverse is envisioned to shape a virtual digital world accommodating people to live, work, and interact with each other, which requires massive information exchange for frequent updates of panoramic information in the digital world and imposes unprecedented pressure on future networks, so it is desirable to only sample the information updating process covering objects of user’s main attention. Yet under the always-limited wireless capacity compared to the persistently-growing information load, a critically important but unanswered question remains, i.e., how often shall we sample information updating process and deliver it to users, while keeping information at users’ side as fresh as possible. To answer this question, we investigate the statistical age-of-information (AoI) minimization problems to catch varying wireless channels and attention of users. Unlike conventional average or maximum AoI optimization technologies, we concentrate on statistical feature of AoI to more accurately characterize the capability of supporting metaverse applications. The formulated problems are solved by fractional programming. Specifically, using the Dinkelbach’s and quadratic transforms, we derive the adaptive sampling and transmission schemes for cases with single and multiple users, respectively. The interaction among multiple users is also considered. Analyses reveal that the optimized sampling rate shall decrease as the information updating process covers more varying objects or the channel gets poorer. Moreover, when the AoI requirement becomes extremely stringent, the sampling rate approaches a constant. Numerical results validate that our proposals can achieve lower statistical AoI than baseline schemes, thus offering better experiences for metaverse users.

Communication infrastructure is often severely disrupted in post-disaster areas, which interrupts communications and impedes rescue. Recently, the technology of reconfigurable intelligent surface (RIS)-equipped-UAV has been investigated as a feasible approach to assist communication under such conditions. However, the channel characteristics in the post-disaster area rapidly change due to the topographical changes caused by secondary disasters and the high mobility of UAVs. In this paper we develop a new fading-shadowing model to fit the path loss caused by the debris. Following this, we derive the exact distribution of the new channel statistics for a small number of RIS elements and the approximate distribution for a large number of RIS elements, respectively. Then, we derive the closed-form expressions for performance analysis, including average capacity (AC), energy efficiency (EE), and outage probability (OP). Based on the above analytical derivations, we maximize the energy efficiency by optimizing the number of RIS elements and the coverage area by optimizing the altitude of the RIS-equipped UAV, respectively. Finally, simulation results validate the accuracy of derived expressions and show insights related to the optimal number of RIS elements and the optimal UAV altitude for emergency wireless communication (EWC).

Reconfigurable intelligent surface (RIS), composed of nearly passive elements, is regarded as one of the potential paradigms to support multi-gigabit data in real-time. However, in traditional CSI (channel state information) driven frame, the training overhead of channel estimation greatly increases as the number of RIS elements increases to intelligently manipulate the reflected signals. To conveniently use the reflected signal without complex CSI feedback, in this paper we propose a position-aided phase configuration scheme based on the property of Fresnel zone. In particular, we design the impedance based discrete RIS elements with joint absorption mode and reflection mode considering the fabrication complexities, which integrated the property of the Fresnel zone to resist the impact of position error. Then, with joint absorption and 1-bit reflection mode elements, we develop the two-step position-aided ON/OFF states judgement (TPOSJ) scheme and the frame structure to control the ON/OFF state of RIS, followed by analyzing the impacts of mobility and position error on our proposed scheme. Also, we derive the Helmholtz-Kirchhoff integral theorem based power flow. Simulations show that the proposed scheme can manipulate the ON/OFF state intelligently without complex CSI, thus verifying the practical application of our proposed scheme.

Driven by the tremendous demand for real-time data processing in the Internet of Vehicles (IoV), edge computing is envisioned as a promising solution to alleviate the resource limitation on vehicles. Current works on edge task scheduling simply optimize the total system cost and ignore the various constraints of applications, which will result in the reduction of the task completion rate and even cause security accidents. Although few works studied the multi-task deadline-constrained scheduling problem, their complexity is too high, resulting in the explosive growth of the runtime. Spurred by the above issues, the multi-task scheduling problem is formulated to maximize the task completion rate. Further, a Multi-user Efficient Task Scheduling (METS) algorithm is proposed to solve the formulated problem, which consists of three key components: (1) the dominating set-based network clustering that aims to reduce the network scale, (2) the matching-based task assignment to assign tasks that are modeled by the Directed Acyclic Graph (DAG) to their proper clusters, and (3) the intra-cluster DAG scheduling to schedule DAGs to the proper network nodes. Simulation results show that the proposed METS algorithm can significantly improve the task completion rate and reduce the algorithm runtime in an IoV environment with thousand-level network scale and thousand-level task requests.

The development of orbital angular momentum (OAM)-based radio vortex transmission presents a promising opportunity for increasing the capacity of wireless communication in correlated channels due to its inherent orthogonality among different OAM modes. One of the most popular schemes for high-efficient OAM transmission is the digital baseband associated with uniform circular array (UCA) based transceiver. However, the periodicity of complex-exponential feed makes the maximum number of orthogonal signals carried by multiple OAM modes generally restricted to the array-element number of UCA antenna, which poses an open question of how to employ more OAM modes given a fixed number of array elements. Furthermore, signals modulated with high-order OAM modes are difficult to be captured by the receiver due to their serious divergence as propagating in free space, thus severely limiting the capacity of radio vortex communications. To overcome the above challenges, in this paper based on the partly element-overlapped fractal geometry layout and effectively using low-order OAM modes, we propose the quasi-fractal UCA (QF-UCA) antenna based OAM multiplexing transmission. We perform the two-dimension OAM modulation (TOM) and demodulation (TOD) schemes with the orthogonal OAM mode number exceeding the array-element number, which is beyond the traditional concept of multiple antennas based wireless communications. Simulation results show that our proposed scheme can achieve more number of orthogonal multiplexing streams than the maximum number of orthogonal multiplexing corresponding to traditional multiple antenna systems.

Directional modulation and artificial noise (AN)-based methods have been widely employed to achieve physical-layer security (PLS). However, these approaches can only achieve angle-dependent secure transmission. This paper presents an AN-aided decomposed and distributed directional modulation (D3M) scheme for secure wireless communications, which takes advantage of the spatial signatures to achieve an extra range-dimension security apart from the angles. Leveraging decomposed and distributed structure, each of modulated signal is represented by mutually orthogonal in-phase and quadrature branches, which are transmitted by two distributed transmitters to enhance PLS. In particular, we first aim to minimize transmit message power by integrated design of the transmit beamformers, subject to prescribed received signal-to-noise ratio (SNR) for the legitimate user (LU) and no inter-branch interference. This guarantees reliable and accurate transmission for the LU with the minimum transmit message power. Considering the leakage power on the sidelobes, AN is superimposed on the messages to try to mask the confidential information transmission. Simulation results demonstrate the security enhancement of our proposed D3M system.

As one of the pivotal enablers for 6G, satellite-terrestrial integrated networks have emerged as a solution to provide extensive connectivity and comprehensive 3D coverage across the spatial-aerial-terrestrial domains to cater to the specific requirements of 6G massive ultra-reliable and low latency communications (mURLLC) applications, while upholding a diverse set of stringent quality-of-service (QoS) requirements. In the context of mURLLC satellite services, the concept of data freshness assumes paramount significance, as the use of outdated data may lead to unforeseeable or even catastrophic consequences. To effectively gauge the degree of data freshness for satellite-terrestrial integrated communications, the notion of age of information (AoI) has recently emerged as a new dimension of QoS metrics to support time-sensitive applications. Nonetheless, the research efforts directed towards incorporating diverse statistical QoS provisioning metrics, including AoI, delay, and reliability, while accommodating the dynamic and intricate nature of satellite-terrestrial integrated environments, are still in their infancy. To overcome these problems, in this paper we develop analytical modeling formulations/frameworks for statistical QoS over 6G satellite-terrestrial integrated networks using hybrid automatic repeat request with incremental redundancy (HARQ-IR) in the finite blocklength regime. In particular, first we design the satellite-terrestrial integrated wireless network architecture model and AoI metric model. Second, we characterize the peak-AoI bounded QoS metric using HARQ-IR protocol. Third, we develop a set of new fundamental statistical QoS metrics in the finite blocklength regime. Finally, extensive simulations have been conducted to assess and analyze the efficacy of statistical QoS schemes for satellite-terrestrial integrated networks.

Full-duplex (FD) is an attractive technology that can significantly boost the throughput of wireless communications. However, it is limited by the severe self-interference (SI) from the transmitter to the local receiver. In this paper, we propose a new SI cancellation (SIC) scheme based on reconfigurable intelligent surface (RIS), where small RISs are deployed inside FD devices to enhance SIC capability and system capacity under frequency-selective fading channels. The novel scheme can not only address the challenges associated with SIC but also improve the overall performance. We first analyze the near-field behavior of the RIS and then formulate an optimization problem to maximize the SIC capability by controlling the reflection coefficients (RCs) of the RIS and allocating the transmit power of the device. The problem is solved with alternate optimization (AO) algorithm in three cases: ideal case, where both the amplitude and phase of each RIS unit cell can be controlled independently and continuously, continuous phases, where the phase of each RIS unit cell can be controlled independently, while the amplitude is fixed to one, and discrete phases, where the RC of each RIS unit cell can only take discrete values and these discrete values are equally spaced on the unit circle. For the ideal case, the closed-form solution to RC is derived with Karush-Kuhn-Tucker (KKT) conditions. Based on Riemannian conjugate gradient (RCG) algorithm, we optimize the RC for the case of continuous phases and then extend the solution to the case of discrete phases by the nearest point projection (NPP) method. Simulation results are given to validate the performance of our proposed SIC scheme.

