The rapid development of communication technology has enabled intelligent vehicles and edge networks, making vehicular crowdsensing an emerging paradigm for data collection and dissemination. Vehicles can be mandated to collaboratively perform large-scale data sensing. However, due to high mobility, availability, and resource constraints, it is difficult to design effective mechanisms to encourage suitable vehicles to complete sensing tasks. To overcome these challenges, this work proposed a two-stage bus-aided vehicular crowdsensing framework, which involves collaboration between the bus system and normal vehicles in the vehicular network. In the first stage, the buses perform sensing, and a modified stable matching is applied to effectively allocate tasks to vehicles while optimizing sensing cost. In the second stage, normal vehicles are selected through contract to perform sensing in areas inaccessible to the buses. The vehicles are offered optimal contracts based on the sensing quality index (SQI). The SQI of a vehicle is obtained by considering some vital metrics, including promptness, reputation, willingness, and commitment. To achieve fairness in contract design, information asymmetry is also considered. Simulations were conducted to validate the effectiveness of the proposed schemes against baseline methods. The proposed schemes achieve remarkable results in various experiments.

Integrated satellite-terrestrial networks (ISTNs) provide crucial support for ubiquitous connectivity, whereas the inherent openness poses significant security threats. Covert communication that achieves intrinsic protection by concealing transmission behavior has become an ideal solution. This work studies a downlink covert satellite communication scenario under signal overlay. Existing research mostly carries out optimizations in the power domain. However, since waveforms serve as the physical carriers of information, their design critically impacts the covert communication performance via different adjustability to channel state information (CSI). Specifically, blindly enhancing demodulation reliability does not necessarily lead to better covert capacity and may even be counterproductive. OFDM is vulnerable to carrier frequency offset, while OTFS lacks the flexibility to accommodate CSI, resulting in difficulties applying existing candidate waveforms. In light of this, we propose a nimble waveform named orthogonal fractional dual index multiplexing (OFDIM) tolerant of fractional delay and Doppler. It enables common modulation schemes through integer indices and creates a fractional domain resilient to CSI. The closed-forms of the maximum covert transmit power, reliability, and spectral efficiency are derived. Fractional indices are optimized to improve the performance based on CSI. Simulations validate the theoretical analysis and demonstrate the benefits of the fractional domain in covert communication. Additionally, OFDIM incorporates a complementary security mechanism where the indices can act as dynamic ciphers and guarantee certain security even if the communication behavior is exposed.

Continuous variable-quantum key distribution (CV-QKD) protocols attract increasing attentions in recent years because they enjoy high secret key rate (SKR) and good compatibility with existing optical communication infrastructure. Classical coherent receivers are widely employed in coherent states based CV-QKD protocols, whose detection performance is bounded by the standard quantum limit (SQL). Recently, quantum receivers based on displacement operators are experimentally demonstrated with detection performance outperforming the SQL in various practical conditions. However, potential applications of quantum receivers in CV-QKD protocols under turbulent channels are still not well explored, while practical CV-QKD protocols must survive from the atmospheric turbulence in satellite-to-ground optical communication links. In this paper, we consider the possibility of using a quantum receiver called multi-stage CD-Kennedy receiver to enhance the SKR performance of a quadrature phase shift keying (QPSK) modulated CV-QKD protocol in turbulent channels. We first derive the error probability of the multi-stage CD-Kennedy receiver for detecting QPSK signals in turbulent channels and further propose three types of multi-stage CD-Kennedy receiver with different displacement choices, i.e., the Type-I, Type-II, and Type-III receivers. Then we derive the SKR of a QPSK modulated CV-QKD protocol using the multi-stage CD-Kennedy receiver and post-selection strategy in turbulent channels. Numerical results show that the multi-stage CD-Kennedy receiver can outperform the classical coherent receiver in turbulent channels in terms of both error probability and SKR performance and the Type-II receiver can tolerate worse channel conditions compared with Type-I and Type-III receivers in terms of error probability performance.

In order to realize the internet of everything in sixth generation (6G), the emerging 6G applications have brought increasing demands for the high-speed communication and high-accuracy positioning concurrently. Relying on the potentials of high transmission power, large quantity and excellent geometric topology, low earth orbit (LEO) satellites have become strong candidates for providing integrated communication and positioning (ICAP) services. However, the resource competition between services and high dynamics of space-ground environment make it intractable to strike a balance between communication and positioning performance, which is one of the key design issue in LEO-ICAP networks. Against this backdrop, we consider an ICAP system with multiple LEO satellites, and adopt communication rate and squared position error bound (SPEB) as performance evaluation metrics. Based on that, we further formulate a weighted utility maximization problem, where the balance between communication and positioning performance can be achieved by jointly optimizing the subcarrier and power allocation, while simultaneously satisfying users’ quality of service (QoS) requirements. To solve this mixed-integer nonlinear programming problem, we propose a compressed sensing-based resource allocation algorithm, where the sparsity property of optimization variables is exploited to reformulate the problem into a continuous form, and the sequential convex programming method is then applied to solve the problem iteratively until convergence. Extensive simulations verify the superiority of our proposed algorithm compared to various benchmark schemes, where the proposed algorithm achieves a sum-rate improvement of at least 22% and a SPEB reduction of at least 28%, showing its effectiveness in realizing the balanced optimization of communication and positioning performance.

Integrated sensing and communication (ISAC) in dense multi-cell networks suffers from severe inter-cell interference, degrading both sensing and communication performance. Conventional strategies focus on interference suppression. To address this issue, this paper proposed an interference empowering precoding (IEP) framework that exploits informative inter-cell interference as a distributed sensing resource while protecting communication quality-of-service (QoS) in ISAC. IEP operates on two timescales. In the slow timescale, a lightweight Transformer-based module infers the symbol-level knowledge coefficients of interfering waveforms while per-resource-element adaptation between constructive interference exploitation for sensing and robust suppression for communication is conducted in the fast timescale. A joint optimization problem was formulated to maximize the weighted sum of the communication sum rate and the log-determinant of Fisher Information Matrix with QoS and constructive interference alignment constraints under channel state information uncertainty. The non-convex problem is reformulated as a difference-of-convex program and solved using an alternating Weighted Minimum Mean Square Error and Convex-Concave Procedure algorithm with guaranteed convergence. Simulation results demonstrate that the proposed framework could sub-stantially enlarge the achievable communication-sensing trade-off region. Notably, it achieves approximately 24% reduction in the Position Error Bound and a 21.5% extension in detection range compared to conventional interference suppression schemes, without compromising communication throughput.

The sixth generation mobile communication system promises to achieve ubiquitous coverage. Although integrated satellite-terrestrial networks (ISTNs) offer a direct resolvent, high mobility and openness pose challenges to reliability. Orthogonal time frequency space (OTFS) mitigates the limitation of orthogonal frequency division multiplexing (OFDM) in combating time-selective fading with the cost of additional complexity. Nonetheless, the diverse scenarios lead to differentiation in requirements from the perspective of waveform design. Therefore, a single waveform is inadequate to be uniformly adopted in ISTNs, further aggravated by the difficulty in upgrading satellite hardware. Consequently, there is an urgent need for a soft approach capable of switching between traditional waveforms while maintaining reliable communication under adverse conditions. A fractional dual index transform is introduced, enabling seamless switching and fusion across time, frequency, delay, and Doppler domains through two tunable indices. Based on this, fractional dual index division multiplexing (FDIDM) is designed along with input-output relationships and feasible decoders. FDIDM generalizes typical waveforms like OFDM and OTFS as special cases with the same complexity and improves performance through indices optimization at the cost of additional computational overhead. Theoretical analysis is conducted to obtain the closed-form symbol error rate (SER) and performance boundaries of FDIDM. Simulation results validate the deduction and demonstrate the advantages of cross-domain modulation over existing waveforms.

Channel modeling for the satellite-to-underwater laser communication (StULC) link remains challenging due to long distances and the diversity of the channel constituents. The StULC channel is typically segmented into three isolated channels: the atmospheric channel, the air-water interface channel, and the underwater channel. Previous studies involving StULC channel modeling either focused on separated channels or neglected the combined effects of particles and turbulence on laser propagation. In this paper, we established a comprehensive StULC channel model by an analytical-Monte Carlo hybrid approach, taking into account the effects of particles, bubbles, and turbulence. We first obtained the intensity distribution of the transmitted laser beam after passing through the turbulent atmosphere based on the extended Huygens-Fresnel principle. Then we derived a closed-form probability density function of angular fluctuations of the beam refracted by the random sea surface. These analytical results are then mapped to initialize photon states for the Monte Carlo simulation of the underwater channel. Based on the proposed StULC channel model, we analyzed the bit error rate and the outage probability under different environmental conditions and system parameters. Numerical results demonstrated that the influence of underwater particle concentration on the communication performance is more pronounced than that of both the atmospheric turbulence and the underwater turbulence. In addition, enlarging the receiver aperture provides a more effective performance improvement than increasing the receiver field-of-view angle.

Modeling of multiple-scattering channels in atmospheric turbulence is essential for the performance analysis of long-distance non-line-of-sight (NLOS) ultraviolet (UV) communications. Existing works on the turbulent channel modeling for NLOS UV communications either focused on single-scattering cases or estimate the turbulent fluctuation effect in an unreliable way based on Monte-Carlo simulation (MCS) approach. In this paper, we establish a comprehensive turbulent multiple-scattering channel model by using a more efficient Monte-Carlo integration (MCI) approach for NLOS UV communications, where both the scattering, absorption, and turbulence effects are considered. Compared with the MCS approach, the MCI approach is more interpretable for estimating the turbulent fluctuation. To achieve this, we first introduce the scattering, absorption, and turbulence effects for NLOS UV communications in turbulent channels. Then we propose the estimation methods based on MCI approach for estimating both the turbulent fluctuation and the distribution of turbulent fading coefficient. Numerical results demonstrate that the turbulence-induced scattering effect can always be ignored for typical UV communication scenarios. Besides, the turbulent fluctuation will increase as either the communication distance increases or the zenith angle decreases, which is compatible with existing experimental results and also with our experimental results. Moreover, we demonstrate numerically that the distribution of the turbulent fading coefficient for UV multiple-scattering channels under all turbulent conditions can be approximated as log-normal distribution; and we also demonstrate both numerically and experimentally that the turbulent fading can be approximated as a Gaussian distribution under weak turbulence.

Low Earth orbit satellite (LEOS) communications are vital for advancing global connectivity. However, these systems are vulnerable to threats from malicious jamming and the conflict between safety and transmission rates. Covert communication is a promising technology that can reduce the detectability of wireless transmissions at high rates. Therefore, in this paper, we introduced atwo-layer holographicapproach in the LEOS covert communication system based on holographic jamming-parasitic modulation (HJPM) and holographic multiple-input multiple-output (HMIMO). In HJPM, the transmitter superim-poses information signals onto jamming signals, allowing the legitimate receiver to reconstruct useful information from the jamming, effectively hiding the information within the jamming. The HMIMO surface utilizes holographic beamforming to achieve dynamic high-directional gains, thereby enhancing communication for legitimate users while meeting covertness constraints. Additionally, we proposed an optimization algorithm, i.e., Tri-CoHo, that combines elastic parasitic modulation depth with hybrid beamforming to maximize covert transmission rate in LEOS communications. Results showed that our scheme achieves a higher communication performance while maintaining a high level of undetectability compared to benchmarks.

As the application fields of permissioned blockchains broaden and the integration of related industries accelerates, there is a rising demand for permissioned blockchains to support scalable networks. This paper proposes a Partitioned, Parallel and Practicable permissioned blockchain, called as P3-Chain, which builds upon a multi-shard two-tier architecture. Its key design insight is to extend scalability in terms of consensus algorithm protocol, architecture, and scheduling. In particular, P3-Chain employs a dual-consensus algorithm with decoupled intra- and inter-shard operations, allowing them to run in parallel and asynchronously under practical scenarios. To resolve the conflicting transaction problem brought by this decoupled dual-consensus algorithm, P3-Chain incorporates a state-access locking mechanism. P3-Chain is implemented in Golang across multiple OSs, and it is evaluated on Hyperledger Caliper testbed, ensuring standardized and fair benchmarking. Through extensive experiments, the results indicate that P3-Chain can achieve TPS$3.3\times $that of FISCO,$3.3\times $that of partitioned FISCO,$2.7\times $that of Fabric,$2.3\times $that of AHL+,$2.2\times $that of SharPer and$7.4\times $that of Ethereum when system contains 32 nodes. Meanwhile, within the same experimental settings, P3-chain is scalable to 1024 nodes successfully, while Fabric and FISCO run with 64 nodes only. Furthermore, P3-Chain only sacrifice less than a 10% performance when the system scale expands$256\times $from 4 to 1024.