Facing the dramatic increase of real-time applications and time-sensitive services, large-scale ultra-low delay requirements are put forward for the sixth generation (6G) wireless networks. To support massive ultra-reliable and low-latency communications (mURLLC), in this paper we propose an adaptive finite blocklength framework to reduce the over-the-air delay for short packet transmissions with multiple-access and delay-bounded demands. In particular, we first give the specified over-the-air delay model. Then, we reveal the tradeoff relationship among queuing delay, transmission delay, and the number of retransmissions along with the change of finite blocklength, as well as formulate the adaptive blocklength framework. Based on the adaptive blocklength framework and associated with grant-free (GF) access protocol, we formulate the average over-the-air delay minimization problem, where the blocklength can be adaptively changed in terms of transmission time interval (TTI) design and bandwidth allocation to achieve the optimal tradeoff and obtain its minimum over-the-air delay. We develop the cooperative multi-agent deep Q-network (M-DQN) scheme with a grouping mechanism to efficiently solve the average over-the-air delay minimization problem. Numerical results validate our proposed adaptive blocklength scheme outperforms corresponding schemes in long-term evolution (LTE) and the fifth generation (5G) new radio (NR).

The vortex electromagnetic wave carried by multiple orthogonal orbital angular momentum (OAM) modes in the same frequency band can be applied to the field of wireless communications, which greatly increases the spectrum efficiency. The uniform circular array (UCA) structure is widely used to generate or receive vortex electromagnetic wave with multiple OAM-modes. However, the maximum number of orthogonal OAM-modes based UCA is usually limited to the number of array-elements of the UCA antenna, leaving how to utilize more OAM-modes given a fixed number of array-elements as an intriguing question. In this paper, we propose a quasi-fractal UCA (QF-UCA) antenna structure in different fractal geometry layouts to break through the limits of array-element number on OAM-modes number. We develop the two-dimensional OAM modulation (TOM) and demodulation (TOD) schemes for OAM multiplexing transmission with the OAM-modes number exceeding the array-element number, which is beyond the traditional concept of multiple antennas based wireless communications. Then, we study four different types of array-element layouts for QF-UCA and evaluate the optimal layout to obtain the maximum number of OAM-modes with fixed number of array-elements. Simulation results show that our proposed scheme can obtain the maximum number of orthogonal streams, which is larger than those corresponding to other array-element layouts of QF -UCA and the traditional multiple antenna systems.

Massive ultra-reliable and low latency communications (mURLLC) has emerged to support wireless time/error-sensitive services, which has attracted significant research attention while imposing several unprecedented challenges not encountered before. By leveraging the significant improvements in space-aerial-terrestrial resources for comprehensive 3D coverage, satellite-terrestrial integrated networks have been proposed to achieve rigorous and diverse quality-of-services (QoS) constraints of mURLLC. To effectively measure data freshness in satellite communications, recently, age of information (AoI) has surfaced as a novel QoS criterion for ensuring time-critical applications. Nevertheless, because of the complicated and dynamic nature of network environments, how to efficiently model multi-dimensional statistical QoS provisioning while upper-bounding peak AoI, delay, and error-rate for diverse network segments is still largely open. To address these issues, in this paper we propose statistical QoS provisioning schemes over satellite-terrestrial integrated networks in the finite blocklength regime. In particular, first we establish a satellite-terrestrial integrated wireless network architecture model and an AoI metric model. Second, we derive a series of fundamental statistical QoS metrics including peak-AoI bounded QoS exponent, delay-bounded QoS exponent, and error-rate bounded QoS exponent. Finally, we conduct a set of simulations to validate and evaluate our proposed statistical QoS provisioning schemes over satellite-terrestrial integrated networks.

Rate splitting multiple access (RSMA) is one of the most promising techniques for ultra-reliable and low-latency communications (URLLC) with stringent requirements on delay and reliability of multiple access. To fully explore the delay performance enhancement brought by uplink RSMA to URLLC, in this paper, we evaluate the performance of two-user uplink RSMA and propose the corresponding blocklength minimization problem. We analyze the impact of finite blocklength (FBL) code on the achievable rate region and the effective throughput of uplink RSMA. On this basis, we propose the problem of minimizing the blocklength for uplink RSMA with power allocation under constrained reliability and effective throughput. Then, we present an alternating optimization method to solve this non-convex problem. Simulation results show that different from the infinite blocklength (IBL) regime, the achievable rate region of the uplink RSMA is not always larger than that of uplink non-orthogonal multiple access (NOMA) in the FBL regime. But with the help of our proposed blocklength minimization scheme, uplink RSMA can achieve the same achievable rate with a smaller blocklength compared to uplink NOMA, frequency division multiple access (FDMA), and time division multiple access (TDMA) without the need for time sharing in the FBL regime, showing the potential of uplink RSMA to achieve low delay for URLLC.

The integrated sensing and communications (ISAC) system, which integrates radar sensing and communication into the same hardware platform, has been considered as one of the new paradigms for the sixth-generation wireless networks. In order to significantly increase the communication rate and enhance the sensing accuracy with low cost and power consumption, in this paper the hybrid beamforming for millimeter wave (mmWave) ISAC systems is proposed. The hybrid analog and digital beamforming is jointly optimized to maximize the weighted sum rate (WSR) of communication users while satisfying the sensing target angle Cramér-Rao bound (CRB) constraint and transmit power budget. Based on the weighted minimum mean square error (WMMSE), Riemann conjugate gradient under exact penalty method (EPM-RCG), and successive convex approximation (SCA) methods, an efficient alternating algorithm is developed to optimize the WSR of communication users in the ISAC systems. Numerical results demonstrate the effectiveness of the developed hybrid beamforming for WSR maximization in mmWave ISAC systems.

Orbital angular momentum (OAM) carried electro-magnetic waves can be used for optical and radio-frequency communications to improve spectrum efficiency thanks to the orthogonal wavefronts of different OAM modes. However, OAM beams are vortically hollow and divergent, which seriously decreases the capacity for OAM transmissions. Moreover, unaligned-transceiver-based OAM transmissions can lead to a high bit error rate. In this paper, we propose the fractal OAM generation and detection schemes, which can alleviate the hollow divergence of OAM beams and greatly improve the capacity performance for unaligned OAM transmissions. We first briefly introduce the fractal OAM phenomenon. Then, we propose the fractal OAM beam generation and detection schemes. Simulations verify our proposed fractal OAM generation scheme. The improved capacity performance of our proposed fractal OAM compared with normal OAM transmissions are also validated via simulations.

Magnetic induction (MI) communications have gained substantial attention due to the advantages in special environments such as underground, underwater, and human body. MI wireless networks in such environments can enable important applications in future civil and public security fields. However, due to the particularity of application environments, it is challenging to guarantee the energy supply for MI devices. In this paper, we propose a backscatter based bidirectional full-duplex MI communication (BFMC), which can enhance the energy efficiency of MI communications. In BFMC, additional information is backscattered with the inherent backscatter property of MI channel and the backscatter device (BD) is free of self-interference (SI). For the BFMC based multi-user network, we formulate the joint magnetic beamforming and time allocation optimization problem, which aims to minimize the average energy consumption and guarantee the downlink signal to noise ratio (SNR), uplink signal to interference plus noise ratio (SINR), and energy harvesting requirements. The formulated problem is non-convex and transformed into magnetic beamforming and time allocation sub-problems. Based on block coordinate descent (BCD) method, the sub-problems are solved iteratively. Numerical results show that BFMC and the proposed joint beamforming and time allocation scheme can effectively reduce average energy consumption and enhance energy efficiency.

In this paper, we consider the physical layer (PHY) security problem for hybrid wiretapping wireless systems in millimeter wave transmission, where active eavesdroppers (AEs) and passive eavesdroppers (PEs) coexist to intercept the confidential messages and emit jamming signals. To achieve secure and reliable transmission, we propose an artificial noise (AN)-aided robust multi-beam array transceiver scheme. Leveraging beamforming, we aim to minimize transmit power by jointly designing the information and AN beamforming, while satisfying valid reception for legitimate users (LUs), per-antenna power constraints for transmitter, as well as all interception power constraints for eavesdroppers (Eves). In particular, the interception power formulation is taken into account for protecting the information against hybrid Eves with imperfect AE channel state information (CSI) and no PE CSI. In light of the intractability of the problem, we reformulate the considered problem by replacing non-convex constraints with tractable forms. Afterwards, a two-stage algorithm is developed to obtain the optimal solution. Additionally, we design the received beamforming weights by means of minimum variance distortionless response, such that the jamming caused by AEs can be effectively suppressed. Simulation results demonstrate the superiority of our proposed scheme in terms of energy efficiency and security.

Due to high bandwidth and small antenna size, millimeter-wave (mmWave) integrated line-of-sight (LOS) multiple-input-multiple-output (MIMO) systems have attracted much attention. Reconfigurable intelligent surfaces (RISs), which have the potential to change the characteristics of incident electromagnetic waves with low power cost, can improve the performance for the MIMO mmWave wireless communications. Uniform circular array (UCA) is an effective antenna structure with low complexity transceiver. In this paper, UCA based RIS-assisted MIMO mmWave wireless communications with transmit UCA, the RIS UCAs, and receive UCA are investigated. Since the rotation angles between the transceiver make the channel matrix noncirculant, an algorithm is developed to derive the ranges of the rotation angles based on an acceptable error and reduce the impact of rotation angles on channel matrix. Then, we propose a low-complexity precoding scheme at the transmitter, phase designs at the RIS UCAs, and a phase compensation scheme at the receiver, which can convert the channel matrix into an equivalent circulant channel matrix with a small error. Then, a fast symbol-wise maximum likelihood (ML) detection scheme is proposed to recover the signals with low computational complexity. Simulation results are presented to illustrate the theory.