Permissioned blockchain systems provide a mutual-trust platform for data sharing and collaboration among organizations. However, performance bottlenecks limit their adoption in industrial applications requiring high transaction throughput and low latency. Recent advancements have focused on leveraging parallelism to improve performance, but transaction contention remains a significant challenge. The Optimistic Concurrency Control (OCC) mechanism, once widely used to manage transaction contention in permissioned blockchains, is valued for its simplicity and minimal design constraints. However, its reliance on the strategy of aborting conflicting transactions results in resource wastage and suboptimal performance, rendering it less favorable in recent research. This paper presents the Overpass Ledger (OPL), a high-performance permissioned blockchain system that utilizes an overpass-inspired workflow. To address transaction contention in such a highly parallelized workflow, the OCC mechanism is revisited and Re-Execution (ReX) is proposed, an enhanced OCC variant that efficiently re-executes conflicting transactions to eliminate transaction abortion and maximize resource utilization. By integrating ReX, OPL fully harnesses the advantages of parallel stage processing and concurrent transaction execution. Experimental results demonstrate that OPL achieves throughput improvements of$77\times$,$19\times$, and$4\times$compared to Hyperledger Fabric, BIDL, and FISCO BCOS, respectively, while maintaining consistently low latency.

Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to mitigate the severe Doppler shift in low Earth orbit (LEO) satellite communications. However, the frequency-dependent Doppler shift induced by the high mobility of LEO satellites leads to the Doppler squint effect (DSE). This effect compromises the channel sparsity in the delay-Doppler (DD) domain, rendering existing channel estimation methods ineffective. To overcome this challenge, this paper proposes a DSE-resilient transmission scheme for cyclic prefix OTFS (CP-OTFS)-based LEO satellite systems. Specifically, we analyze the input-output relationship of the CPOTFS- based LEO satellite communication system and derive a DSE-aware representation of the satellite-terrestrial channel in the DD domain. To efficiently capture DSE-aware channel characteristics, we propose a novel OTFS frame structure that allows the energy distribution of the received signal to serve as prior information for channel estimation. Meanwhile, this frame structure strategically allocates pilot symbols to achieve uniform energy distribution and reduce the peak-to-average power ratio (PAPR), while imposing a time-domain waveform continuity constraint to suppress out-of-band emission (OOBE) caused by rectangular pulses. Based on the frame structure, we propose a prior-aided iterative channel reconstruction (PAICR) algorithm to mitigate the severe power leakage induced by DSE. The proposed algorithm iteratively extracts and removes dominant channel components using Doppler-domain received signal energy observations, with a convergence criterion ensuring reliable termination. Furthermore, a Cramer-Rao lower bound is derived to provide a theoretical benchmark for evaluating the algorithm's performance.

Federated learning (FL) is a promising paradigm for collaborative intelligence in low-altitude economy, enabling unmanned aerial vehicle (UAV) swarms to perform deep learning tasks (e.g., logistics, emergency rescue) while preserving data sovereignty. However, limited communication channels and heterogeneous computation capabilities among UAVs cause significant FL aggregation delays. To reduce convergence time, we propose an asynchronous FL (AFL) framework for UAV swarms, integrating over-the-air computation (AirComp) to boost communication efficiency via simultaneous transmission. To address signal distortion in AirComp, we design an objective function solved via an aggregation scheduling algorithm, which transforms the nonconvex problem into two convex subproblems tackled via alternating optimization, to derive optimal aggregation strategies and beamforming vectors. Besides, to mitigate model staleness in AFL, which causes gradient divergence and slow convergence, we propose a self-adaptive aggregation scheme with staleness awareness, enabling UAVs to adjust local models autonomously without information from other UAVs. Simulation results show that our scheme not only reduces staleness impact but also leverages stale parameters, helping AFL outperform synchronous FL in convergence speed and accuracy. Overall, our study presents an effective AFL framework, a fast aggregation scheduling algorithm, and a self-adaptive aggregation scheme for UAV swarms, accelerating global model convergence while reducing energy expenditure.

Relying on the powerful communication capabilities and rapidly changing geometric configurations, Low Earth Orbit (LEO) satellites have become strong candidates for offering the integrated communication and navigation (ICAN) services in future sixth generation (6G) networks. Considering the distinct performance and resource requirements, how to strike a balance between communication and navigation is one of the key design issues in LEO-ICAN systems. Against this backdrop, we take the transmission rate and geometric dilution of precision (GDOP) as evaluation metrics of communication and navigation performance, respectively, and formulate a weighted rate and GDOP maximization problem by jointly optimizing the beamforming design and satellite selection. To deal with the optimization problem, we split the original problem into the beamforming design and satellite selection subproblems, and propose a two-layer resource allocation algorithm to solve these subproblems iteratively until convergence. Specifically, in the inner layer, the beamforming design is modeled as a difference-of-convex (DC) problem, and the DC programming method is applied to maximize the communication rate. In the outer layer, the satellite selection is modeled as an overlapping coalition formation (OCF) game, and the OCF-based satellite selection algorithm is proposed to simultaneously reconcile the navigation GDOP. Extensive simulation results demonstrate the effectiveness of our proposed algorithms and reveal the trade-off between communication and navigation performance.

This paper presents a novel approach for wireless federated learning (WFL) that, for the first time, enables the aggregation of local models with mild to moderate errors under practical communication settings, which has to date been prevented by floating-point standards, e.g., IEEE binary32, and encryption. Specifically, we propose a new conversion from floating-point local models to fixed-point models on a layer basis, eliminating the need to transmit error-intolerant sign and exponent bits of floating-point numbers while accommodating variations in model layer widths and magnitudes. We also quantify how bit errors in the ciphertext affect the plaintext when symmetric encryption is employed for local model uploading, e.g., under Rayleigh, Rician, and Nakagami-m fading channels. Notably, these bit errors are leveraged to enhance the privacy of local models. We interpret the local model transmission process as a (λ, ϵ)-Rényi Differential Privacy (DP) mechanism, where bit errors induced by noisy channels, controlled via transmit powers, and exacerbated by decryption act as DP perturbations. Experiments show the superiority of the new WFL to the status quo with higher training accuracy and lower communication overhead.

Unmanned Aerial Vehicle (UAV) swarms are key enablers for cooperative intelligent tasks in Internet of Everything (IoE) environments, like environmental monitoring and disaster response. Meanwhile, Digital Twin (DT) technology is rapidly advancing in academia and industry due to its potential to design, test, and optimize evolving IoE networks. While DT-empowered UAV swarms provide a compelling solution for complex tasks, challenges remain in jointly designing swarm control and service provision strategies, especially in dynamic IoE environments. To harness the potential of UAV swarm intelligence, we propose a DT-empowered cluster formation technique employing the Over-the-Air Computation (AirComp) transmission scheme for energy-efficient IoE data aggregation, where UAV and IoE device clusters are dynamically constructed and adjusted. Furthermore, we consider the limited power resources by maximizing energy efficiency, which leads to a joint cluster formation, multi-UAV trajectory design, and power allocation problem under the constraint of AirComp signal distortion. To efficiently solve this mixed-integer nonlinear fractional programming problem, we develop a low-complexity iterative algorithm based on the block coordinate descent method. Simulation results show that the proposed DT-empowered AirComp cluster formation technique achieves up to 42% higher energy efficiency and 34% greater throughput than the baselines, validating its potential for efficient data aggregation tasks.

Orthogonal time frequency space (OTFS) modulation has become an indispensable key technology for high mobility communication scenarios. By accurately estimating the channel sparsity characteristics in the delay-Doppler (DD) domain, it can effectively compensate for doubly selective fading, thereby ensuring system robustness to rapidly time-varying channels. However, existing pilot designs for DD domain channel estimation cannot simultaneously meet the requirements of high estimation accuracy and low pilot overhead. To address this issue, we propose a low-power buffer band with superimposed pilot (BBSP) design scheme to jointly optimize the channel estimation performance and spectral efficiency. Low-power buffer symbols are employed to reduce the interference between pilot and data, and the adaptive Bayesian optimization algorithm is implemented for power allocation to maximize the channel capacity. Simulation results show that the BBSP scheme achieves high channel capacity with acceptable loss of estimation accuracy, demonstrating significant performance advantages over existing schemes.

Large language models (LLMs) have shown remarkable abilities by significantly scaling up model size, but this has also greatly increased their computing overhead. Traditional solutions to reduce the overhead are offloading inference tasks to cloud servers. With the evolution of computing power networks, computing resources are increasingly distributed at the network edge, allowing inference tasks to be handled locally. This edge inference can reduce traffic stress on backbone networks from cloud offloading and improve the utilization of heterogeneous edge computing power. However, the challenge is how to balance the gigantic computing workloads of LLMs with the limited computing power at the edge. To overcome this, a mixture-of-depths (MoD) Transformer based tri-parallel distributed approach was introduced. By dynamically allocating computing power to specific positions in the Transformer and parallel computing, this approach maximizes the capabilities of heterogeneous edge nodes to achieve resource-efficient inference. Simulation results showed that the proposal performs best in various edge environments, reducing inference delay by up to 24.3 % and energy consumption by up to 37.5%, respectively.

The application of continuous aperture array (CAPA) for mono-static sensing is studied in this paper. Specifically, the transmit CAPA emits a probing signal to a sensing target (ST) and then positions this ST using the reflected echo signal from the ST. To evaluate the sensing performance, the CramérRao Bound (CRB) is derived according to the proposed roundtrip channel model based on electromagnetic theory. Moreover, a maximum likelihood detection scheme is proposed to position the ST under the maximum likelihood criteria. Simulation results demonstrate the high accuracy of CAPA-enabled mono-static sensing.

Unmanned Aerial Vehicles (UAVs) face significant challenges in autonomous navigation due to their limited energy and computational resources. This paper introduces a knowledge-driven meta-learning framework specifically designed for ultra-fast path planning in lightweight UAVs. The proposed approach integrates domain-specific knowledge across three core domains-environment, network, and behavior-with visual data to enable adaptive learning from unlabeled data and rapid model retraining in various scenarios. To evaluate this framework, we created the Meta-UAV Optimal Path Dataset, a unique dataset tailored for complex, multi-domain path planning tasks. Additionally, a knowledge-driven loss function incorporating physics-based constraints ensures that the model's predictions align with real-world conditions. Experimental results demonstrate that our model achieves superior path efficiency, cross-domain adaptability, and lower resource consumption compared to traditional models, making it a suitable choice for real-world UAV applications.

Terahertz (THz) band is acknowledged as a candidate spectrum in future networks. However, THz communications demand the assistance of high-accuracy parameter estimations to achieve narrow beam-alignment. Despite existing works have intensively explored sensing-assisted THz communications, they fail in mobile and near-field scenarios without concerning the benefits brought from velocity and distance estimations. To this end, a closed-form framework toward sensing-assisted terahertz near-field mobile communications (SA-TNMC) is introduced in this paper. Four-dimensional parameters containing distance, azimuth angle, radial velocity, and tangential angular velocity are extracted from the echoes, all of which are utilized for beamfocusing. For performance characterizations, both Cramér-Rao Bound and Ergodic Shannon Capacity are derived in closedform expressions. Besides, a novel critical point is proposed to demarcate the minimized resource division factor to activate SA-TNMC system. At last, numerical results are proceeded to validate the framework.

The spectrum shift to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose the space-frequency Fourier transformation (SFFT)-based wideband beamforming design for RIS-assisted systems. In the proposed design, we exploit SFFT and stationary phase method to yield an approximate closed-form solution of RIS phase shifts, which significantly reduces signal processing complexity. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band to overcome the beam squint effect. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve communication and sensing performance. Furthermore, the proposed method can be extended to generate any expected frequency-domain beampattern which provides valuable insights into the design of novel wideband beamforming for RIS-assisted systems.

Integrated sensing and communication (ISAC) is emerging as a key enabler of future wireless networks. However, existing analyses of ISAC performance commonly assume discrete-time systems, overlooking the impacts of temporal, spectral, and spatial properties. To address this limitation, we establish a unified information model for band-limited continuous-time ISAC systems. In this model, we employ a novel sensing performance metric called the sensing mutual information (SMI). Our analysis demonstrates how SMI serves as a bridge between the mutual information domain and the minimum mean squared error (MMSE) domain. Additionally, we characterize the communication mutual information (CMI)-SMI and CMI-MMSE regions to identify the performance bounds of practical ISAC systems and reveal the trade-off between communication and sensing performances. Moreover, through analysis and numerical results, we derive two valuable insights for the design of ISAC-enabled systems: i) communication prefers waveforms with random amplitude, sensing prefers waveforms with constant amplitude, and both benefit from waveforms with low correlations and random phases; ii) a linear positive proportional relationship exists between allocated time-frequency resources and the achieved communication rate/sensing accuracy.

Low earth orbit (LEO) satellite communication has been seen as a promising solution for achieving global ubiquitous connectivity and high data rates. However, the propagation delays between satellites and cells are time-varying and significantly different, resulting in asynchronous inter-cell interference. Most existing research assume that the signals from all satellites are received simultaneously, which oversimplifies the interference situation and limits their applicability. To address this challenge, a beam management approach with asynchronous interference mitigation is proposed to improve network throughput. Firstly, a primary serving duration allocation method is developed based on convex optimization, while asynchronous interference is ignored. Subsequently, considering that inter-cell interference can be mitigated by appropriately setting guard periods, a beam scheduling with guard period selection method is designed under given service duration constraints, where conflict graphs are constructed to characterize interference situations. Extensive simulation results validate that our proposed scheme can effectively mitigate asynchronous inter-cell interference and significantly enhance network throughput. Specifically, the network through-put is increased by 20% compared with baselines that ignore asynchronous interference.

The rapid advancement in communication and computation technologies of vehicles has led to an explosion of data at the network edge; the data can be used in decision-making and supporting services such as traffic management, environmental monitoring, and autonomous driving. The vehicles can assist in data sensing and dissemination. However, not all vehicles will agree to participate in such a task without a reward. Designing incentive mechanisms for vehicular crowdsensing is complicated due to the high mobility of vehicles, link reliability, and utility maximization. This paper proposes a vehicular data sensing and dissemination scheme based on contract theory; vehicles are offered sensing contracts while considering their performance value, which includes reputation, link reliability, and sensing power. Simulation results show that the proposed scheme performs better than some closely related baseline schemes.

The rapid growth of wireless devices has significantly increased the demand for user capacity in communication systems while exacerbating interference challenges. Existing multiple access techniques often face challenges in user capacity, particularly under unfavorable channel conditions. To tackle this issue, we propose a complementary coded scrambling hopping multiple access (CCSHMA) scheme that is designed to address the high user capacity demands in low signal-to-interference-plusnoise ratio environments. Our scheme utilizes three-dimensional complementary codes to scramble signals across multiple domains to mitigate interference. Furthermore, we implement a grouped code-hopping scheme to improve the user capacity. Simulation results indicate that the CCSHMA scheme effectively improves user capacity and achieves a reduced bit error rate under poor channel conditions compared to multiple-input multiple-output orthogonal frequency division multiple access.

Hybrid analog and digital ultra-massive multiple-input multiple-output (UM-MIMO) has become one of the key enabling technologies for the upcoming 6G. As the number of antennas of UM-MIMO systems increases and the array size grows, the near-field assumption should be considered instead of the far-field assumption. Therefore, more complex algorithms are required to estimate the DoA and distance that describe the characteristics of the source. In this paper, we propose an efficient near-field localization algorithm for hybrid analog and digital UM-MIMO systems, which reduces the high computational complexity of existing algorithms in the near field by decoupling directions of arrival (DoA) and distance estimation. Firstly, by designing the digital combiner, we estimate the DoA using the central subarray received signal. Next, we design a set of analog combiners that depend solely on the distance, and apply signal-to-noise ratio (SNR) determination to narrow down the range for distance estimation. Finally, digital combiners are applied to perform an exhaustive search within the narrowed distance range, enabling efficient localization. Simulation results show that the localization performance of our proposed algorithm is superior to the existing algorithms, and its computational complexity is significantly reduced.

Emerging as a promising technology to bridge the trust gap among multiple participants, blockchain has been envisioned to enable dynamic spectrum sharing in a decentralized manner. However, satellites with limited resources may struggle to support the frequent interactions required by blockchain networks. Additionally, due to the large coverage area of satellites, the differentiated spectrum sharing needs in various regions can make traditional blockchain approaches inadequate. In this paper, a two-tier multi-region blockchain-based dynamic spectrum sharing approach (TMB-DSS) is proposed. This approach enables regions to manage spectrum autonomously while jointly maintaining a unified blockchain ledger. Moreover, a theoretical framework using stochastic geometry is derived to evaluate the stability performance of TMB-DSS. Finally, numerical results are presented to validate the proposed approach.

The increasing demand for integrated sensing and communication (ISAC) in sixth generation (6 G) mobile networks calls for advancements in sensing parameter estimation technologies. Orthogonal frequency division multiplexing (OFDM) is the core technology of the fifth generation (5G) system with excellent resilience to multi-path fading and is gaining popularity as an ISAC waveform. However, the performance of traditional range/velocity (r/v) estimation algorithms, e.g., multiple signal classification (MUSIC), is restricted by the resolution defined by the bandwidth and symbol duration of OFDM, especially in multi-target scenarios. To overcome this issue, we proposed a REsidual network based SPEctra Calibration (RESPEC) algorithm. It improved the multi-target sensing accuracy by residual network that resists gradient vanishment to calibrate the spectra generated with the two-dimensional MUSIC (2D-MUSIC). Simulation results demonstrated the superiority in r/v estimation accuracy of RESPEC in multi-target scenarios, compared to the traditional 2D-MUSIC algorithm and other benchmarks. The results also exposed a trade-off between neural network depth and communication bandwidth for range estimation.

Reconfigurable Intelligent Surface (RIS) is a promising low-cost technology for enhancing wireless communication coverage. It consists of passive reflecting elements that can adjust the phase shifts of incident signals. RIS is not only deployed on ground structures but also integrated with unmanned aerial vehicles, high-altitude platforms, and satellites. This enables the establishment of virtual line-of-sight (VLoS) links, thereby over-coming obstacles, extending communication range, and reducing long-distance path loss. However, satellite-mounted RIS faces challenges such as increased launch costs and the need for complex dynamic beamforming optimization. Fortunately, the deployment of large plane array antennas is becoming mainstream in low Earth orbit (LEO) satellite constellations. These large arrays, acting as high-reflectivity meta-surfaces, enable reflection communication via satellites. In this paper, we propose a beyond-line-of-sight long-distance reflection communication system using LEO satellites. Specifically, we introduce a new paradigm where LEO satellite serves as a passive reflector to establish a VLoS path between ground stations. We also propose a satellite selection method to choose suitable satellites as passive reflectors and a communication window prediction method to determine optimal communication opportunities. Simulation results validate the feasibility and effectiveness of the proposed system.

Terahertz (THz) offers the availability of huge bandwidth to provide unprecedented data rate for the sixth-generation mobile communication and beyond. Since the narrow beam would be transmitted in the terminals to address severe path loss, THz beam alignment has been a source of significant overhead. The Direction of Arrival (DoA) sensed from a low-frequency band communication system could help fast beam alignment of THz communication. However, it is inefficient since it wastes hardware sources and requires exchange information between the sub-6 GHz and THz systems. In this article, based on the spatial similarity of the intermediate frequency (IF) channel and THz channel, a THz communication system is designed by sensing the DoA of the leaky waves from IF ports of the classical superheterodyne communication structure to aid the THz beam alignment, in which the IF part is controlled by a switching network. It is fast and efficient, since the DoA is employed and no additional communication baseband is used. Compared to the conventional beam alignment methods, the proposed shows well performance on accuracy, reactiveness and overhead, especially in scenarios for limited channel measurement as well as massive multiple-input multiple-output which are regular in THz communication.

To meet the multidimensional extreme performance requirements of intelligent services in sixth-generation mobile (6G) networks, it is crucial to implement the joint management of sensing, communication, and computation resources. However, the competition between services and the inherent conflicts among multidimensional resources result in a prominent contradiction between the efficiency of joint resource management and its high complexity. To address the challenges, a multi-service coexistence model is proposed, incorporating sensing, communication, and computing requirements. The optimization problem is decomposed to enable a low-complexity solution. Initially, a service resource management and mode selection algorithm is proposed, leveraging attention-assisted multi-agent reinforcement learning to effectively coordinate service resource competition. Subsequently, a one-to-one matching game is developed for radio resource blocks and users, ensuring stable maximization of joint sensing and communication performance while optimizing radio resource reuse. Finally, a computing resource management algorithm is designed using the Lagrange multiplier method and Karush-Kuhn-Tucker conditions to enhance computing performance. Theoretical analysis and numerical simulations validate the proposed schemes in terms of low complexity and high effectiveness, achieving approximately 20% overall performance improvement over baseline schemes.

With gigahertz-level bandwidth, terahertz (THz) holds promise for achieving exceptionally high transmission rates in prospective sixth-generation (6G) communications. However, considerable path loss poses an obstacle to THz communications. To compensate for this, massive multiple-input-multiple-output (MIMO) based beamforming is utilized to promote directional power with narrow beams in communications. In dynamic environments, the frequent adjustment of narrow beams results in fast time-varying channel state information (CSI), which constrains the application of the THz communication systems. While traditional deterministic-based and statistical-based channel tracking methods address different aspects of this issue, they suffer from balancing accuracy and complexity in the THz dynamic environments. To solve this problem, based on the cluster distribution of THz time-varying channel, we propose a novel hybrid channel tracking method that uses deterministic physical motion variation law to extract the cluster subspace, and then statistical Markov evolution models are applied within it. To achieve this, an integrated clustering and estimation method, clustering subspace matching pursuit (CSMP) is proposed for obtaining the channel clusters prior knowledge. Then based on above hybrid tracking method design, we propose a virtual cluster subspace turbo-approximate message passing (VCS-TAMP). Finally, several simulation results validate that our proposal achieves great improvement in both accuracy and computational time performance.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs’ requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs’ caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.

The growing demand for edge computing is driving the proliferation of wireless computing power infrastructures and poses significant challenges to network energy efficiency (EE). Traditional offloading schemes rely solely on multi-access edge computing (MEC) servers. High-quality communication links and adequate distributed resources are expected to improve EE. Inspired by reconfigurable intelligent surface (RIS) and device-to-device communication technologies, this paper first proposed an edge-end collaborative computing system in RIS-empowered heterogeneous wireless computing power networks. Through resource virtualization, heterogeneous computing powers on MEC servers and nearby devices are unified into resource pools for efficient utilization. In this wireless system, task offloading is closely coupled with channel allocation, power coordination, RIS phase shift design, and base station receive beamforming. To tackle it, this paper suggested a block coordinate descent (BCD)-based framework that decouples the problem into three sub-problems. For each sub-problem, specialized solutions are applied: the Rayleigh quotient maximization and concave-convex procedure for the beamforming and power allocation co-design sub-problem, dimensionality reduction for 3-dimensional task offloading and channel allocation co-pairing sub-problem, and convex optimization for the RIS phase shift control sub-problem. Numerical results showed that the proposed method can outperform benchmark approaches in terms of EE and delay by up to 28.5% and 22.9%, respectively.

To enhance the positioning and tracking performance of dynamic user equipment (UE) in wideband millimeter-wave (mmWave) systems, we propose a novel positioning error lower bound (PELB)-driven ping-pong positioning framework, where the base station (BS) and UE alternately transmit and receive adaptive beamforming signals for positioning. All beamformers are scheduled based on the locally evaluated PELB. In this framework, we exploit multi-dimensional information fusion to assist in positioning. Firstly, a multi-subcarrier collaborative positioning error lower bound (MSCPEB) is proposed to evaluate the positioning error limits of wideband mmWave systems, which quantifies the contribution of all subcarriers to positioning accuracy. Moreover, we prove that the MSCPEB does not exceed the arithmetic mean of the PELBs of the individual subcarriers. Subsequently, we develop an alternating optimization (AO) algorithm to optimize the hybrid beamformers targeted for MSCPEB minimization. By convexifying this problem, closed-form solutions of beamformers are derived. Finally, we develop a multipath collaborative positioning method that quantifies the impact of path reliability on positioning accuracy, with a closed-form solution for user position derived. The proposed method does not rely on path resolution and traditional triangular relationships. Numerical results validate that the proposed method improves estimation accuracy by at least 16% compared to potential schemes without optimized beam configurations, while requiring only approximately one-quarter of the slot resources.

Code-hopping multiple access (CHMA) technology is an effective approach for improving the user capacity of direct sequence spread spectrum systems. However, the use of randomly generated code-hopping (CH) patterns in a typical CHMA system often causes code collisions that severely degrade the bit error rate (BER) performance. Our previous work has developed a CH pattern design method to improve the BER performance of complementary coded CHMA (CC-CHMA) by pre-generating well-designed CH patterns. However, this method is mainly applicable to single-path channels, while detecting collisions and taking appropriate actions in complex multipath channels remains challenging. In this work, we present a collision-aware algorithm and propose a CH pattern update algorithm based on multi-agent reinforcement learning for CC-CHMA in uplink multipath channels. The simulation results show that the proposed CH pattern design scheme can automatically adapt to the uplink multipath channels and effectively reduce code collisions, thereby achieving further improved BER performance compared with existing CC-CHMA systems.

As one of the key enabler technologies for future networks, terahertz communications (THzComs) are vulnerable to performance degradation and even outage in mobile and near-field scenarios. The emerging integrated sensing and communications (ISAC) technique paves a way to settle the challenges by estimating user position or channel state to achieve beam-alignment, but fails to content the needs of near-field beam-focusing in mobile scenarios. To this end, a closed-form model for terahertz near-field sensing-assisted mobile communications (TNF-SAMC) is provided. Firstly, the TNF-SAMC system models in temporal/frequency/spatial-division schemes are exhibited. Secondly, a near-field four-dimensional sensing framework characterized by Cramér-Rao Bound is put forward. Thirdly, considering the assistance of sensing, the closed-form expressions of Ergodic Shannon Capacity is derived. Next, the theoretical analysis is proceeded to reveal TNF-SAMC performance features in terms of Division Factor, Joint Cramér-Rao Bound, and Critical Point. Especially the simplified closed-form derivations of Critical Points are given, hinting the minimum resources that should be allocated for sensing in TNF-SAMC systems. Then, numerical results verify the conclusion that temporal-division scheme prefers low mobility scenario, spatial-division scheme performs better in beam alignment case, while frequency-division scheme is most superior in TNF-SAMC systems.