Simultaneous wireless information and power transfer (SWIPT) can enhance the spectrum and power efficiencies of wireless communications networks. Line-of-sight (LOS) transmission is a typical SWIPT scenario. However, the strong channel correlation limits the spectrum and energy efficiencies of SWIPT in the LOS channel. Due to the orthogonal wavefronts, orbital angular momentum (OAM) waves can facilitate the SWIPT in LOS channels. With the assistance of the reconfigurable intelligent surface (RIS), both the energy efficiency and capacity can be further improved for the OAM-SWIPT systems. In this paper, we model the RIS-assisted OAM-SWIPT transmission and derive the optimal reflection coefficients and power splitting ratio for it. We first give the system and channel models. Then, we propose the transmission scheme. Based on the transmission scheme, we formulate the capacity and energy harvesting (EH) trade-off problem. We solve the problem by developing an alternating optimization algorithm. Simulations validate the capacity and EH enhancements brought by the RIS for OAM-SWIPT.

Regionalized communication and computation architecture aggregates idle vehicular fog computation resources in urban cities without dedicated infrastructure deployment. Vehicular fog computing migrates computation services close to the edge of the network, which can break the long response latency limitation of cloud computing. However, the high mobility and random distribution of vehicles lead to the difficulty of the fog infrastructure deployment and the offloading decision. In this paper, we investigate the latency minimization problem in the regionalized three-tier vehicular fog network, which is divided into the sub-problems of single-region offloading decision and multi-region resource allocation. In particular, we make the preliminary offloading decision for users in each roadside unit (RSU) coverage area to solve the single-region offloading decision problem. We also transfer the multi-region resource allocation problem into a group-knapsack problem to obtain the optimal resource allocation results among all the users in the base station coverage area. Simulation results validate the superiority of our proposed algorithm, which enables latency reduction in the regionalized three-tier vehicular fog network.

As the number of real-time applications with ultra-low delay requirements quickly grows, massive ultra-reliable and low-latency communication (mURLLC) has been proposed to provide a wide range of delay-sensitive services for the sixth generation (6G) wireless networks. However, it is difficult to meet the stringent delay demand of massive connectivity with existing grant-based (GB) random access and fixed frame structure in long-term evolution (LTE) and the fifth generation (5G) new radio (NR) systems. To solve this problem, in this paper we propose the new grant-free (GF) based adaptive blocklength scheme for short packet transmission to reduce the access delay. We develop the adaptive blocklength framework where the blocklength can be adaptively changed according to the real-time load, to revise the traditional non-flexible frame structure which impacts the delay performance. Taking the features of mURLLC into consideration, we analyze the GF random access procedure, packet arrival behavior, packet collision, and packet transmission error in the finite blocklength (FB) regime. On this basis, we derive the closed-form expression of successful access and transmission probability and give the GF-based status update model. Then, we propose the access delay minimization problem that jointly considers queuing delay and transmission delay to reduce the overall access delay. With the alternating optimization algorithm, we obtain the optimal blocklength of each packet, thus forming the corresponding adaptive blocklength scheme for mURLLC. Simulation results verify the correctness of theoretical results and show that our proposed adaptive blocklength scheme can significantly reduce the access delay compared with that of LTE and 5G NR systems.

The reconfigurable intelligent surface (RIS), com-posed of nearly-passive elements to induce reconfigurable re-flecting replicas of signal, is regarded as one of the promising approaches to support multi-gigabit data about the surrounding environment and recognizing objects in real-time. This makes RIS a potential technology in the autonomous vehicle application. However, the application of RIS and the mobility of vehicles substantially increase the training overhead of channel estimation, without which the RIS cannot optimally operate passive beamforming. To overcome the aforementioned problems, in this paper we aim to leverage the Fresnel zone characteristic and the position information of vehicles to design a position-aided on/off states judgment (POSJ) scheme for practical 1-blt RIS. The proposed scheme does not require instantaneous channel state information at the transmitter (CSIT) and is based on the superposition of the reflected signals from different Fresnel zone boundaries. Simulation results indicate that our developed scheme performs well with the increase in positioning accuracy, thus enabling the practical application of RIS.

The legitimate devices are easy to be attacked in wireless transmission, which is harmful for communication security. As a candidate technology for the sixth-generation (6G) network, reconfigurable intelligent surface (RIS) has attracted much attention due to its ability to enhance the security of wireless communication. Existing works for RIS-assisted security design generally assume that the phase shift matrices of RIS are the same, which limits the security performance of the RIS-assisted system. In this paper, taking into account the independent optimization of each RIS, we develop the relax-and-retract based joint transmit and reflecting beamforming to enhance the received signal of the legitimate device and mitigate anti-jamming signal of the jammer. Simulation results verify that our developed scheme can enhance the anti-jamming performance of multiple RISs (multi-RIS) assisted aerial-ground system with lower complexity compared with existing methods.

In post-disaster area, the communication infrastructure is often destroyed or seriously disrupted, along with the secondary disaster. Recently, to support communication under such circumstances, the technology of reconfigurable intelligent surface(RIS)-equipped-UAV has been studied as a feasible solution to achieve low complexity and high energy efficiency. However, due to the terrain changes caused by secondary disasters and high mobility of UAV, the channel characteristics in post-disaster areas rapidly change, which makes the traditional distributions unable to accurately describe the channel model. In this paper, based on the Fisher-Snedecor $\mathcal{F}$ fading channel, we consider a RIS-equipped-UAV based emergency wireless communication system. In view of the large number of RIS elements, we use Central Limit Theorem(CLT)-based and Gamma-based approximation to present the closed-form expressions of the new channel statistics for the probability density function (PDF) and cumulative distribution function (CDF). Based on the new statistics, we derive closed-form expressions of the achievable rate, bit error rate, and outage probability. Also, to obtain the diversity gain, we derive the asymptotic approximation of the outage probability at the high signal-to-noise ratio (SNR). Our analytical and simulation results demonstrate the striking gain of RIS under the Fisher-Snedecor $\mathcal{F}$ fading channel model.

Energy efficiency (EE) is one of the most important metrics for the beyond fifth generation (B5G) and the future sixth generation (6G) wireless networks. Reconfigurable intelligent surface (RIS) has been widely focused on EE enhancement for wireless networks because it is power-saving, programmable, and easy to be deployed. However, RIS is generally passive and thus difficult to obtain corresponding full channel state information (CSI), which severely impacts the EE enhancement of RIS-assisted wireless communications. To solve this problem, in this paper we propose the new single-active-antenna combined RIS transmitter structure, which can replace traditional multiple antennas to reduce hardware cost and power consumption. Based on the single-active-antenna combined RIS structure, we develop the Dumb RIS-Assisted Random Beamforming (Darb)-based Joint RIS-Elements and Transmit-power optimizAtion (Jeta) scheme, where dumb RIS randomly changes its phase shift according to isotropic distribution only depending on the CSI feedback from users to RIS-assisted transmitter. Then, we jointly design the number of RIS elements and optimize the transmit power to maximize the EE of RIS-assisted wireless communications. Simulation results show that compared with the traditional multi-antenna system, our developed Darb-based-Jeta scheme can significantly increase the EE without the full CSI.

Magnetic Induction (MI) based transmissions, where the typical applications are wireless power transfer (WPT) and MI communications, have gained substantial attention in recent years. Existing works related to MI based transmissions mainly focus on the case with single resonant frequency. However, from the perspective of practical applications, it is highly demanded to achieve multi-frequency access for MI based transmissions. Although multiple coils can be used to generate additional resonant frequencies, each coil is often with one resonant frequency. In this paper, we develop the Multi-frequency Resonating Compensation (MuReC) coil based Multiple-band, Multiple-Input, and Multiple-Output (MbMIMO) simultaneous wireless information and power transfer (SWIPT), which can generate multiple resonant frequencies with one coil. The access point (AP) is equipped with multiple MuReC coils as well as delivers power and data to single-coil receivers (RXs) with different resonant frequencies. We propose the magnetic beamforming scheme for MuReC-MbMIMO-SWIPT, which aims to minimize the transmit power as well as guarantee the network throughput, total delivered power, and individual requirements of each channel. The beamforming schemes for two special cases of MuReC-MbMIMO-SWIPT, that is, MuReC-MbMIMO-communications and MuReC-MbMIMO-WPT, are also given. The optimal beamforming scheme in closed-form for MuReC-MbMIMO-communications is derived. In addition, the parameter design scheme for MuReC coils, which can support an arbitrary number of resonant frequencies, is developed. Numerical results verify our theoretical analyses and show that MuReC-MbMIMO transmissions and the proposed beamforming schemes can fully use antenna resources and effectively support multi-frequency access.

With the very stringent demand for real-time transmission of wireless communication services, the requirement of sub-millisecond ultra-low end-to-end delay has been initially proposed in the sixth generation (6G) communication networks. Finite blocklength transmission is one of the potential technologies to meet such a low end-to-end delay demand for the next generation networks. However, as the finite blocklength decreases, the transmission delay decreases while the queuing delay increases, which results in the tradeoff between the transmission delay and the queuing delay. To achieve the optimal balance, in this paper we propose an adaptive blocklength transmission framework to minimize the important part of the end-to-end delay of wireless networks, where we focus on the transmission delay and queuing delay. A dynamic buffering model for variable transmission time interval (V-TTI) is introduced for the time-varying arrival of packets adaptation. Then, we propose the Flexible proximal Alternating direction method of multipliers based Blocklength Optimization (FaBo) scheme to minimize the important part of the end-to-end delay for the single user case. We also propose the Multiple deep Q-learning network based Resource Allocation (MuRa) scheme, which can efficiently balance the transmission delay and queuing delay, to minimize the important part of the end-to-end delay for the multi-user case. Numerical results show that the proposed adaptive blocklength framework can reduce the important part of the end-to-end delay compared with that of long-term evolution and the fifth generation (5G) new radio. We also show that our proposed schemes can quickly converge to the minimum end-to-end delay.

Owing to the spatial orthogonality, orbital angular momentum (OAM), which describes the helical phase fronts of electromagnetic waves, shows an extensive application prospect for increasing the spectrum efficiency and enhancing the physical layer security of wireless communications. However, efficient anti-jamming results of existing mode hopping (MH) schemes are achieved with pre-shared hopping sequences. Such MH schemes require the strict synchronization between the legitimate transmitter and receiver, thus leading to complex system design. Also, pre-shared hopping sequences greatly limit the MH application scenarios. To solve the problem of no pre-shared hopping sequences between the legitimate transmitter and receiver for anti-jamming, in this paper we propose the precoding-based mode hopping (PoM) scheme. Specifically, we design the transmitter by dividing the input information into the index information and signal information. Based on the index information, the activated OAM-modes for hopping and the phase shift for precoding are determined. OAM-mode and phase shift randomization make jammers difficult to disrupt the communication. Then, the receiver with OAM decomposition and phase shift decoding is designed to extract legitimate signals. Also, the lower bound of achievable rate for our proposed PoM scheme is derived. Numerical results show that our proposed PoM scheme is superior to the conventional index-modulation based mode division multiplexing (IM-MDM) schemes in terms of achievable rate under hostile jamming.

By offloading tasks on the drones in the transmission route over an emergency UAVs network, the rescue efficiency can be significantly enhanced. Most routing algorithms and task offloading strategies relied on the drones' future location information. However, due to the harsh conditions of the disaster scene, drones' future locations are unpredictable, which results in extremely dynamic topology and brings a great challenge for the credibility of the scheduling result. Motivated by the aforementioned problem, a credibility Computation Offloading based Task-driven routing (COT) strategy for emergency UAVs network is proposed, where a two-step Weighted time Expanded Graph (WEG) is constructed to cope with the network dynamics. Then, based on the two-step WEG, the COT is formulated to offload the tasks on the data transmission route to minimize the task processing latency and realize the computing while transmitting. Moreover, a novel credibility model is conceived to enhance the credibility of the scheduling result. Finally, the binary particle swarm optimization (BPSO) algorithm is adopted to solve this problem. The simulation results validate that the proposed COT leads to significant performance improvement in latency compared with cloud computing and local computing.

The reconfigurable intelligent surface (RIS), which converts the wireless channel into an intelligent transmission entity, has been regarded as one of the new paradigms for beyond fifth-generation (5G) wireless communication. One of the main functions of RIS is converting the none-line-of-sight (NLOS) channel to the line-of-sight (LOS) channel. However, the natural combination of RIS and millimeter wave (mmWave) communication results in a low rank channel caused by the high correlation of LOS channel and sparsely scattered mmWave channel. To tackle this problem, we investigate the uniform circular array (UCA) to leverage the potential circulant characteristic of two parallel UCAs. To be compatible with existent uniform planar array (UPA) systems, we develop the antennas/PINs selection and joint beamforming scheme which merely needs the relative position among transmitter, receiver, and RIS instead of real-time channel information to derive equivalent UCAs in traditional UPA systems. As the result, the low rank LOS channel can be converted to high rank channel due to the full rank feature of circulant matrix. Numerical results verify the superiority of our antennas/PINs selection and joint beamforming scheme.

Integrating Network Function Virtualization (NFV) into Multi-Access Edge Computing (MEC) network has been proposed. In the NFV-enabled MEC network, Service Function Chains (SFCs) are proposed to orchestrate Virtual Network Functions (VNFs) required by Network Service Requests (NSRs) for adapting to the various NSRs. NSRs are initiated as a set of requests for VNFs, and those requests should be mapped onto specific VNF instances in the executing-order, which also means embedding dependence-aware SFCs into the physical network. It is extremely challenging to optimize the latency when embedding SFCs into the MEC network because the executing-order constraint of VNFs in SFCs causes trouble for measuring the latency of designed SFC embedding schemes. For these reasons, we investigate how to minimize the latency when embedding SFCs into the heterogeneous MEC network under the condition of considering the dependency and concurrency of VNFs. Above all, a resource management model based on graph theory, element graph, is proposed to express the dependency of various resources in the NFV-enabled MEC network, which overcomes the problems caused by heterogeneity. Furthermore, relying on graph theory, we propose a universal evaluating model that can flexibly measure the latency cost of any designed service chain. Depending on the element graph and evaluating model, the Genetic Algorithm (GA) is adopted to optimize the latency of embedding SFCs, which can adaptively orchestrate the optimal VNFs for any SFC to minimize the latency.

Since the relatively low frequency band has been extensively used, the design paradigm is shifted to the high frequency band such as the promising millimeter wave (mmWave) band in the fifth generation (5G) beyond and the expected terahertz in the sixth generation (6G). Due to the severe electromagnetic degradation for high frequency band, line-of-sight (LOS) transmission for short-range high-speed scenarios has been receiving much attention. In this paper, we introduce the nonplanar electromagnetic waves (NEWs) based wireless communications, which can provide a considerable enhancement for channel capacity. After giving a brief definition of the NEWs, a specific case of the NEWs which is based on the Zadoff-Chu (ZC) phase shift matrix is proposed and analyzed. This kind of NEWs is non-hollow with its orthogonal beams sharing the same radiation pattern except with different rotations along the azimuth. Simulation results are given to validate the capacity enhancement and analyze the robustness performance of NEWs based wireless communications.

Low earth orbit (LEO) satellite network has the advantages of comprehensive coverage and has the unique benefits of short satellite-to-ground transmission distance and low construction cost, which can effectively complement the limited coverage of ground mobile communication network. Hence, LEO satellites gain extensive attention in the field of mobile communication. However, restricted by the existing mode (BP, Bent Pipe) of high transmission latency problems in the LEO space satellite computing (LSSC), it is challenging to meet the low latency requirement of time-sensitive tasks. Therefore, on-orbit collaborative computing technology for LSSC is proposed in this paper. While due to the high dynamic and non-centrality of the LEO satellite system, collaborative computing in satellite networks will face many difficulties. Aiming at the high dynamic and non-centrality of the LEO satellite network, this paper proposes a dispersed computing paradigm for the LEO satellite network, which is suitable for high dynamic no-center scenarios. In this dispersed computing paradigm, a steady-state matrix based on the time-expanded graph (TEG) model is introduced in this paper to steady-state the topology of a high dynamic LEO satellite network. According to the matrix, a transmission capacity and computing capacity based diffusion algorithm (TCGDA) is proposed to perform optimal task allocation in the LEO satellite network. The simulation results show that the proposed dispersed computing method can effectively complete the computing task with the optimized latency.

For unforeseen natural disasters, such as earthquakes, hurricanes, and floods, etc., the traditional communication infrastructure is unavailable or seriously disrupted along with persistent secondary disasters. Under such circumstances, it is highly demanded to deploy emergency wireless communication (EWC) networks to restore connectivity in accident/ incident areas. The emerging fifth-generation (5G)/beyond-5G (B5G) wireless communication system, like unmanned aerial vehicle (UAV) assisted networks and intelligent reflecting surface (IRS) based communication systems, are expected to be designed or re-farmed for supporting temporary high quality communications in post-disaster areas. However, the channel characteristics of post-disaster areas quickly change as the secondary disaster resulted topographical changes, imposing new but critical challenges for EWC networks. In this paper, we propose a novel heterogeneous$\mathcal {F}$composite fading channel model for EWC networks which accurately models and characterizes the composite fading channel with reflectors, path-loss exponent, fading, and shadowing parameters in 5G-UAV based EWC networks. Based on the model, we develop the optimal power allocation scheme with the simple closed-form expression and the numerical results based optimal joint bandwidth-power allocation scheme. We derive the corresponding capacities and compare the energy efficiency between IRS and traditional relay based 5G-UAVs. Numerical results show that the new heterogeneous Fisher-Snedecor$\mathcal {F}$composite fading channel adapted resource allocation schemes can achieve higher capacity and energy efficiency than those of traditional channel model adapted resource allocation schemes, thus providing better communications service for post-disaster areas.

Due to its low energy consumption and simplicity, near field communication (NFC) has been extensively used in various short-range transmission scenarios, for example, proximity payment and NFC entrance guard. However, the low data rate of NFC limits its application in high rate demanded scenarios, such as high-resolution fingerprint identification and streaming media transmission as well as the future promising high rate indoor communications among pads, phones, and laptops. In this paper, we model and analyze the performance of the orbital angular momentum based NFC (OAM-NFC) system, which can significantly increase the capacity of NFC. We first give the OAM system model. With coils circularly equipped at the transmitter and receiver, OAM-NFC signals can be transmitted, received, and detected. Then, we develop the OAM-NFC generation and detection schemes for NFC multiplexing transmission. We also analyze the OAM-NFC channel capacity and compare it with those of single-input-single-output (SISO) as well as multi-input-multi-output (MIMO) NFC. Simulation results validate the feasibility and capacity enhancement of our proposed OAM-NFC system. How different variables, such as the transceiver misalignment, the numbers of transceiver coils, and transceiver distance, impact the OAM-NFC capacity are also analyzed.

Orbital angular momentum (OAM), which is considered as a novel way to achieve high capacity, has been attracted much attention recently. OAM signals emitted by uniform circular array (UCA) are widely treated as going through the Bessel-form channels. However, the channel gains corresponding to the Bessel-form channels are with low signal-to-noise-ratio (SNR) on OAM-modes and it is difficult to achieve high capacity using all OAM modes. To achieve maximum capacity offered by OAM multiplexing for wireless backhaul communications, in this paper we propose the optimal UCA design, which selects the optimal OAM-modes and radius of receive UCA. We formulate the capacity maximization problem and divide it into two subproblems for obtaining the corresponding optimal UCA design for OAM multiplexing based wireless backhaul communications. In particular, the optimal radius of the receive UCA is firstly derived. Then, we propose a mode selection scheme to choose appropriate OAM-modes for data transmission to maximize the capacity. Extensive simulations obtained validate that the capacity of OAM multiplexing can be significantly increased with our developed scheme.