Next-generation mobile communication systems will employ higher frequency bands and larger antenna arrays to meet the growing demand for data rates. However, this shift will extend the Rayleigh distance, resulting in near-field effects. Traditional far-field algorithms for source sensing yield considerable errors under these conditions. Near-field spatial spectrum estimation algorithms require multi-dimensional spectral peak searches, which lead to high computational complexity and necessitate a trade-off between sensing accuracy and real-time performance. Consequently, there is an urgent need for high-precision, low-complexity algorithms suitable for near-field sensing. This study proposes a calibration algorithm for spectral peak searches in near-field spatial spectra, referred to as the joint parabolic interpolation and barycenter calibration (PI-BC) algorithm. Simulation results indicate that, compared to existing parameter estimation algorithms, the joint PI-BC algorithm significantly improves sensing accuracy. Furthermore, the computational complexity of the calibration process in the joint PI-BC algorithm is negligibly low.

To achieve ubiquitous wireless connectivity, low earth orbit (LEO) satellite networks have drawn much attention. However, effective beam management is challenging due to time-varying cell load, high dynamic network topology, and complex interference situations. In this paper, under inter-satellite handover frequency and satellite-terrestrial/inter-beam interference constraints, we formulate a practical beam management problem, aiming to maximize the long-term service satisfaction of cells. Particularly, Lyapunov framework is leveraged to equivalently transform the primal problem into multiple single epoch optimization problems, where virtual queue stability constraints replace inter-satellite handover frequency constraints. Since each single epoch problem is NP-hard, we further decompose it into three subproblems, including inter-satellite handover decision, beam hopping design and satellite-terrestrial spectrum sharing. First, a proactive inter-satellite handover mechanism is developed to balance handover frequency and satellite loads. Subsequently, a beam hopping design algorithm is presented based on conflict graphs to achieve interference mitigation among beams, and then a flexible satellite-terrestrial spectrum sharing algorithm is designed to satisfy the demands of beam cells and improve spectral efficiency. Simulation results show that our proposal significantly improves service satisfaction compared with baselines, where the average data queue length of beam cells is reduced by over 20% with affordable handover frequency.

Isolatability and scalability are two critical issues faced by blockchain. Blockchain interoperability addresses isolatability between heterogeneous blockchains, while sharding-based blockchain achieves scalability by solving isolatability of homogeneous blockchains running in different shards. To ensure atomicity between different blockchains, they both need to overcome two significant problems: 1) how to handle cross-chain transactions without trusting any third parties; 2) how to enhance the resistance to double-spending attacks of participating blockchains. To this end, this work presents a two-tier multi-zone architecture, Distributed and Parallel Blockchain (DP-Chain), whereConsensus Zonein tier-1 features blockchain interoperability and sharding-based blockchain by allowing homogeneous or heterogeneous blockchains run in different zones, whileCoordination Layerin tier-2 allows them to interoperate as a Direct Acyclic Graph (DAG) without trusting any third parties. Meanwhile, the coordination scheme between the two tiers is designed to resist potential double-spending attacks. Then, stochastic models are used to captureDP-Chainconsensus process and analyze the probability of successful double-spending attacks. These analyses can help understandDP-Chaineasily and offer theoretical guidelines for further implementations. Finally, based on the proposed architecture, a practical system is realized with C++ and open in Github for testing. Extensive experiments show correctness and effectiveness of this design.

Driven by diverse applications, radio access networks (RAN) are expected to embrace built-in computing and intelligence, forming a versatile wireless computing platform that closely integrates communication and computing. To fully unleash the potential of such a synergistic system, it is essential to coordinate communication and computing with intelligence unlocked by the radio intelligent controllers (RICs) in O-RAN. Building on the groundwork established by existing theoretical studies and simulations, we develop a platform that can emulate the events in the real-world system in more detail, bringing theoretical works closer to practical implementation. In this paper, we first introducens-GP-O-RAN, a software simulation platform developed over ns-3, enabling communication, computation task processing, large-scale data collection, and testing of system-level orchestration policies through user-level control. Taking virtual reality (VR) as an example, we formulate the computation offloading problem and develop a prediction-based computation offloading xAPP, which contains a prediction phase to predict users’ end-to-end (E2E) performance with the deep neural network and a system-level decision-making phase for global orchestration with the differential evolution algorithm. We evaluate the system capacity and E2E latency over the developed ns-GP-O-RAN, which is more effective than existing approaches.

As data transmission demand grows rapidly, terahertz (THz) frequency has been regarded as the most promising candidate in future sixth-generation (6G) communication systems. Facing severe path loss, THz communications have to use massive MIMO technology to promote the directional transmission gain in narrow beams. However, narrow beams and wide bandwidth signal in THz communications together result in the time-frequency-space non-stationary channel. To track this non-stationary channel, we first combine THz massive MIMO communication system with orthogonal time frequency space (OTFS) modulation to get the union sparse channel on delay-Doppler-angle (DDA) domain. Then this non-stationary channel is modeled through our proposed DDA-2D-Markov models (DDA-2D-MM) considering the unique channel characteristics on delay, Doppler and angle dimension respectively. Besides, we design a unique DDA-structured approximate message passing (DDA-STAMP) algorithm to track the dynamic channel with the prior information in consecutive time slots to improve the algorithm convergence speed and accuracy performance. Simulation results show that the proposed DDA-STAMP algorithm enhances both of the time consumption and accuracy performance in THz massive MIMO-OTFS communication channel tracking.

The forthcoming sixth generation (6G) mobile communication system aims to advance technologies that span and integrate computation and communications. Computing power networks (CPNs) and virtualized radio access networks (vRANs) are regarded as two fundamental techniques to achieve this integration. Network functions of virtualized next-generation Node Bs (vgNBs) are implemented on general-purpose servers to process protocol stacks. The energy consumption of vgNBs accounts for a significant portion of energy consumption. However, the proliferation of computing power nodes results in increased energy consumption in CPNs. Power usage effectiveness (PUE) reflects the efficiency of computing nodes while efficiency of computing power (ECP) is adopted to indicate data rates per computing power unit. In this work, a joint load adjustment and sleep management scheme was designed to maximize ECP while minimizing PUE. The optimization problem was formulated as a mixed integer non-linear programming (MINLP) problem, which is NP-hard. A quantum genetic algorithm (QGA) with non-equal size quantum register was suggested to solve this problem. Simulation results demonstrated that the proposed algorithm could outperform benchmark approaches in terms of convergence speed, ECP, PUE, and computing power consumption. When compared to other methods, the proposed approach could improve ECP and computation energy consumption by up to 19.5% and 21.7%, respectively.

Integrated sensing and communication (ISAC) has been widely recognized as an effective solution to achieving robust performances in both communication and sensing within the same spectrum, but interference poses a critical challenge in waveform design. To address this issue, we propose a code-domain waveform approach applicable to multi-user and multi-target ISAC scenarios. Specifically, this work describes the scheme to mitigate multipath, multi-user, multi-antenna, and mutual interferences between communication and sensing by applying three-dimensional complete complementary codes (3D-CCC). This study provides a comprehensive overview of the codebook structure and details the signal processing workflow. The impact of codebook parameters on system performance is evaluated through simulations considering bit error rate (BER), data rate, radar detection probability, and Kullback-Leibler divergence (KLD). The simulation results show that, in terms of communication performance, the code-domain-based spread spectrum technique enhances the robustness to interference and ensures reliable signal transmission. In terms of sensing performance, 3D-CCC achieves higher range resolution and lower angular mean square error. Under certain conditions, the proposed scheme outperforms existing systems in terms of detection probability.

Integrated sensing and communication (ISAC) has been recognized as a key enabler and feature of future wireless networks. In the existing works analyzing the performance of ISAC, discrete-time systems were commonly assumed, which, however, overlooked the impacts of temporal, spectral, and spatial properties. To address this issue, we establish a unified information model for the band-limited continuous-time ISAC systems. In the established information model, we employ a novel sensing performance metric, called the sensing mutual information (SMI). Through analysis, we show how the SMI can be utilized as a bridge between the mutual information domain and the minimum mean squared error (MMSE) domain. In addition, we illustrate the communication mutual information (CMI)-SMI and CMI-MMSE regions to identify the performance bounds of ISAC systems in practical settings and reveal the trade-off between communication and sensing performances. Moreover, via analysis and numerical results, we provide two valuable insights into the design of novel ISAC-enabled systems: i) communication prefers the waveforms of random amplitude, sensing prefers the waveforms of constant amplitude, and both communication and sensing favor the waveforms of low correlations with random phases; ii) There exists a linear positive proportional relationship between the allocated time-frequency resource and the achieved communication rate/sensing accuracy.

The novel concept of mobile edge generation (MEG) is investigated, where the generative artificial intelligence (GAI) model is partitioned into sub-models to be distributed in the network edge, thus enabling latent feature exchange between the edge server and user equipments (UEs). A seed coding module is introduced to encode the intermediate latent features generated by the GAI sub-model at the edge server into flexibly-sized seed for transmission to UEs, instead of transmitting large-size raw data. A weighted energy consumption minimization problem is formulated by jointly optimizing the seed coding ratio (SCR), transmit power, and computing frequencies while guaranteeing the quality-of-generation requirements including total latency and peak signal-to-noise ratio (PSNR). To enhance the resilience of the MEG models against the channel noise, a joint fine-tuning scheme based on low-rank adaption is proposed to train the introduced rank-reduced bypass matrices and seed coding module. Based on the fine-tuned results, a PSNR model regarding SCR and communication signal-to-noise ratio is established to overcome the optimization difficulty due to the lack of the explicit PSNR model. A proximal policy optimization-based MEG energy consumption optimization (MEG-ECO) algorithm is proposed to solve the formulated problem, where the order of magnitude balancing on state and penalty shaping are exploited for more efficient learning. Numerical results reveal that 1) the fine-tuned MEG models have superior resilience against the channel noise; 2) the proposed MEG-ECO algorithm can significantly reduce energy consumption by up to 87.4% compared to conventional centralized generation and up to 33.5% against MEG without seed coding module; and 3) the energy consumption decreases when more partial models are assigned to the edge server, whereas this impact diminishes as the latency threshold is relaxed.

The active reconfigurable intelligent surface (RIS) consists of multiple independently controllable reflective elements, each equipped with a power amplifier (PA). Deploying it in a wireless communication environment allows the amplified signal to be reflected to users, thereby enhancing the performance. However, a large number of independent PAs brings challenges in energy efficiency (EE). To address this issue, we introduced a sub-connected hybrid RIS (SC-HRIS). The SC-HRIS consists of multiple passive and active elements, with active elements organized into groups, each equipped with a dedicated PA. For RIS-assisted downlink multi-user multiple-input single-output systems, we considered joint transmit beamforming and hybrid RIS coefficient design. The EE maximization problem was formulated and solved by fractional programming in conjunction with the block coordinate descent (BCD) method. Simulation results proved that the proposed SC-HRIS could outperform the other RIS approaches in terms of EE.

Relying on the powerful communication capabilities and rapidly changing geometric configuration, the Low Earth Orbit (LEO) satellites have the potential to offer integrated communication and navigation (ICAN) services. However, the isolated resource utilization in the traditional satellite communication and navigation systems has led to a compromised system performance. Against this backdrop, this paper formulates a joint beamforming design and satellite selection optimization problem for the LEO-ICAN network to maximize the sum rate, while simultaneously reconciling the positioning performance. A two-layer algorithm is proposed, where the beamforming design in the inner layer is solved by the difference-of-convex programming method to maximize the sum rate, and the satellite selection in the outer layer is modeled as a coalition formation game to simultaneously reconcile the positioning performance. Simulation results verify the superiority of our proposed algorithms by increasing the sum rate by 16.6% and 29.3% compared with the conventional beamforming and satellite selection schemes, respectively.

In the design of grant-free multiple access (GFMA) mechanisms, the number of active users (UEs) with heterogeneous delay tolerance is commonly assumed to be known, which is less practical in real-world implementations. In this paper, we propose a network state sensing assisted resource allocation algorithm for the GFMA systems, in which both the delay-sensitive unmanned aerial vehicles (UAVs) and the delay-tolerant terrestrial UEs simultaneously access to the base stations (BSs). Taking the limited radio resources and the practical assumption that the number of active UEs is not available at the BSs into account, we first design a Bayesian-based estimation algorithm that can determine the number of delay-sensitive UAVs and delay-tolerant terrestrial UEs in real-time. Based on the estimated result, we further develop a dynamic algorithm to judiciously allocate the limited radio resources according to the weights and number between the UAVs and the terrestrial UEs. Moreover, we dynamically adjust the access class barring factors to ensure the access delay requirements of the UEs. Through numerical results, we show how our proposed algorithm can achieve almost the same performance as that of the ideal algorithm assuming perfect information on the number of active UEs is available.

Deep neural networks have achieved promising progress in remote sensing (RS) image classification. However, the training process requires abundant samples for each class, and it is unrealistic to annotate labels for each RS category, especially considering that the RS target database is increasing dynamically. Therefore, we introduce an innovative prototype network tailored for Generalized Category Discovery (GCD) in remote sensing scene classification. This network consists of two essential modules: one dedicated to representation learning and the other to prototype learning. Through extensive experiments conducted on three benchmark datasets, i.e., RSS-DIVCS, NWPU-RESISC45, and AID, we demonstrate that the proposed model achieves remarkable performance gain up to 20%, effectively addressing the challenges inherent in classifying dynamically varying remote sensing images.