Due to its low energy consumption and high security, near field communication (NFC) has been extensively used in various short-range non-contact transmission scenarios such as the proximity payment and NFC entrance guard. However, the low data rate of NFC limits its application in high rate demanded scenarios, such as high-resolution fingerprint identification and streaming media transmission. In this paper, we propose the orbital angular momentum (OAM) based NFC system to significantly increase the capacity for NFC systems. With coils circularly equipped at the transmitter and receiver, OAM signals can be transmitted, received, and detected. Then, we analyze the mutual inductances between the transmit and receive coils to derive the OAM-NFC magneto-inductive channel matrix. Based on the channel matrix, we develop the OAM-NFC transmission and detection schemes for NFC multiplexing transmission. We also compare the capacity of our proposed OAM-NFC system with those of SISO NFC system and MIMO NFC system. Simulation results validate the feasibility and capacity enhancement of our proposed OAM-NFC system. How the number of the transceiver coils impacts the capacity of OAM-NFC system is also evaluated.

Orbital angular momentum (OAM), which has attracted much attention recently, is a potential technology to achieve high capacity for future wireless communications. In existing literatures, the OAM-based transmission is assumed to be used in ideal line-of-sight (LoS) scenarios. The non-line-of-sight (NLoS) scenarios, however, is more practical for wireless communications. In this paper, we focus on OAM-based transmission in sparse multipath environments. We build the OAM-based transmission model for Rician fading channels and derive the corresponding capacity of radio vortex wireless communications. Also, we analyze the key parameters of OAM-based transmission in sparse multipath environments. Conducted numerical results verify that the capacity of OAM-based transmission is superior to that of multiple-input-multiple-output (MIMO) based transmission in sparse multipath environments.

Rapid deployment is crucial for building up drone aided emergency communications to ensure the coverage and service support after the disaster. The chaos in the post-disaster area, such as the number of ground users and scattered locations, makes difficult on the decision of drone deployment. In this paper, an unsupervised machine learning method is conducted for drone deployment in drone aided emergency communications. Considering the importance of sustainable services for drones, the drone deployment problem is formulated with the aim of minimizing the total power of drones with all users' coverage while maintaining their rate requirements, with constraints on drones' coverage area, capacity and limited power. The problem is solved by two steps. A modified k-means clustering algorithm is proposed to obtain the number of drones and an optimal altitude and minimum transmit power algorithm is then derived. Simulation results show that although the number of drones obtained by the modified algorithm is more than that of the original k-means algorithm, all users are served and the minimum power of drones is guaranteed by proposed algorithms.

Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

Orbital angular momentum (OAM) based mode hopping (MH) scheme is expected to be a potential anti-jamming technology in radio vortex wireless communications. However, it only uses one OAM-mode for hopping, thus resulting in low spectrum efficiency (SE). Index modulation offers a trade-off balance between the SE and performance reliability. In this paper, we propose an MH with OAM-based index modulation scheme, where several OAM-modes are activated for hopping, to achieve high SE at a given bit error rate in radio vortex wireless communications. Based on the proposed scheme, we derive the upper bound and lower bound of achievable SEs. Furthermore, in order to take advantage of index information, we derive the optimal hopped OAM-modes to achieve the maximum SE. Numerical results show that our proposed MH with index modulation scheme can achieve high SE while satisfying a certain reliability of radio vortex wireless communications.

Orbital angular momentum (OAM) has attracted much attention for radio vortex wireless communications due to the orthogonality among different OAM-modes. To maintain the orthogonality among different OAM modes at the receiver, the strict alignment between transmit and receive antennas is highly demanded. However, it is not practical to guarantee the transceiver alignment in wireless communications. The phase turbulence, resulting from the misaligned transceivers, leads to serious intermode interference among different OAM modes and therefore fail for signals detection of multiple OAM modes at the receiver. To achieve practical OAM based wireless communications, in this paper we investigate the radio vortex wireless communications with misaligned transmit and receive antennas. We propose a joint Beamforming and Pre-detection (BePre) scheme, which uses two unitary matrices to convert the channel matrix into the equivalent circulant matrix for keeping the orthogonality among OAM-modes at the receiver. Then, the OAM signals can be detected with the mode-decomposition scheme at the misaligned receiver. Extensive simulations obtained validate and evaluate that our developed joint BePre scheme can efficiently detect the signals of multiple OAM-modes for the misaligned transceiver and can significantly increase the spectrum efficiency.

With low cost and high mobility, drones are envisioned to be an important contributor to emergency communications. In this paper, we consider a unique drone aided emergency communication deployed in hills and mountains areas, where power-constrained drones serve as relays to improve the uplink sum rates via amplify-and-forward (AF) relaying. The channel models between drones and ground users in this communications and the ground users distributions are greatly different from that in conventional relay networks, while drones have their coverage areas and capacity constraint. By taking these specific characteristics into account, we formulate the joint power and subcarrier allocation problem to maximize the uplink throughput in the drone aided emergency communications under constrains to the transmit power budget for each drone and the number of accessed users on each subcarrier. Due to the intractability of the formulated problem, it is decomposed into two subproblems: power allocation optimization and subcarrier allocation optimization. Then a joint resource allocation algorithm is proposed. The simulation results show that the proposed algorithm outperforms the others.

Due to the limited computing resources of swarm of drones, it is difficult to handle computation-intensive tasks locally, hence the cloud based computation offloading is widely adopted. However, for the business which requires low latency and high reliability, the cloud-based solution is not suitable, because of the slow response time caused by long distance data transmission. Therefore, to solve the problem mentioned above, in this paper, we introduce fog computing into swarm of drones (FCSD). Focusing on the latency and reliability sensitive business scenarios, the latency and reliability is constructed as the constraints of the optimization problem. And in order to enhance the practicality of the FCSD system, we formulate the energy consumption of FCSD as the optimization target function, to decrease the energy consumption as far as possible, under the premise of satisfying the latency and reliability requirements of the task. Furthermore, a heuristic algorithm based on genetic algorithm is designed to perform optimal task allocation in FCSD system. The simulation results validate that the proposed fog based computation offloading with the heuristic algorithm can complete the computing task effectively with the minimal energy consumption under the requirements of latency and reliability.

Orbital angular momentum (OAM), which is inherently possessed by the electromagnetic (EM) beams, bridges a new way for multiple access using multiple orthogonal OAM-modes in wireless communications. Uniform circular array (UCA), as a convenient antenna structure for supporting the transmission and reception of OAM based signals, has been paid much attention in recent years. However, the OAM beams generated by the UCAs are centrally hollow and divergent, limiting the efficient reception as well as the long-distance transmission for OAM based signals. To solve this problem, in this paper we propose the Zadoff-Chu (ZC) sequence based scrambling scheme to generate the non-hollow OAM beams while maintaining the orthogonality among different OAM-modes. Then, based on the properties of applying discrete Fourier transform (DFT) for circulant matrices and zero-free sequences, we develop the scrambling-tolerant detection scheme to efficiently obtain the transmit symbols. Performance evaluations show the non-hollow irregular OAM beams and validate that our developed ZC sequence based scrambling scheme works well for long-distance transmission in OAM based wireless communications.

In the past decade, more and more researchers have concentrated on orbital-angular-momentum (OAM) based radio vortex wireless communications, which is expected to provide orthogonality among different OAM-modes. The uniform circular array (UCA) is considered as one promising antenna structure for OAM based radio vortex wireless communications. However, most studies regarding UCA focus on the scenario where the transmit and receive UCAs are aligned with each other. In this paper, we investigate the OAM based radio vortex wireless communications with non-coaxial UCA, i.e., the UCA transceivers are parallel but non-coaxial. We study the channel model and develop the mode-decomposition scheme to decompose the OAM-modes. Then, we discuss the impact of included angles on the channel model under non-coaxial scenario. Numerical results are presented to evaluate our developed scheme and show that the spectrum efficiency of the non-coaxial UCA transceiver in some cases is larger than that of the aligned UCA transceiver based radio vortex wireless communications.

Wireless network virtualization, which decouples the physical supply process and the service provisioning process, can abstract, isolate, and share the physical infrastructure network equipment. This paper studies the resource virtualization in virtual multiple-input multiple-output single-carrier frequency-division-multiple-access uplink systems, where resources are abstracted to hide the complex details of the fading channel and the link rates are virtualized using the statistical method. Furthermore, the virtual link rates are scheduled and instantiated to different slices with customized delay-bounded quality of service (QoS) provisioning. In this scheme, a physical mobile network operator (PMNO) is in charge of the network resource at the physical layer, while virtual mobile network operators (VMNOs) are responsible for the traffic admission and the slice management at the MAC layer. Furthermore, we build up the resource virtualization problem as a cross-layer Stackelberg game, which has the interactive dual processes based on the QoS exponent: top-to-down sub-game of leaders at the MAC layer and down-to-top sub-game of follower at the physical layer. Using the newly designed functions for PMNO and VMNOs, we develop an effective dynamic algorithm with an iterative dual update to meet the optimization targets of PMNO and VMNOs. Simulation results verify the superiority and stability of delay-bounded QoS guaranteed wireless resource virtualization algorithm developed in this paper in terms of convergence, access rate, and delay-outage probability.

Orbital angular momentum (OAM) based radio vortex wireless communications have the potential to significantly increase the spectrum efficiency (SE) without increasing extra time and frequency resources. In this paper, we propose a hybrid orthogonal division multiplexing (HODM) scheme, which jointly uses the orthogonal mode division multiplexing and the conventional orthogonal frequency division multiplexing (OFD-M) to increase the SE of radio vortex wireless communications. In particular, we develop the mode and frequency dimensions based two-dimension inverse fast Fourier transform (2D-IFFT) algorithm at the transmitter to modulate transmit signal and two-dimension fast Fourier transform (2D-FFT) algorithm at the receiver to demodulate received signal, respectively. Then, we propose an optimal power allocation scheme to maximize the SE of our proposed HODM scheme. Numerical results show that the HODM scheme can significantly increase the SE in comparison with the conventional OFDM schemes.

Simultaneous wireless information and power transfer (SWIPT) has been recognized as a promising approach to improving the performance of energy constrained networks. In this paper, we investigate a SWIPT based three-step two-way decode-and-forward (DF) relay network with a non-linear energy harvester equipped at the relay. As most existing works require instantaneous channel state information (CSI) while CSI is not fully utilized when designing power splitting (PS) schemes, there exists an opportunity for enhancement by exploiting CSI for PS design. To this end, we propose a novel heterogeneous PS scheme, where the PS ratios are dynamically changed according to instantaneous channel gains. In particular, we derive the closed-form expressions of the optimal PS ratios to maximize the capacity of the investigated network and analyze the outage probability with the optimal dynamic PS ratios based on the non-linear energy harvesting (EH) model. The results provide valuable insights into the effect of various system parameters, such as transmit power of the source, source transmission rate, and source to relay distance on the performance of the investigated network. The results show that our proposed PS scheme outperforms the existing schemes.

The plane wave based wireless communications have becoming more and more matured, along with the well utilization of the traditional resources such as time and frequency. To further increase the capacity for rapidly increasing capacity demand of wireless communications, it is potential to use the twist wave, which has the orbital angular momentum (OAM). In this paper, we discuss the OAM based wireless communications in the aspect of orthogonality, degree of freedom (DoF), and capacity, where both the transmitter and the receiver use uniform circular array (UCA) antennas. In particular, we compare OAM based wireless communications with multiple-input-multiple-output (MIMO) based wireless communications in terms of DoF and capacity. Numerical results are presented to validate and evaluate that the DoF of OAM based wireless communications is greater than or equal to that of correlated MIMO based wireless communications when the transmitter and the receiver antennas are aligned well. The OAM based wireless communications can achieve larger capacity than the correlated MIMO in high signal-to-noise ratio (SNR) region under line-of-sight scenario.

Orthogonal frequency division multiplexing (OFDM), which has attracted much attention during the past few decades, can be used for not only anti-multipath but also high spectrum efficiency in wireless communications. However, as the spectrum becoming more and more crowded and the amount of traffic eventually increasing, it is very difficult to increase the spectrum efficiency in wireless communications. Recently, orbital angular momentum (OAM), which provides a novel mode dimension, offers the potential to achieve high spectrum efficiency for wireless communications. In this paper, we propose the orthogonal mode division multiplexing (OMDM) scheme for high capacity in wireless communications. In particular, we propose to transmit half-integer and integer OAM-modes signals within two narrowbands to avoid the inter-mode interference. Second, we propose the mode inverse fast Fourier transform (M-IFFT) algorithm at the transmitter to modulate signals and mode fast Fourier transform (M-FFT) algorithm at the receiver to demodulate signals, respectively. Finally, we develop the dynamic power allocation scheme to solve the problem of how to maximize the capacity of OMDM. Numerical results show that our developed OMDM scheme can achieve high capacity. Furthermore, we can jointly use our developed OMDM scheme with the conventional OFDM scheme to increase the capacity of wireless communications.

Airborne mobile wireless networks (AMWNs), which use spacecrafts and aircrafts such as satellites, airships, airplanes, unmanned aerial vehicles, and other high/medium/low-altitude platforms (HAPs/MAPs/LAPs) can efficiently support high dynamic network topologies and weakly connected communication links. Due to the dramatic dynamics of the AMWNs, it is very difficult to provide the deterministic delay-bounded quality of service (QoS) provisioning for time-sensitive real-time traffics (such as video and audio) over the AMWNs. Alternatively, the statistical delay-bounded QoS provisioning provides an efficient way for guaranteeing delay-bounded QoS requirements for real-time traffics over the AMWNs. On the other hand, because of the diversity of real-time traffics, it is highly demanded to consider the heterogeneity of delay-bounded QoS requirements for distinct real-time services under different HAPs/MAPs/LAPs in the AMWNs. In this paper, we establish the heterogeneous statistical QoS provisioning framework to support the diverse real-time services over the AMWNs. In particular, we formulate the optimization problem to maximize the aggregate effective capacity subject to heterogeneous statistical delay-bounded QoS requirements for both downlink and uplink transmissions-based AMWNs groups (AMWNGs) in the AMWNs. We solve the aggregate effective capacity maximization problems and derive the optimal heterogeneous statistical QoS-driven power allocation schemes for the AMWNs. The numerical analyses we obtained verify that our developed optimal heterogeneous statistical QoS-driven power allocation schemes can significantly increase the aggregate effective capacity for the AMWNs than the other existing schemes.

In this paper, we develop statistical quality of service (QoS)-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless networks. In particular, first, we analyze the long-term available energy constraints and formulate the EEE maximization problem. Then, we derive the closed-form solutions of optimal power control policies to the EEE maximization problem under the battery capacity dominated constraint, the average harvested energy constraint, and both the battery capacity and average harvested energy constraints, respectively. Furthermore, we derive the threshold to judge whether the optimal power control policy is limited by the battery capacity. The obtained numerical results validate our analyses and show that our developed optimal power control policies can optimize the EEE with statistical QoS provisioning over energy harvesting based wireless networks.

In this paper, we develop the resource allocation scheme with heterogeneous statistical QoS provisioning for wireless powered sensor networks (WPSNs). In particular, we build up the downlink energy transfer and heterogeneous statistical QoS provisioning uplink data transmission models, where the aggregate effective capacity (AEC) is defined as the aggregate throughput under the statistical QoS constraints, for WPSNs. Based on the models, we formulate the AEC maximization problem to jointly optimize the downlink energy assignment and uplink power control scheme. To efficiently solve this problem, we divide it into the hybrid access point determined downlink energy assignment problem and the sensor node determined uplink power control problem, solving which yields the joint downlink energy assignment and uplink power control scheme. Extensive simulations are conducted to evaluate the performance of our proposed heterogeneous QoS-driven resource allocation scheme. The obtained results show that the heterogeneous QoSdriven resource allocation scheme can significantly increase the AEC as compared with the homogeneous QoS-driven resource allocation scheme.

Recently, orbital angular momentum (OAM) based vorticose communication has attracted much attention because of its potential to significantly increase the spectrum efficiency (SE) of wireless communications. However, the multiple radio frequency (RF) chains used for multiple OAM modes lead to an unexpected cost for wireless vorticose communications. To reduce the high cost of RF chains for multiple OAM modes and the high complexity required for signal processing, we first propose the mode modulation (MM) based OAM system to allow multiple OAM modes sharing a common RF chain, which can not only reduce the hardware cost, but also boost the SE by introducing the mode as an additional dimension for data transmission. To solve the problem of how to maximize the SE of MM based OAM systems with the limited RF chains, we develop the equal-probability mode modulation (EMM) scheme, where the OAM modes are selected with equal probability and the signal is transmitted though the activated OAM modes. Moreover, we develop the Huffman coding based adaptive mode modulation (AMM) scheme, which can adaptively choose the OAM modes to further increase the SEs of OAM based vorticose communications. We also develop the OAM- water-filling power allocation policies for both EMM and AMM schemes to achieve the maximum SEs for OAM based vorticose communications. Numerical results are presented to show that the MM can offer the mode dimension for vorticose communications and the AMM scheme can achieve larger SE than the EMM scheme. Also, our developed power allocation policies can further increase the SEs for the MM based OAM communications.

In the non-time-slotted cognitive radio networks (CRNs), the synchronization between PUs and secondary users (SUs) cannot be guaranteed, resulting in two challenging problems: the reactivation-failure of PUs and the frequently unexpected hand-offs among SUs. The reactivation-failure of PUs is the incident that the SUs cannot detect the PUs' reactivation when the SUs are occupying the channels to transmit their data in non-time-slotted CRNs. The frequently unexpected handoffs among SUs are the events that the SU cannot distinguish between the PUs' reactivation and the other SUs' contention, thus causing many unexpected hand-offs among SUs, which severely degrade the achieved throughput of SUs in non-time-slotted CRNs. Employing the energy-detection based wireless full-duplex spectrum sensing schemes, the PUs' reactivation-failure problem can be efficiently solved, thus guaranteeing the required throughput of PUs. However, the key of CRNs is not only the throughput-guarantees for PUs, but also the throughput-boosts for SUs. To optimize the throughput of SUs in multichannel non-time-slotted CRNs, in this paper we develop the pilot-based full-duplex spectrum sensing (PF-SS) scheme and the pilot-based medium access control (P-MAC) protocol to not only guarantee the required throughput of PUs, but also significantly increase the throughput of SUs in multichannel non-time-slotted CRNs. Using the PF-SS scheme, the SUs can identify whether the PUs' signal or the SUs' signal, thus significantly reducing the frequently unexpected hand-offs among SUs. Then, based on the PF-SS scheme, the P-MAC protocol can significantly increase the throughput of SUs. We conduct extensive numerical analyses to show that our developed PF-SS scheme and P-MAC protocol can significantly increase the throughput of SUs while guaranteeing the required throughput for PUs in multichannel non-time-slotted CRNs.