Satellite communication with beam hopping is a promising approach to meeting wide-area user traffic demands under on-board resource limitation. However, there is currently a lack of theoretical models that characterize the impact of beam hopping on user uplink transmission performance. In this article, two simplified beam hopping modes for satellite communication are proposed, namely, probabilistic beam hopping and deterministic beam hopping. Based on these two modes, two Markov chain models are established, which describe the states and state transitions of users. In the first model, the user state incorporates only user queue length, while the state in the second model considers both user queue length and time slot index. For both models, we first derive their steady-state probabilities and state transition probabilities, and then the steady-state probability of successful packet transmission is solved through a numerical method. Further, we theoretically derive the explicit expressions of various user performance metrics based on the steady-state probability of successful packet transmission, such as average throughput, buffer occupancy rate, packet loss rate, and transmission delay, and simulation results verify the correctness of all expressions. By simulation, it is shown that the minimum beam illumination probability or time length can be determined given specific performance requirements to guide system design, and there is an inflection point for the improvement in system performance with the increase of beam resource. Moreover, it is found that deterministic beam hopping achieves lower user delay compared to probabilistic beam hopping when the system is near to saturation.

Ultradense networks (UDNs) are considered to be a key technology that can meet the growing rate requirements caused by the explosion of user equipments (UEs) in the Internet of Things (IoT) applications. The dense deployment of small-cell base stations (SBSs) facilitates the reuse of spectrum resources but also leads to significant interference among adjacent SBSs. Joint transmission (JT) technology can alleviate intercell interference and improve throughput. However, signal processing and backhaul during BS cooperation require additional power consumption, which reduces the energy efficiency (EE) of UDNs. Additionally, the arrival time of received signals from different cooperative BSs at UEs results in asynchronous interference, which poses a significant challenge for JT. To improve EE, we need to determine the clustering strategy and address the interference issue of asynchronous JT. Specifically, the EE-centric (EEC) clustering scheme was proposed based on the maximal independent set of graph theory to determine the SBS clusters. In each cluster, asynchronous gap generation and gap compensation operations were employed to eliminate tail interference of asynchronous JT and compensate for the gap between adjacent received blocks, respectively. This approach effectively mitigated the asynchronous interference at UEs. Simulation results demonstrated that the proposed asynchronous interference cancelations in EEC clusters can significantly improve sum rate and EE compared to other schemes.

In this article, an unmanned aerial vehicle (UAV)-mobile edge computing (MEC) network is considered, where cellular-connected UAVs can either handle the computing tasks locally or offload to base stations. Considering the emerging computation-intensive and delay-sensitive applications, how to strike a balance between energy and delay, is one of the key design issues in UAV-MEC networks. Against this backdrop, we establish a double-queue model innovatively, in which the virtual queue is introduced to sense the backlog status of the actual queue. As such, the delay guarantee of computing tasks can be turned into the stable control of virtual queues. The network quality and server heterogeneity are considered to schedule the workloads rationally. Based on the Lyapunov optimization method, we formulate the deterministic problem to achieve a tradeoff between the long-term energy consumption and the time-average traffic delay, by jointly optimizing the offloading decision, resource allocation and trajectory planning, subject to the constraints of queue stability, resource budgets and flying kinematics. To solve this mixed-integer nonlinear programming problem, we propose an energy-efficient and delay-aware online algorithm, in which the problem is first split into equivalent resource allocation and trajectory planning subproblems, and the closed-form solutions of power, slot and computing resource allocation can be derived. Then, based on the Lagrange dual and successive convex approximation methods, these subproblems are solved iteratively to explore the optimality. Extensive simulations validate the superiority of our proposed algorithm over various benchmark schemes, showing its effectiveness in minimizing the energy consumption while simultaneously maintaining the low latency.

Ultraviolet (UV) communication is a promising technology for civilian and military secure communication systems due to nonline-of-sight transmission, low background noise, and high local security. The full-duplex relay-assisted UV communications can achieve longer communication distances and higher efficiency of time–frequency utilization compared with direct UV communications. However, due to the strong scattering effect, serious interrelay-interference (IRI) is inevitably introduced in multihop full-duplex relay links. To mitigate the impacts of IRI, this article proposes an alternate iterative-Newton method (AINM) to optimize jointly the relay placement and transmit powers of each relay. We further propose a space-division coupled full-duplex relay configuration to reduce the influence of IRI. Numerical results show that the proposed AINM can significantly decrease the bit-error rate (BER); and the proposed space-division scheme can further decrease the BER but sacrifices about half achievable information rate. Besides, we demonstrate that, when the communication links are strong, the distance between adjacent relays should gradually decrease and the transmit power increase from the source node to the destination node. However, when the communication links are weak, each relay should adopt its maximum transmit power to achieve the minimum BER.

Terahertz (THz) massive MIMO with wideband hybrid precoding has been considered one of the crucial techniques to compensate for the high-path loss in 6G high-data-rate Internet of Things (IoT). However, the beam split in wideband hybrid precoding makes the beam of different subcarriers aiming at different directions, which results in only partial channel state information (CSI) from the users to the BS. The efficiency of the CSI-based terahertz (THz) wideband beamforming scheme which is more efficient than the hardware-based scheme in narrow-band would degrade severely. To address the degradation, in this article, we first propose a sensing-aided THz wideband hybrid precoding which restores the full CSI. Through sensing and deducing the angle-frequency information, we construct a channel-selecting matrix and inverse the full CSI from our complete channel dictionary. Moreover, in order to satisfy the multiuser access requirements in IoT, we also propose dynamic radio frequency (RF) chains and dynamic power allocation schemes to further enhance the performance in multiuser scenarios based on a new precoding perspective in which each RF chain serves only one user. This benefits from the highly sparse THz channel characteristic. The spectral efficiency and energy efficiency are employed to validate that the proposed is efficient. The numerical results demonstrate that our proposed sensing-aided wideband hybrid precoding scheme achieves similar performance to the optimal precoding and much better performance to the true time delay scheme and the full CSI-based scheme.

In the context of the burgeoning Industrial Internet of Things (IIoT), the proliferation of interconnected devices has created a reservoir of data resources distributed across diverse domains. However, due to the conflict between proprietary data and the use of data, it is a challenge to fully obtain data value in an efficient and legal way. To release the data value in an efficient and legal way, blockchain is considered a promising technology for data security and privacy, which has been widely introduced to cross-domain data governance. In this paper, we propose a blockchain-assisted cross-domain data sharing (BCDS) in IIoT. Specifically, by deploying the permissioned blockchain, we design a zero-knowledge proof scheme to verify data ownership under the criterion of confidence and anonymity. Besides, to prevent the thrid-party from decrypting data, we design a key agreement protocol to ensure that only recipient is authorized to decrypt data based on private key. Furthermore, we theoretically analyze the security performance of schemes. Extensive experiments in simulation computer systems and testbed deployment are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.

Traditional optical positioning techniques employing visible light signals or infrared light signals require line-of-sight links between transmitters and receivers. The wireless positioning techniques using ultraviolet (UV) signals can enjoy both non-line-of-sight (NLOS) positioning ability and immunity to electromagnetic jamming. In this work, we focus on NLOS UV positioning techniques using linearly-arrayed photon-counting receivers. We first derive the geometrical and physical constrains for the NLOS UV positioning using linearly-arrayed receivers. We then derive the analytical relation between location parameters and pointing parameters of unknown transmitter and propose a NLOS UV positioning method with acceptable computational complexity. We further derive the Cramér-Rao bounds for the positioning method when the separate distance between adjacent receivers equals zero. Numerical results demonstrate that the proposed NLOS UV positioning methods using photon-counting receivers can achieve a distance error less than 2 m when the transmitting elevation angle is greater than 30 degrees and the separate distance is greater than 2 m. Besides, we demonstrate that at least three receivers are required to avoid multiple solution problem; and three receivers are enough for achieving an acceptable positioning error for NLOS UV positioning using photon-counting receivers.

Existing integrated sensing and communication (ISAC) systems face two critical challenges: a trade-off between resource allocation and interference management, and a lack of a unified performance metric that can be simultaneously applied to both sensing and communication functions. To address two aforementioned issues, a non-orthogonal multiple access-assisted ISAC (NOMA-ISAC) scheme is first proposed in this paper, which can simultaneously perform the sensing and communication tasks on shared radio resource, significantly improving spectral efficiency while mitigating mutual interference. Furthermore, in order to provide a unified metric to simplify the performance analysis in ISAC systems, we extend the definition of the outage probability from a mean square error (MSE) perspective. Then based on the built framework, the tractable expressions of the outage probability are derived for both communication and sensing tasks to evaluate the performance of our proposed NOMA-ISAC scheme and the conventional orthogonal radio resource-based ISAC (OR-ISAC) scheme. Next, the asymptotic outage probability comparisons are carried out to show that the NOMA-ISAC scheme can achieve better performance than the OR-ISAC scheme. Finally, simulation results are provided to verify the analytical derivations, and also demonstrate the robustness and performance gains of our proposed NOMA-ISAC scheme over OR-ISAC scheme.

Mega low earth orbit (LEO) satellite constellation is promising in achieving global coverage with high capacity. However, forwarding packets in mega constellation faces long end-to-end delay caused by multi-hop routing and highcomplexity routing table construction, which will detrimentally impair the network transmission efficiency. To overcome this issue, a distributed low-complexity satellite-terrestrial cooperative routing approach is proposed in this paper, and its core idea is that each node forwards packets to next-hop node under the constraints of minimum end-to-end hop-count and queuing delay. Particularly, to achieve an accurate and low-complexity minimum end-to-end hop-count estimation in satellite-terrestrial cooperative routing scenario, we first introduce a satellite realtime position based graph (RTPG) to simplify the description of three-dimensional constellation, and further abstract RTPG into a key node based graph (KNBG). Considering the frequent regeneration of KNBG due to satellite movement, a low complexity generation method of KNBG is studied as well. Finally, utilizing KNBG as input, we design the minimum end-to-end hop-count estimation method (KNBG-MHCE). Meanwhile, the computational complexity, routing path survival probability and practical implementation of our proposal are all deeply discussed. Extensive simulations are also conducted in systems with Ka and laser band inter-satellite links to verify the superiority of our proposal

Jittering effect is a critical issue in the unmanned aerial vehicle (UAV) millimeter wave (mmWave) communications. In this paper, we identify and characterize the asymmetric impact of jitter on the angular domain information in the UAV mmWave channels, showing how it can lead to significant performance degradation if not properly handled. To address this issue, we propose an adaptive hybrid beamforming for the UAV mmWave communications to maximize the average transmission rate against the asymmetric jitter. The proposed adaptive hybrid beamforming consists of an optimal beam angular range design and an adaptive beamforming vector design. Specifically, we first analytically derive a compact expression of the average transmission rate of our systems. Based on the derived expression, we optimize the beam angular range to maximize the average transmission rate under arbitrary asymmetric jitter. Moreover, we derive the asymptotic expression of the optimal beam angular range in the high signal-to-noise ratio regime. We further develop a simple-yet-efficient algorithm to obtain an adaptive beamforming vector that delivers the optimal beam angular range. Through numerical results, we verify the destructive impacts of the asymmetric jitter, and demonstrate how our proposed scheme can be robust to it, compared to the existing methods without considering the asymmetric jitter.

Owing to its ability to offer collaborative data utilization while ensuring data privacy, federated learning (FL) provides a promising paradigm to enable cooperative intelligent tasks across multiple low-earth orbit (LEO) satellites, such as carbon estimation, traffic surveillance, and forest fire detection. Although the advantages of pushing intelligence to satellites are multi-fold, limited communication channels along with the rigid global model aggregation conditions result in dramatic convergence delays. In order to reduce the convergence time, we propose an asynchronous FL framework in LEO satellite networks by exploiting multiple high-altitude platforms for model aggregation, where the advanced over-the-air computation (AirComp) transmission scheme is utilized for the sake of further reducing energy consumption. Considering the practical constraint of AirComp signal distortion, the objective function of optimizing FL performance is carefully formulated and solved by the proposed quantity-quality jointed linkage search algorithm. Simulation results demonstrate that our proposed asynchronous FL framework outperforms the conventional synchronous FL framework by a decline of 30.07% in convergence time at most. It also provides an average increase of 110% and 580%, respectively, in terms of throughput and energy efficiency in all scenarios considered. Overall, our study presents a beneficial asynchronous FL framework and a fast aggregation scheduling algorithm in LEO satellite networks, accelerating the convergence of the global model with reduced energy expenditure.

To provide seamless communication service, the integrated satellite-terrestrial network (ISTN) has attracted lots of interest, where the promising dynamic spectrum sharing technology is widely used to improve spectrum efficiency. Meanwhile, rate-splitting multiple access (RSMA) has recently emerged due to the advantages of flexible multiple access and robust interference management. Based on the two promising technologies, we design joint satellite-terrestrial RSMA schemes in overlay and underlay spectrum sharing modes for ISTN, which considers both cases with and without inter-beam interference. Furthermore, we propose two adaptive RSMA schemes based on the hybrid spectrum sharing mode for adapting dynamic ISTN, where the number of terminals and spectrum resources available to satellite are unevenly distributed and time-varying due to the broad communication coverage. Finally, we consider the quality of service rate requirements and formulate joint optimization problems to maximize the weighted sum rate. To solve these non-convex optimization problems, we introduce an improved alternating optimization algorithm based on weighted minimum mean square error. Simulation results verify that the proposed schemes have significant performance gains compared with SDMA and NOMA schemes and can better adapt to the dynamic changes in the number of terminals and spectrum resources.