The newly imposed heterogeneous statistical delay- bounded quality of service (QoS) provisioning, which refers to the different/variable delay-bounded QoS guarantees among different wireless links, for the fifth-generation (5G) mobile multimedia wireless networks has received much research attention recently. The heterogeneous statistical delay- bounded QoS provisioning can be classified into two categories: centralized and decentralized heterogeneous statistical delay-bounded QoS provisioning where the delay-bounded QoS requirements are centralized at the base station (BS) and distributed at different mobile user equipments (UEs), respectively. The decentralized heterogeneous statistical delay-bounded QoS provisioning is more challenging than the centralized heterogeneous statistical delay-bounded QoS provisioning. In this paper, we build up the system model for decentralized heterogeneous statistical delay-bounded QoS provisioning in terms of aggregate effective capacity and heterogeneous QoS exponents. Based on this model, we develop the optimal joint bandwidth and power allocations scheme to maximize the aggregate effective capacity of uplink transmissions supporting the heterogeneous statistical delay-bounded QoS provisionings over 5G mobile multimedia wireless networks. The BS allocates the bandwidth based on the QoS exponents of all uplinks while the mobile UEs dynamically allocate the power based on the corresponding instantaneous channel state information (CSI), the corresponding QoS exponent, and the bandwidth allocated by the BS. We conduct the extensive simulations to validate and evaluate our developed optimal joint bandwidth and power allocations schemes, showing that our proposed heterogeneous statistical delay-bounded QoS provisioning scheme can significantly increase the aggregate effective capacity as compared with the homogeneous statistical delay-bounded QoS provisioning schemes.

Mobility management and handover, in the presence of the macro-small wireless cellular networks, are of the critical challenging issues, owing to the large- scale small cells deploying and frequent mobility of user equipment (UE). Existing literatures include variously studied the inbound handover, namely the UE's movement from a macrocell to a small cell. The outbound handover (OHO), which refers to the UE's movement from a small cell to a macrocell, is also of importance. In this paper, instead of the concentric circles based handover regions, we propose a novel model for the OHO regions by utilizing the principle of the circle of Apollonius. Based on the mathematical analysis for the boundaries of OHO regions, we derive the closed-form expression to characterize the relationship between the OHO hysteresis margin and UE's mobility in terms of the velocity and the moving direction. Then, we develop the UE's movement based regulatory algorithm to adaptively adjust hysteresis for achieving the proper OHO. Simulation results show the effectiveness of the proposed OHO model. The proposed hysteresis adjusting algorithm outperforms the existing schemes in terms of the decrease for radio link failure and pingpong effect during OHO process.

By enabling two adjacent mobile devices to establish a direct link, device-to-device (D2D) communication can increase the system throughput over underlaying wireless networks, where D2D and cellular communications coexist to share the same radio resource. Traditional D2D schemes mainly focus on maximizing the system throughput without taking into account the quality-of-service (QoS) provisioning. To overcome this problem, we develop a framework to investigate the impact of delay-QoS requirement on the performance of D2D and cellular communications in underlaying wireless networks. Then, we propose the optimal power allocation schemes with statistical QoS provisioning for the following two channel modes: 1). co-channel mode based underlaying wireless networks where D2D devices and cellular devices share the same frequency-time resource; 2). orthogonal-channel mode based underlaying wireless networks where the frequency-time resource is partitioned into two parts for D2D devices and cellular devices, respectively. Applying our proposed optimal power allocations into D2D based underlaying wireless networks, we obtain the maximum network throughput subject to a given delay-QoS constraint for above-mentioned two underlaying wireless network modes, respectively. Also conducted is a set of numerical and simulation results to evaluate our proposed QoS-driven power allocation schemes under different delay-QoS requirements.

Since the Information and Communications Technologies (ICT) were designed without taking the energy-saving into account, the unexpected excessive energy consumption of the fourth-generation (4G) and pre-4G wireless networks causes serious carbon dioxide emissions. To achieve green wireless networks, the fifth-generation (5G) wireless networks are expected to significantly increase the network energy efficiency while guaranteeing the quality of service (QoS) for time-sensitive multimedia wireless traffics. In this paper, we develop the statistical delay-bounded QoS driven green power allocation schemes to maximize the effective power efficiency (EPE), which is defined as the statistical-QoS-guaranteed throughput (effective capacity) per unit power, over single-input single-output (SISO) and multipleinput multiple-output (MIMO)-channels based 5G mobile wireless networks. For the SISO-channel based 5G wireless networks, our developed QoS-driven green power allocation scheme converges to the despicking water-filling scheme (despicking channel inversion scheme) when the QoS constraint becomes very loose (stringent). We further develop and analyze the statistical-QoS-driven green power allocation scheme to maximize the EPE over the multiplexing-MIMO based 5G mobile wireless networks. The obtained numerical results show that our developed statistical QoS-driven green power allocation schemes can optimize the EPE over 5G mobile wireless networks, thus enabling the effective implementation of green 5G wireless networks.

The fifth-generation (5G) communications and wireless networks, which are expected as the next new era of wireless networks, have received much research attention in recent years. The academic and industrial researchers have developed a great deal of 5G candidate techniques to improve the performance of 5G wireless networks, where the most important two metrics are the spectrum efficiency and the quality-of-service (QoS). Jointly using full-duplex (FD) wireless communications and device- to-device (D2D) communications, forming the FD-D2D communications, can significantly increase the spectrum efficiency of 5G wireless networks in frequency/time/space-domains. On the other hand, not only increasing the spectrum efficiency, but also supporting QoS guarantees is very important for 5G wireless networks. However, supporting QoS guarantees for FD-D2D communications imposes the new challenges that we need to provide heterogeneous QoS guarantees for different types of traffics over the same link simultaneously. To overcome the aforementioned problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for FD-D2D communications over 5G wireless networks. In particular, we formulate the optimization problems to maximize the system throughput subject to heterogeneous statistical delay-bound QoS requirements. Then, we convert the resulted non- convex optimization problem into an equivalent convex optimization problem, solving which we can derive the optimal heterogeneous-QoS-driven power allocation scheme to maximize the system throughput while guaranteeing the heterogeneous statistical delay-bound QoS requirements. The extensive simulation results obtained show that our proposed heterogeneous-QoS-driven power allocation scheme can significantly increase the system throughput while guaranteeing heterogeneous statistical delay-bound QoS requirements.

Recently, both academia and industry are moving their research attention to the fifth-generation (5G) wireless networks - the next new era of wireless networks. The wireless full-duplex transmission, as one of promising candidate techniques for 5G, can significantly boost the spectrum efficiency of the wireless networks, thus providing a powerful thrust to optimize the quality-of-service (QoS) performances for the wireless networks. However, due to the heterogeneity caused by different types of simultaneous traffics over the wireless full-duplex link, supporting QoS guarantees for wireless full-duplex networks imposes the new challenges that we need to provide heterogeneous QoS guarantees for different types of traffics over the same link simultaneously. To overcome the aforementioned problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for bidirectional transmission based wireless full-duplex networks. In particular, we formulate the optimization problems to maximize the system throughput subject to heterogeneous statistical delay-bound QoS requirements. Then, we convert the resulted non-convex optimization problem into an equivalent convex optimization problem, solving which we can derive the optimal QoS-driven power allocation scheme to maximize the system throughput while guaranteeing the heterogeneous statistical delay-bound QoS requirements. The extensive simulation results obtained show that our proposed QoS-driven power allocation scheme for heterogeneous statistical delay-bound QoS requirements can achieve larger aggregate system throughput than the scheme for the homogeneous statistical delay-bound QoS requirement over 5G mobile wireless full-duplex networks.

Because of the asynchronization between primary and secondary wireless networks, the synchronization between primary users (PUs) and secondary users (SUs) can be hardly guaranteed in nontime-slotted cognitive radio networks (CRNs). In this paper, we propose a novel framework for multichannel nontime-slotted CRNs, where the PUs randomly access and leave the licensed channels. Since the PUs cannot distinguish between primary and secondary signals, the PUs may sense a busy channel when the PUs start to reactivate during the SUs' transmission, thus generating a collision or entering the backoff stage. To guarantee the high-throughput transmission of the PUs and increase the channel utilization of the SUs, in this paper, we propose the wireless full-duplex spectrum sensing (FD-SS) scheme for SUs in multichannel nontime-slotted CRNs. Using our developed FD-SS scheme, the SUs can timely sense the PUs' reactivation during the same time when the SUs are transmitting their signals. Then, based on our proposed wireless FD-SS scheme, we further develop and analyze the wireless full-duplex cognitive medium access control (FDC-MAC) protocol for multichannel nontime-slotted CRNs. We conduct extensive numerical analyses, showing that our developed FD-SS scheme and FDC-MAC protocol can efficiently guarantee the high-throughput transmission of the PUs and increase the channel utilization of the SUs without requiring the synchronization between the PUs and the SUs over the multichannel nontime-slotted CRNs.

To guarantee the real-time transmission for time-sensitive traffic, we need to take delay-bound quality-of-service (QoS) into account when designing the wireless cellular networks. The traditional powerful QoS guarantee technique, called the homogeneous statistical QoS provisioning, assumes that the QoS provisioning of each link can be individually processed. In order to further significantly increase the global system throughput, the available resources of all links need to be jointly controlled, which is typically suitable for downlink transmissions over wireless cellular networks. Under this setup, we need to consider the diverse delay-bound QoS provisionings for different links at the same time, which represents the new heterogeneous statistical QoS provisioning framework and imposes many new challenges not encountered before in wireless networks. To overcome these problems, in this paper we propose the heterogeneous statistical QoS provisioning framework for high-speed downlink transmissions in wireless cellular networks. In particular, we formulate the optimization problem to maximize the downlink throughput subject to heterogeneous statistical delay-bound QoS requirements. For solving this optimization problem, we develop the heterogeneous-QoS-driven power allocation scheme to derive the closed-form solutions which can maximize the global system throughput while guaranteeing the heterogeneous delay-bound QoS for the entire wireless cellular networks. We show that the heterogeneous-QoS-driven power allocation provides more generic framework for downlink transmissions. The extensive simulation results obtained show that our proposed heterogeneous-QoS-driven power allocation scheme can achieve the global optimization of wireless resource efficiency, thus significantly increasing the global system throughput as compared with the homogeneous statistical delay-bound QoS provisioning schemes.