Orthogonal time frequency space (OTFS) is considered a promising modulation technology for integrated sensing and communication (ISAC) systems, which is robust against Doppler effects in time-frequency doubly-selective channels. However, limited by the communication bandwidth and frame duration, the fractional delay and Doppler problem in OTFS-ISAC systems, that is, the delay and Doppler of the channel are not integer multiples of the resolutions, leading to estimation errors. To help overcome this issue, in this work, we propose an amplitude barycenter calibration (ABC) algorithm, which uses observation points on the integer delay-Doppler grid to calibrate target estimates, thereby providing satisfactory sensing performance without broadening the bandwidth. In addition, the estimation error and Cramér–Rao lower bound of the ABC algorithm were derived by analyzing its sensing performance. The results demonstrated that the proposed ABC algorithm can improve distance and velocity sensing resolution while achieving acceptable communication performance.

In this paper, we investigate an anti-jamming hybrid beamforming (HBF) design in millimeter-wave (mmWave) massive MIMO systems for reliable wireless communications. Different from the conventional schemes designed by assuming perfect channel state information (CSI) of both communication channels and jamming channels. We aim to design the HBF scheme by minimizing the dispersion of the output signals in statistics without the knowledge of the jamming channel information. Two partially-connected HBF architectures are considered. Specifically, we propose a two-stage robust HBF design by solving the formulated non-convex problem. First, we introduce an auxiliary vector that represents the product of an analog beamformer (ABF) and a digital beamformer (DBF), and propose a pseudo-Newton method assisted beamforming algorithm by relaxing the non-convex constraint on the ABF. In the second stage, we separately optimize the ABF and DBF for two different partially-connected HBF architectures by proposing a matrix decomposition-based alternating minimization method. Finally, simulation results are provided for demonstrating the superiority of our proposed robust HBF schemes over other benchmark schemes by considering the security threat of potential jamming attacks, especially when the number of snapshots is small.

Ensuring secure access to cellular networks is of paramount importance, in which system information (SI) protection plays a crucial role at the initial access stage. While the 3rd generation partnership project (3GPP) released many standardizations to enhance SI protection for preventing users from false base station (FBS) attacks, most of them are centralized solutions which are vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-enabled SI protection (BeSI), as a compatible and effective secure access scheme, is developed in this work, which aims at guaranteeing the authenticity and reliability of SI by considering the features of blockchain in immutability, traceability, and decentralization. Then, we derive a mathematical framework to justify the superiority of using blockchain in SI protection. Moreover, by resorting to a Poisson point process as the geographical model for both base stations and FBSs, we thus theoretically analyze the security gain of blockchain and understand the impact of network parameters including redundancy rate, number of confirmation blocks, and the density of base stations. Finally, numerical results are demonstrated to validate the effectiveness of BeSI.

Recently, reconfigurable intelligent surface (RIS) has gained growing research interests in sensing fields. While extensive efforts have been devoted to designing RIS space beamforming for improving sensing accuracy, the potential of RIS in improving sensing resolution has so far not been fully dug. In this paper, we investigate the fundamental performance of RIS-enabled multi-target sensing, and exploit the potential of RIS in simultaneously boosting resolution and accuracy. Specifically, based on the identification that sensing is to obtain target state information from the received echo signals, we adopt the sensing mutual information as the performance metric, thus enabling comprehensive evaluation of both sensing resolution and accuracy. Then, we derive the analytical expression of the sensing mutual information in our considered systems, revealing that in addition to providing beamforming gains to enhance sensing accuracy in the space domain, RIS can also flexibly vary its beamforming in the time domain to improve sensing resolution. Based on this result, we propose a novel space-time beamforming, in which space-domain and time-domain beamforming gains are utilized for enhancing sensing accuracy and resolution, respectively. Numerical results demonstrate the significant advantages of the proposed space-time beamforming scheme over the traditional space-only beamforming scheme in terms of sensing resolution.

The strong scattering effects of ultraviolet (UV) signals in the atmosphere enables the non-line-of-sight (NLOS) ability of UV communications, but introduces large time dispersion of the channel impulse response (CIR), which inevitably results in serious inter-symbol interferences (ISIs). Because a larger transmitting rate can introduce a larger ISI, it is meaningful to study the impact of CIR on the ISI and further quantify the achievable information rate (AIR) of NLOS UV communications. In this paper, we analyzed the influence of both coplanar and non-coplanar geometries on the CIR for multiple scattering effects and proposed a modified Gamma function (MGF) to precisely characterize the CIR. Based on the MGF model, we quantified the ISIs and derived the bit-error rate (BER) and the AIR under thermal noise for both on-off keying (OOK) modulation and 2-pulse-position modulation (2-PPM) using photon-counting receivers. The simulation results showed that the proposed MGF model can achieve higher fitting precision of both CIRs and BERs compared with the existing Gamma function model. Besides, we demonstrated that when the thermal noise photons are less than 10, the OOK and 2-PPM can achieve an AIR of 15 Mbit/s and 13 Mbit/s, respectively, under typical system geometries.

Spatial diversity receptions are usually employed to improve the performance of scattering based optical communication (SOC) systems. Existing works on the diversity receptions for SOC systems ignored the detecting properties of the photomultiplier tube (PMT) receivers. In this paper, we focus on single-input multiple-output (SIMO) SOC systems using PMT receivers. We first present the system model of SIMO SOC system considering the link geometries and prove that the traditional maximum-ratio combining is no longer the optimal combining method for PMT receivers. Then we propose the statical combining method and derive its statistical characteristics and the bit-error rate (BER). We further derive some analytical results of the optimal weights for the statical combining in terms of minimizing the BER or maximizing the average signal-to-noise ratio (SNR). Simulation results show that, in non-turbulent channels, the statical combining for maximizing the average SNR can always outperform the equal-gain combining, especially when the difference between signal strengths on different branches is large. Besides, more weights should be allocated to the branches with either larger signal strength or small turbulent variances. Fortunately, in practical SOC systems, the branch with larger signal strength usually enjoys smaller turbulent variance.

Software-defined satellite-terrestrial integrated networks (SDSTNs) are seen as a promising paradigm for achieving high resource flexibility and global communication coverage. However, low latency service provisioning is still challenging due to the fast variation of network topology and limited onboard resource at low earth orbit satellites. To address this issue, we study service provisioning in SDSTNs via joint optimization of virtual network function (VNF) placement and routing planning with network dynamics characterized by a time-evolving graph. Aiming at minimizing average service latency, the corresponding problem is formulated as an integer nonlinear programming under resource, VNF deployment, and time-slotted flow constraints. Since exhaustive search is intractable, we transform the primary problem into an integer linear programming by involving auxiliary variables and then propose a Benders decomposition based branch-and-cut (BDBC) algorithm. Towards practical use, a time expansion-based decoupled greedy (TEDG) algorithm is further designed with rigorous complexity analysis. Extensive experiments demonstrate the optimality of BDBC algorithm and the low complexity of TEDG algorithm. Meanwhile, it is indicated that they can improve the number of completed services within a configuration period by up to 58% and reduce the average service latency by up to 17% compared to baseline schemes.

Federated learning is a promising technique to implement network intelligence for the sixth generation (6G) communication systems. However, the collected data in wireless networks is non-independent and identically distributed (non-IID), which leads to severe deterioration of model performance. Although various enhanced schemes are proposed, it is still challenging to balance the communication cost and the model performance, due to the scarcity of radio resource for model update in wireless networks. In this paper, an ensemble federated learning paradigm is proposed for handling non-IID data, which is also optimized for its deployment in wireless networks in a cost efficient way. First, the framework of ensemble federated learning is designed. By formulating individual user clusters, intra-cluster federated learning models can be generated to reduce the impact of non-IID data, which can be integrated to adapt to various learning data via model ensemble. Second, the optimization of user cluster formation is studied to improve the performance of ensemble federated learning, which is modeled as a coalition formation game to design a Nash-stable algorithm. Finally, the simulation results on the public data sets are provided to verify the performance gains of our proposed schemes for deploying federated learning with non-IID data in wireless networks.

Optical positioning techniques have inherent ad-vantages such as high precision, low power consuming and immunity to the electromagnetic interference. However, it is challenging to estimate the non-line-of-sight (NLOS) targets using current optical positioning methods. In this work, we propose an NLOS ultraviolet positioning method based on a simplified single-scattering channel model, which can obtain both the location and the pointing direction of the transmit-ter. Numerical results demonstrated that the proposed NLOS ultraviolet positioning method can achieve a positioning error less than 2 meters and an azimuth error less than 2 degrees by using two photon-counting receivers under typical transceiver geometries within 100 meters. Besides, we found that the positioning performance can be improved by increasing the gap between two receiving elevation angles.

Currently, Internet of Things (IoT) services in radio access networks require access to multi-dimensional network resources such as communication, computation, and caching to provide customized services. When resources are limited, there is always competition for resources and multi-dimensional resource requirement conflicts (MRRCs), which will lead to performance degradation of the IoT services. Moreover, the diverse resource requirements of IoT services and the fact that multi-dimensional resources are involved in scheduling make it extremely difficult to solve the MRRCs problem. To depict the above issues, we formulate a hierarchical MRRCs model, which applies the Stackelberg model and the multi-objective optimization model to describe the conflicts among services and users, respectively. Then, to address the aforementioned problem, we propose a deep reinforcement learning scheme with a hierarchically structured action space. Additionally, a case study is designed to simulate the resource conflicts of three different types of services on the spectrum, computation capacity, and caching resources. The numerical simulation results show that the proposed scheme has the best convergence ability and overall performance in terms of the MRRCs' coordination compared with the baseline schemes.

The beam split in the frequency domain seriously affects hybrid precoding performance in wideband terahertz (THz) massive multiple-input multiple-output (MIMO) systems. The receiver hardly catches full Channel State Information (CSI) of all subcarriers due to spatial angle diffusion caused by the beam split, which would decrease the efficiency of hybrid precoding. To address the decrease, sensing-aided hybrid precoding is proposed to deduce the frequency-angle information in this paper. A channel matrix inverting scheme which can regain full CSI from the frequency-angle information of partial CSI is modeled. A 300 GHz MIMO system with 15 GHz bandwidth is simulated to validate the proposed sensing-aided hybrid precoding. Spectral and energy efficiency are employed to characterize the performance of the systems. Compared with true-time-delayers (TTD) based and partial CSI based schemes with no sensing in the state of the art, the proposed sensing-aided scheme performs higher spectral and energy efficiency.

Mobile edge computing (MEC) has been envisioned as an essential technology for latency-critical applications by providing computing services in close proximity to mobile users. Bringing MEC to come into practice, how to support user mobility remains challenging. In addition to seeking a thorny tradeoff between service latency and migration cost, both interactions in space and time exist in mobility management, which requires collaboration among users and perfect prior knowledge, including user mobility and network information. In this article, we propose an efficient mobility management framework for MEC networks, in which mobility management is operated centered around users’ performance and cost, while radio access and computing service provision are loosely coupled. With a loosely coupled design, the proposed framework exhibits more flexibility and incurs higher complexity. Focusing on multiuser and multicell MEC networks, this joint control problem is formulated to maximize the long-term total utility accounting for the service delay and migration cost. Considering the exponential complexity, a distributed mobility management approach is developed, which combines game theory and user-oriented deep reinforcement learning to deal with the interactions in space and time. Simulation results show the efficiency and scalability of the proposed approach.

The satellite-terrestrial networks (STNs) network has the characteristics of open links, node movement, dynamic network topology, and diverse collaborative algorithms, which will lead to frequent passive handovers and continuous reauthentication of user equipment (UE). This causes a waste of valuable computing and storage resources in the STNs network and seriously affects the user’s network experience. To address this, we propose an access authentication protocol with user anonymity and traceability to reduce the communication delay and signaling cost of access authentication. In addition, we also propose a hierarchical group key distribution scheme to implement cross-domain handover authentication between different groups of UE, thereby effectively avoiding reauthentication. We take strict security analysis to prove that the authentication protocol we propose is secure. Compared with the three-related existing arts, our protocol can greatly reduce the communication interaction delay and improve the efficiency of initial access and handover authentication of UE.

Reconfigurable intelligent surface (RIS) has shown its great potential in facilitating device-based integrated sensing and communication (ISAC), where sensing and communication tasks are mostly conducted on different time-frequency resources. While the more challenging scenarios of simultaneous sensing and communication (SSC) have so far drawn little attention. In this paper, we propose a novel RIS-aided ISAC framework where the inherent location information in the received communication signals from a blind-zone user equipment is exploited to enable SSC. We first design a two-phase ISAC transmission protocol. In the first phase, communication and coarse-grained location sensing are performed concurrently by exploiting the very limited channel state information, while in the second phase, by using the coarse-grained sensing information obtained from the first phase, simple-yet-efficient sensing-based beamforming designs are proposed to realize both higher-rate communication and fine-grained location sensing. We demonstrate that our proposed framework can achieve almost the same performance as the communication-only frameworks, while providing up to millimeter-level positioning accuracy. In addition, we show how the communication and sensing performance can be simultaneously boosted through our proposed sensing-based beamforming designs. The results presented in this work provide valuable insights into the design and implementation of other ISAC systems considering SSC.

In this paper, we establish an integrated sensing and communication (ISAC) system based on a distributed semi-passive intelligent reflecting surface (IRS), which allows location sensing and data transmission to be conducted on the same time-frequency resources. The detailed working process of the proposed IRS-based ISAC system is designed, including the transmission protocol, location sensing and beamforming optimization. Specifically, each coherence block consists of the channel estimation period, the ISAC period with two time blocks, and the pure communication (PC) period. During the channel estimation period, the low-dimensional effective user-BS channel is estimated. During each time block of the ISAC period, data transmission and user positioning are carried out simultaneously. The estimated user location in the first time block will be used for beamforming design in the second time block. During the PC period, only data transmission is conducted, by invoking the user location estimated in the second time block of the ISAC period for beamforming design. Simulation results show that a millimeter-level positioning accuracy can be achieved by the proposed location sensing scheme. Besides, the proposed two beamforming schemes based on the estimated location achieve similar performance to the benchmark schemes assuming perfect channel state information, verifying the effectiveness of beamforming design using sensed location information.

Wireless federated learning (WFL) undergoes a communication bottleneck in uplink, limiting the number of users that can upload their local models in each global aggregation round. This paper presents a new multi-carrier non-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptive learning setting of Flexible Aggregation. Since a WFL round accommodates both local model training and uploading for each user, the use of Flexible Aggregation allows the users to train different numbers of iterations per round, adapting to their channel conditions and computing resources. The key idea is to use MC-NOMA to concurrently upload the local models of the users, thereby extending the local model training times of the users and increasing participating users. A new metric, namely, Weighted Global Proportion of Trained Mini-batches (WGPTM), is analytically established to measure the convergence of the new system. Another important aspect is that we maximize the WGPTM to harness the convergence of the new system by jointly optimizing the transmit powers and subchannel bandwidths. This nonconvex problem is converted equivalently to a tractable convex problem and solved efficiently using variable substitution and Cauchy’s inequality. As corroborated experimentally using a convolutional neural network and an 18-layer residential network, the proposed MC-NOMA WFL can efficiently reduce communication delay, increase local model training times, and accelerate the convergence by over 40%, compared to its existing alternative.

As an enabler technology in future network, terahertz (THz) joint radar-communication (T-JRC) has been envisioned as a solution to overcome the challenges in THz communications. Recent works on JRC mainly emphasize either performance tradeoffs or mutual benefits between radar and communication functionalities. Unable to characterize both benefit and tradeoff relationships, existing metrics and models are inapplicable to performance analysis on T-JRC systems. In this paper, a closed-form T-JRC performance model are proposed to explore the relationship between radar and communication metrics. To begin with, a novel T-JRC system model concerning random mobility and THz beam tracking is put forward. Next, the performance bounds of T-JRC performance in terms of communication rate and a proposed radar rate are acquired in three different integration cases. Furthermore, the expression of communication rate by radar rate in T-JRC is obtained theoretically, which is the first time to derive the closed-form relationship considering both benefits and tradeoffs to the best of our knowledge. Finally, numerical results validate the proposed model as well as closed-form expression, showing that the model succeeds in benefit and tradeoff relationship characterization in T-JRC systems and the fitting bounds are applicable in non-extreme scenarios.

The full-duplex optical communications can be achieved simply by separating the transmitting link and the receiving link in the space. However, it is challenging to achieve the non-line-of-sight (NLOS) full-duplex ultraviolet (UV) communication due to the serious self-interference caused by the strong multiple scattering effects of UV signals. To explore the capacity of NLOS full-duplex UV communications, in this paper, we first quantify the self-interference using an analytical channel impulse response function. Based on the quantified self-interference, we then derive the error rate and the corresponding achievable information rate (AIR) for on- off keying modulation, 4-digital-pulse-interval modulation and 4-pulse-position modulation. We further propose a self-interference cancelling (SIC) method to mitigate the impacts of self-interferences. Simulation results show that the proposed SIC method can significantly improve the error rate and AIR performances. Besides, we find that the NLOS full-duplex UV communication will gradually lose its advantage over the NLOS half-duplex UV communication as either the communication distance or the elevation angle increases. However, using the proposed SIC method, the NLOS full-duplex UV communication can hold its advantage in a wide range of system geometries.

The performance of unmanned aerial vehicle (UAV)-enabled networks is generally bottlenecked by severe inter-cell interference and limited backhaul connectivity. Against this backdrop, we analyze the performance of the UAV-enabled heterogeneous networks, where antenna downtilt at each UAV-mounted base station (BS) is employed to mitigate inter-cell interference, and the tethered UAV (TUAV)-mounted BS is deployed to provide backhaul connectivity for multiple spatially randomly distributed untethered UAV-mounted BSs, while they are cooperatively serving the terrestrial users. Through leveraging stochastic geometry, the user association probability and conditional distance distributions of serving links are derived. Then, the compact expressions of coverage probability and ergodic rate of the UAV-enabled heterogeneous networks are derived, and the impacts of antenna downtilt and backhaul connectivity are investigated. Numerical results validate our analysis and show the effectiveness of antenna downtilt and the TUAV-enabled backhaul connectivity. Moreover, we find that a larger antenna downtilt angle is required for a higher deployment altitude. We show that the optimal size of the network area that maximizes the average coverage probability reduces, when the backhaul connectivity is considered. We also demonstrate that the average network performance can be significantly improved by judiciously selecting the number of untethered UAVs, which is closely related to the size of the network area. Finally, the advantage of the proposed UAV-enabled heterogeneous network on the average ergodic rate has been validated by comparing it with two benchmarks under the 3GPP channel model.

In this paper, we exploit the potential benefits of intelligent reflecting surface (IRS) in achieving integrated sensing and communication (ISAC) at the same frequency and time resources. To this end, we establish a novel framework, in which a single-antenna user transmits to a multi-antenna base station, with the aid of a distributed semi-passive IRS. In the established framework, the transmission period is divided into two time blocks. At each time block, the distributed semi-passive IRS conducts the location sensing and data transmission simultaneously. Simple-yet-efficient location sensing and beamforming design schemes are respectively proposed. Particularly, the estimated user location in the first time block is used to facilitate the beamforming design of the IRS in the second time block. Through numerical results, we demonstrate that the proposed location sensing scheme can achieve a millimeter-level positioning accuracy, even when the number of semi-passive reflecting elements is small and the allocated sensing time is short. In addition, we show that, utilizing the imperfect location information obtained from our proposed location sensing scheme, the proposed beamforming design scheme can achieve almost the same performance as the optimal beamforming scheme assuming the perfect channel state information, thus verifying the effectiveness of our proposed framework and providing valuable insights into the design of other IRS-enabled ISAC systems.

Analog over-the-air computing enables a swarm of end-user devices to efficiently conduct distributed learning, where the intermediate parameters of users, such as gradients, are modulated and transmitted via a group of orthogonal waveforms, and can be mixed directly at a server without individually detecting the feedback parameters of each user. Nonetheless, the scarcity of orthogonal waveforms, as well as communication resources of the end-user devices, are throttling this paradigm in adopting complex deep learning models. To balance the tradeoff between communication efficiency and accuracy performance, we study model pruning for analog over-the-air distributed learning in this paper. First, a model pruning scheme is proposed to improve the communication efficiency of analog over-the-air training. An importance measure for model parameter pruning is also designed based on the analog over-the-air aggregated gradient, which can characterize the contribution of each parameter without removing channel fading and electromagnetic interference. Second, an analytical expression of the training error upper bound is derived, which shows the proposed scheme is able to converge even when the aggregated gradient is corrupted by heavy-tailed electromagnetic interference with an infinite variance. Finally, several experimental results are provided to show the performance gains achieved by our proposed scheme, and also verify the correctness of analytical results.

Multi-User (MU) MIMO is playing an increasingly important role in wireless communications. As the antennas scale continues to grow, it is expected to fully explore freedom in the space domain in order to support more user access but under the premise of solving interferences in a good way. To address this issue, we construct a new three-dimensional (3D) scrambling code (SC) set for the MU-MIMO systems to reduce the interferences among users in the fading channels. In the proposed scheme, different users can share the same frequency-time block and keep orthogonality in the 3D SC domain without channel information feedback. Besides, it is found that the number of users increases linearly along with the number of transmit antennas. Furthermore, different from the spreading code used in a typical code division multiple access (CDMA) system, the proposed 3D SC can achieve the perfect correlation properties even without spreading the spectrum. In this paper, the capacity of the 3D SC MU-MIMO is discussed in detail, and the bit error rate (BER) performance is evaluated via the simulation results.

Terahertz (THz) joint radar-communication (T-JRC) has been envisioned as a method to overcome the challenges in THz communications. Recent works mainly emphasize the tradeoffs between radar estimations and data communications, unable to characterize the mutual benefit relationship as a key feature of T-JRC system. In this work, a comprehensive performance model considering both tradeoff and benefit relationship is derived for THz systems under random mobility. TJRC performance in three typical cases is analyzed to present the universality of the model. Additionally, the bounds of TJRC performance are acquired in terms of the communication data rate and a novel radar rate extended from the traditional works. Finally, the simulations of T-JRC systems considering mobility, beamwidth and distance are performed to validate the derived models. Numerical results indicate that the mutual benefit relationship dominates in low radar rate, while tradeoffs are crucial when the radar estimations are sufficient.

The ever increasing demand for high transmission rate towards futuristic scenarios stimulates communication systems to utilize a broad spectrum which is supposed to be partially overlapped by the spectrum used by radar. In light of this, communication and radar coexistence (CARC) has developed to be the candidate scheme for the 6th generation (6G) mobile communication. However, the ineluctable interference between the two systems posed by spectral overlap will cause a sharp deterioration on communication performance and most existing approaches to suppress such inter-system interference neglect the scatters of radar signal whose power is relatively large compared with communication transmission. To this end, based on orthogonal frequency division multiplexing (OFDM) communication technique, we propose a successive interference cancellation (SIC) scheme for CARC taking the interference posed by scattering radar signal from the target into account. At the communication receiver, channel impulse response (CIR) of the scattering path is obtained through subtracting the reconstructed communication waveform so that the inter-system interference is estimated and removed. Numerical results with respect to bit error rate (BER) and spectral efficiency (SE) performances verify the effectiveness of our design.

Unmanned aerial vehicle (UAV) communications have been recognized as an important component of future wireless networks, due to the UAVs’ inherent maneuverability and flexibility. However, ultra-dense located buildings and the jittering of the UAVs may significantly degrade the performance of UAV wireless networks. Against this backdrop, this paper analyzes the coverage and throughput performance of three-dimensional UAV networks, taking the probabilistic line-of-sight (LoS) channel and the outdated channel state information introduced by the obstacles and the UAVs’ jittering, respectively, into account. By leveraging the tools from stochastic geometry, the analytical expressions for the coverage probability and throughput in large-scale UAV networks are derived. Through numerical results, the correctness of our derived expressions is validated. In addition, it is shown that the coverage probability and throughput performance of the considered UAV networks can be significantly improved by judiciously selecting the deployment density of the UAVs, even in the presence of blockage and jittering. The results presented in this work provide valuable insights into the design and deployment of large-scale UAV networks in practical implementations.

Virtual reality (VR) streaming is becoming a popular mobile application that requires ultralow latency and active participation of devices. Meanwhile, blockchain is a promising paradigm to decentralize the traditional ledger of a single trusted entity. In this article, a deep deterministic policy gradient (DDPG)-based scheme is proposed to tackle the joint resource allocation and replica selection challenge of VR streaming in blockchain-enabled fog radio access networks (F-RANs). The proposed scheme balances the load on VR streaming and blockchain maintenance and fully exploits the edge caching and computing resources on fog-based access points (F-APs). Thus, less energy is consumed compared with other learning schemes based on extensive simulations.

For conventional signaling, the length of the orthogonal pilot is required at least equal to the total number of user antennas. However, it is not recommended in the Internet of Things (IoT) due to the expensive cost paid in massive connectivities. Thanks to the sporadic nature of the massive connected users where a considerable fraction of users are inactive within a coherence time, the nonorthogonal pilot can be utilized with the joint channel estimation and active-user detection being modeled as a compressive sensing problem. According to the different antenna configuration methods employed by the base station, the constructed problems in this work are formulated into the single measurement vector and the multiple measurement vectors recovery problems. Also, we develop a model-driven deep learning algorithm to solve the problems based on the traditional alternative direction method of multipliers (ADMM) algorithm, where the iteration operation is unfolded into the network layer. The network parameters are learned with the help of the stochastic gradient descent algorithm. Simulation results show that the proposed approach can achieve better performance than an ADMM algorithm under the same computational complexity.