By enabling two mobile devices, which are far away from the base station (BS) and very close to each other, to establish a direct link, device-to-device (D2D) communications can significantly increase the system throughput for wireless cellular networks. Traditional D2D schemes are mainly based on the half-duplex transmission mode. However, the short distance between the two D2D mobile devices can not only increase the received signal-to-noise ratio (SNR) but also increase the signal-to-self-interference-plus-noise ratio (SSINR), which motivates us to apply the wireless full-duplex transmission based D2D communications into wireless cellular networks. To further increase the ergodic capacity of the wireless cellular networks, in the paper we propose the full-duplex D2D communications framework for wireless cellular networks. In particular, we formulate the ergodic capacity optimization problem for full-duplex D2D communications based wireless cellular networks. To solve this optimization problem, we develop the optimal full-duplex power allocation schemes to maximize the ergodic capacity of the wireless cellular networks. Also conducted is a set of numerical and simulation results to evaluate the ergodic capacity gain of our proposed full-duplex power allocation schemes as compared with the traditional half-duplex transmission based D2D communications.

We consider the full-duplex transmission over bidirectional channels with imperfect self-interference cancelation in wireless networks. In particular, together using propagation-domain interference suppression, analog-domain interference cancellation, and digital-domain interference cancellation, we develop the optimal dynamic power allocation schemes for the wireless full-duplex sum-rate optimization problem which aims at maximizing the sum-rate of wireless full-duplex bidirectional transmissions. In the high signal-to-interference-plus-noise ratio (SINR) region, the full-duplex sum-rate maximization problem is a convex optimization problem. For interference-dominated wireless full-duplex transmission in the high SINR region, we derive the closed-form expression for the optimal dynamic power allocation scheme. For non-interference-dominated wireless full-duplex transmission in the high SINR region, we obtain the optimal dynamic power allocation scheme by numerically solving the corresponding Karush-Kuhn-Tucker (KKT) conditions. While the full-duplex sum-rate maximization problem is usually not a convex optimization problem, by developing the tightest lower-bound function and using the logarithmic change of variables technique, we convert the full-duplex sum-rate maximization problem to a convex optimization problem. Then, using our proposed iteration algorithm, we can numerically derive the optimal dynamic power allocation scheme for the more generic scenario. Also presented are the numerical results which validate our developed optimal dynamic power allocation schemes.

Spectrum and power efficiencies are both crucial to design efficient wireless networks. In past two decades, spectrum and power efficiencies of wireless networks are optimized separately. However, to increase the spectrum efficiency while reducing the energy consumption, it is necessary to jointly optimize spectrum and power efficiencies of wireless networks. Supporting the statistical quality of service (QoS) provisionings for real-time traffic is crucial, but imposes new challenges, in the next generation wireless networks. In this paper, we propose an efficient framework to jointly optimize effective spectrum efficiency (ESE) and effective power efficiency (EPE) under different statistical QoS guarantees constraints to support the real-time traffic over wireless networks. In particular, we derive the relationship between ESE and EPE under statistical QoS provisioning constraint. Based on this relationship, we obtain the mutually beneficial (MB) region and the contention-based (CB) region. In the MB region, we propose a novel strategy to achieve the joint effective spectrum and power efficiencies optimization using the average transmit power control. In the CB region, we propose the wireless-relay-based strategy to jointly optimize the effective capacity and power efficiency. In both MB and CB regions, we develop the dynamic transmit-power control strategy and the MIMO-based strategy to jointly maximize the effective spectrum and power efficiencies. Also conducted is a set of numerical evaluations showing that our proposed strategies can achieve superior joint spectrum and power efficiencies optimization for the diverse statistical QoS provisionings.

To enable multiple mobile users to transmit their signals simultaneously over the same sub-channels, the virtual multiple-input multiple-output (V-MIMO) techniques can exploit the multiple-input multiple-output (MIMO) spectrum efficiency gain. Traditional V-MIMO transmission schemes mainly focus on maximizing the throughput of grouped mobile users without taking into account the quality-of-service (QoS) provisionings. In this paper, we propose the optimal power allocation schemes with statistical QoS provisionings to maximize the effective capacity of non-collaborative/collaborative V-MIMO wireless networks, respectively. For non-collaborative V-MIMO wireless networks, the mobile users in one V-MIMO group transmit signals independently over the same sub-channels. In the view point of existing mobile users, they solely occupy the sub-channels. Thus, the existing mobile users employ the QoS-driven single-user power allocation scheme to maximize their effective capacity. By converting the non-collaborative V-MIMO transmission optimization problem into a strictly convex optimization problem, we derive the QoS-driven power allocation scheme for the newly added mobile users to maximize their effective capacity. For collaborative V-MIMO wireless networks where the mobile users in one group can collaboratively transmit their signals, we derive the QoS-driven collaborative power allocation schemes for both the existing and the newly added mobile users. Also conducted is a set of simulation evaluations, showing that our proposed power allocation schemes for V-MIMO wireless networks outperform the other existing schemes.

Spectrum and power efficiencies are both crucial to design efficient wireless networks. In past two decades, spectrum and power efficiencies of wireless networks are optimized separately. However, to increase the spectrum efficiency and reduce the energy consumption, it is necessary to jointly optimize spectrum and power efficiencies of wireless networks. In this paper, we propose an efficient framework to model the statistical delay quality of service (QoS) guarantees, in terms of QoS exponent, effective spectrum efficiency (ESE), and effective power efficiency (EPE), for joint spectrum and power efficiencies optimization to transmit real-time traffics over wireless networks. In particular, we derive the relationship between ESE and EPE under statistical QoS provisioning constraint. Based on the relationship, we obtain the mutually beneficial region and contention-based region. We also analyze the global maximum EPE and optimal average transmit power under various statistical QoS guarantees. Then, we develop a novel strategy to achieve the joint spectrum and power efficiencies optimization. Also conducted is a set of numerical evaluations showing that our proposed strategy can achieve superior joint spectrum and power efficiencies optimization for various statistical QoS provisionings.

We propose the quality-of-service (QoS) driven power allocation scheme for wireless links under average and peak power constraints. By integrating information theory with the principle of effective capacity, our proposed scheme aims at maximizing the system throughput subject to a given delay-QoS constraint. Over the block fading channel, we derive the optimal power allocation scheme to maximize the effective capacity. The analyses and numerical results show that our optimal power allocation scheme converges to the despicking water-filling scheme when the QoS constraint becomes very loose. On the other hand, our optimal power allocation scheme reduces to the dispicking channel inversion scheme when the QoS constraint gets very stringent. Two non-optimal power allocation schemes are described for comparison purposes. We also propose and evaluate the independent optimization for the multicarrier system.

We propose the quality-of-service (QoS) driven power allocation scheme for full-duplex wireless links. By integrating information theory with the principle of effective capacity, we build two models - local transmit power related self-interference (LTPRS) model and local transmit power unrelated self-interference (LTPUS) model to analyze the full-duplex transmission, respectively. In the LTPRS model, self-interference directly corresponds to the level of local transmit power. In the LTPUS model, self-interference does not directly relate to the level of local transmit power. For both of these two models, we derive the optimal power allocation schemes, which aim at maximizing the system throughput subject to a given delay QoS constraint, over bidirectional wireless links with full-duplex transmission. The analyses and numerical results verify that our proposed power allocation scheme can efficiently support diverse QoS requirement over full-duplex wireless links. For LTPRS model, the optimal power allocation scheme converges to a constant power scheme when the QoS constraint gets very loose and the optimal power allocation scheme reduces to the channel inversion scheme when the QoS constraint becomes very stringent. For LTPUS model, the optimal power allocation scheme converges to the the water-filling scheme when the QoS constraint gets very loose and the optimal power allocation scheme reduces to the channel inversion scheme when the QoS constraint becomes very stringent.

Integrating information theory with the principle of effective capacity, we propose the optimal resource allocation schemes for wireless full duplex and half duplex relay networks, respectively, to support the statistical quality-of-service (QoS) provisioning. In particular, we introduce a new control parameter, termed cancellation coefficient, to characterize the performance of full duplex relay transmission mode. For both amplitude-and-forward (AF) and decode-and-forward (DF) relay networks, we develop the dynamic hybrid resource allocation policies under full duplex and half duplex transmission modes to maximize the network throughput for the given delay QoS constraint measured by the QoS exponent. The numerical results obtained verify that our proposed resource allocation schemes can support diverse QoS requirements over wireless relay networks under full duplex and half duplex transmission modes. Our analysis indicates that the optimal effective capacity of perfect full duplex transmission mode is not the just twice as much as the optimal effective capacity of half duplex transmission mode. Our numerical analyses also show that the hybrid transmission mode can achieve better performance than just using full duplex or half duplex transmission mode alone.

Recently, a new concept "Green Radio" has been focused, which means to reduce the existing energy consumption of information and communication technologies (ICTs). Cellular networks account for a rather large share of energy use, lowering their energy consumption appears beneficial, especially BS, which costs the most energy in the cell. Macro-femto network, as a new two-tier cellular network, has obvious advantages in lowering cellular energy consumption. In this paper, we firstly introduce a spatial two-tier model and two new metrics for analyzing the cellular network. Secondly, we analyze the downlink energy consumption of BSs and femtocell access points (FAPs). Thirdly, we provide a novel energy efficient spectrum allocation strategy in marco-femto cellular networks for minimal downlink energy consumption. Finally, simulation results show that this new spectrum allocation strategy can help the cellular network to be energy efficient.