To satisfy the low-latency requirements of emerging computation-intensive vehicular services, offloading these services to edge or cloud servers has been recognized as an effective solution. Due to the limited resources of edge servers and the faraway distance of cloud servers, it is challenging to provide an efficient resource allocation strategy to balance the latency, throughput and the resource utilization. In this paper, an end–edge–cloud collaboration paradigm is presented for computation offloading in fog-based vehicular networks (FVNETs) by incorporating vehicles with idle resources as fog user equipments (F-UEs). To adaptively orchestrate end–edge–cloud resources in different load cases, a two-timescale resource reservation and allocation framework is proposed. Wherein, a Stackelberg-game-based dynamic F-UE incentive problem is first formulated with the cloud server as the leader and multiple F-UEs as the followers, and then an iterative algorithm is proposed to achieve the Stackelberg equilibrium of the computation resource pricing and reservation. On a small timescale, the joint communication and computation resource allocation problem is transferred into a multiagent stochastic game and a lenient multiagent deep-reinforcement-learning-based distributed algorithm is developed to minimize the sum latency. When latency performance deteriorates, F-UE incentive optimization will be triggered to reserve more resources of F-UEs. Simulation results show that the proposed end–edge–cloud orchestrated computation offloading scheme in FVNETs outperforms baselines in terms of average latency.

To securely integrate Mobile Edge Computing (MEC) into Wireless Blockchain Network (WBN), this paper proposes a framework for blockchain based offloading strategy, where blockchain nodes are categorized as blockchain users and blockchain miners from a motivation perspective. Particularly, aiming at improving the motivation ability, a block generation process is first designed for blockchain miners’ short transaction processing time lower bound. Then, to further maximize the utilities of both blockchain users and blockchain miners, an optimization problem is formulated to determine an optimal strategy which involves a trade-off between the fast transaction confirmation rate required by blockchain users and the transaction fees obtained by blockchain miners. A Stackelberg game is introduced to model the interaction between the blockchain users and miners. Meanwhile, a distributed algorithm is designed to converge this strategy in an iterative manner based on the buyer-seller negotiation. Additionally, double-spending attack and selfish mining attack are analysed to examine their impact on the system performance in terms of confirmation delay and throughput while guaranteeing the high security level. Finally, extensive experiments have been conducted to show the rightness and effectiveness of the proposed equilibrium-based strategy and mathematical analysis, and some insights are discussed for the further guide as well.

With the acceleration of a new round of global scientific, technological, and industrial revolution, the next generation of information and communication technology, i.e., 6G, will inject new momentum into industry transformation and upgrading, as well as into economic innovation and development.This will subsequently promote a global industrial integration.Wireless communication will be ubiquitous in all areas of future society, supporting novel applications with various performance requirements, such as immersive-or interactive-experience applications requiring a large bandwidth, autonomous driving and vehicle-to-everything applications requiring ultrahigh reliability and ultra-low latency, and applications for industrial Internet requiring massive machine-type connectivity.Facing the challenges of the post-Moore and post-pandemic era, wireless communication needs breakthroughs in network architecture to improve the intelligence, security, robustness, bandwidth, and heterogeneity.With this background, several important tendencies have emerged in the development of 6G wireless communications

Nowadays, by integrating the smart devices carried by users with existing communication infrastructures to provide large-scale, fine-grained and complex sensing services, crowdsensing as a novel sensing paradigm has significantly enriched the applications of smart city and promoted the development of Internet of Things (IoT). However, privacy has become skyrocketing concern for crowdsensing and gravely affected the deployment of crowdsensing. In this article, we present a framework to make the tradeoff between minimizing data aggregation error and guaranteeing system stability by jointly considering the privacy of participants, the randomness of sensing task arrival and the cost of platform. We propose an online control mechanism by exploiting Lyapunov stochastic optimization technique. Additionally, considering that, in reality, it always takes different time for different tasks to make sensing decisions, we extend standard Lyapunov stochastic optimization technique to make separate decisions for different types of sensing tasks in consecutive time. Through rigorous theoretical analysis, we prove that our time-average data aggregation error is approximately optimal while still maintaining system stability. By carrying out extensive simulations, we demonstrate the superiority of our proposed mechanisms.

Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated learning. To solve this problem, federated learning with non-IID data in wireless networks is studied in this paper. Firstly, based on the derived upper bound of expected weight divergence, a federated averaging scheme is proposed to reduce the distribution divergence of non-IID data. Secondly, to further harmonize the distribution divergence, data sharing is associated with federated learning in wireless networks, and a joint optimization algorithm is designed to keep a sophisticated balance between the model accuracy and the cost. Finally, the simulation results based on a common-used image data set are provided to evaluate the performance of our proposed schemes, which can achieve significant performance gains with a small price of latency and energy consumption.

Fog-enabled vehicular network (FVNET) has been envisioned as a promising solution to provide seamless coverage for the vehicles. However, the mobility of the vehicles and consequently more frequently handoffs make it particularly challenging to characterize the system performance of FVNET. In this paper, we propose an analytical framework to evaluate the coverage performance of FVNET, taking the impact of the vehicles' mobility in two successive time slots into account. Specifically, we first identify all the handoffs cases that can occur due to the vehicles' moblity, namely, no-handoff, horizontal handoff, and vertical handoff. Then, using tools from stochastic geometry, we derive the compact expressions of access probability and joint coverage probability of our system. Numerical results validate our analysis and show that the degrading impacts of the vehicles' mobility on the coverage performance can be well relieved by properly deploying the fog access points.

In this paper, a power allocation strategy is investigated for visible light communications (VLC) in a nonorthogonal multiple access (NOMA) system. The purpose of this work is to maximize the downlink sum-rate under the constraints of quality of service (QoS), power consumption, and LED operating region (LOR). To this end, we first formulated the problem and then analyzed the convexity of the problem. Furthermore, with the help of variable transformation, auxiliary variables and the Taylor series, we approximated the non-convex problem successively with convex problems. Finally, an iterative optimization algorithm with low complexity in some cases was developed. Numerical results show that the proposed scheme can exhibit a higher sum-rate than the modified gain ratio power allocation (GRPA) algorithm.

Despite the advantages of Federated Learning (FL), such as devolving model training to intelligent devices and preserving data privacy, FL still faces the risk of the single point of failure and attack from malicious participants. Recently, blockchain is considered a promising solution that can transform FL training into a decentralized manner and improve security during training. However, traditional consensus mechanisms and architecture for blockchain can hardly handle the large-scale FL task due to the huge resource consumption, limited throughput, and high communication complexity. To this end, this paper proposes a two-layer blockchain-driven FL framework, called as ChainsFL, which is composed of multiple Raft-based shard networks (layer-l) and a Direct Acyclic Graph (DAG)-based main chain (layer-2) where layer-l limits the scale of each shard for a small range of information exchange, and layer-2 allows each shard to update and share the model in parallel and asynchronously. Furthermore, FL procedure in a blockchain manner is designed, and the refined DAG consensus mechanism to mitigate the effect of stale models is proposed. In order to provide a proof-of-concept implementation and evaluation, the shard blockchain base on Hyperledger Fabric is deployed on the self-made gateway as layer-l, and the self-developed DAG-based main chain is deployed on the personal computer as layer-2. The experimental results show that ChainsFL provides acceptable and sometimes better training efficiency and stronger robustness comparing with the typical existing FL systems.

With the rise of the Internet of Things (IoT), the number of mobile devices with sensing and computing capabilities increases dramatically, paving the way toward an emerging paradigm, i.e., crowdsensing that facilitates the interactions between humans and the surrounding physical world. Despite its superiority, particular attention is paid to be able to submit sensing data to the platform wherever possible to avoid leaking the sensitive information of participants and to incentivize them to improve sensing quality. In this article, we propose an incentive mechanism for participants, aiming to protect them from privacy leakage, ensure the availability of sensing data, and maximize the utilities of both platforms and participants by means of distributing different sensing tasks to different participants. More specifically, we formulate the interactions between platforms and participants as a multileader-multifollower Stackelberg game and derive the Stackelberg equilibrium (SE) of the game. Due to the difficulty to obtain the optimal strategy, a reinforcement learning algorithm, i.e., Q-learning is adopted to obtain the optimal sensing contributions of participants. In order to accelerate learning speed and reduce overestimation, a deep learning algorithm combined with Q-learning in a dueling network architecture, i.e., double deep Q network with dueling architecture (DDDQN) is proposed to obtain the optimal payment strategies of platforms. To evaluate the performance of our proposed mechanism, extensive simulations are conducted to show the superiority of our proposed mechanism compared with state-of-the-art approaches.

As a typical and prominent component of the Internet of Things, vehicular communication and the corresponding vehicular networks (VNETs) are promising to improve spectral efficiency, decrease transmission delay, and increase reliability. The ever-increasing number of vehicles and the demand of passengers/drivers for rich multimedium services bring key challenges to VNETs, which requiring huge capacity, ultralow delay, and ultrahigh reliability. To meet these performance requirements, a fog computing-based VNET is presented in this article, where the resource allocation as the corresponding key technique is researched. In particular, joint optimization of user association and radio resource allocation scheme is investigated to minimize the transmission delay of the concerned VNET. The proposed optimization problem is formulated as a mixed-integer nonlinear program and transformed into a convex problem by Perron–Frobenius theory and a weighted minimum mean square error method. Numerical results show that the proposed solution can significantly reduce the transmission delay with fast convergence.

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables the IoT devices of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the devices. In this article, we apply non-orthogonal multiple access (NOMA) technique to enable massive connectivity and investigate how it can be exploited to achieve energy-efficient MEC in IoT networks. In order to maximize the energy efficiency for offloading, while simultaneously satisfying the maximum tolerable delay constraints of IoT devices, a joint radio and computation resource allocation problem is formulated, which takes both intra- and inter-cell interference into consideration. To tackle this intractable mixed integer non-convex problem, we first decouple it into separated radio and computation resource allocation problems. Then, the radio resource allocation problem is further decomposed into a subchannel allocation problem and a power allocation problem, which can be solved by matching and sequential convex programming algorithms, respectively. Based on the obtained radio resource allocation solution, the computation resource allocation problem can be solved by utilizing the Knapsack method. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in IoT networks compared to the existing baselines.

To meet low latency and real-time monitoring demands of IoT-eHealth, fog computing is envisioned as a key technology to offer elastic computing resource at the edge of networks. In this context, eHealth devices can offload collected healthcare data or computational expensive tasks to a nearby fog server. However, the mobility of the eHealth devices may make the connection between them to fog servers uncertain, resulting in possible migration between fog servers. In order to evaluate the impact of this uncertainty on decision-making for offloading and resource allocation, we formulate the task offloading problem as a Multi-Stage Stochastic Programming (MSSP), with aim of minimizing the total latency of offloading to determine whether to offload or not, how much workload to offload, how much computing resource to allocate, as well as whether to migrate or not. Different from the previous MSSP based work focusing on the workload assignment only, the proposed MSSP examines joint decisions of offloading, resource allocation, and migration, advancing the understanding of the interactions among these decisions. Furthermore, to reduce the computational complexity of MSSP, we design an efficient sub-optimal offloading policy based on Sample Average Approximation, called SAA-MSSP. We conduct extensive simulation experiments to validate the effectiveness of SAA-MSSP. The results show that SAA-MSSP can converge to a near-optimal solution quickly.

The combination of artificial intelligence and mobile edge computing (MEC) is considered as a promising evolution path of the future wireless networks. As a model-level coordination learning paradigm, federated learning can make full use of the distributed computation resource in the MEC systems, which allows the users to keep their private data locally. However, due to the unreliable wireless transmission circumstances and resource constraints in the MEC systems, both the performance and training efficiency of federated learning cannot be guaranteed. To solve this problem, the optimization design of federated learning in the MEC systems is studied in this paper. First, an optimization problem is formulated to manage the tradeoff between model accuracy and training cost. Second, a joint optimization algorithm is designed to optimize the model compression, sample selection, and user selection strategies, which can approach a stationary optimal solution in a computationally efficient way. Finally, the performance of our proposed optimization scheme is evaluated by numerical simulation and experiment results, which show that both the accuracy loss and the cost of federated learning in the MEC systems can be reduced significantly by employing our proposed algorithm.

Fog computing is seen as a key enabler to meet the stringent requirements of industrial Internet of Things (IIoT). Specifically, lower latency and IIoT devices’ energy consumption can be achieved by offloading computation-intensive tasks to fog access points (F-APs). However, traditional computation offloading optimization methods often possess high complexity, making them inapplicable in practical IIoT. To overcome this issue, this article proposes a deep reinforcement learning (DRL) based approach to minimize long-term system energy consumption in a computation offloading scenario with multiple IIoT devices and multiple F-APs. The proposal features a multi-agent setting to deal with the curse of dimensionality of the action space by creating a DRL model for each IIoT device, which identifies its serving F-AP based on network and device states. After F-AP selection is finished, a low complexity greedy algorithm is executed at each F-AP under a computation capability constraint to determine which offloading requests are further forwarded to the cloud. By conducting offline training in the cloud and then making decisions online, iterative online optimization procedures are avoided and, hence, F-APs can quickly adjust F-AP selection for each device with trained DRL models. Via simulation, the impact of batch size on system performance is demonstrated and the proposed DRL-based approach shows competitive performance compared to various baselines including exhaustive search and genetic algorithm based approaches. In addition, the generalization capability of the proposal is verified as well.

In this paper, we examine the potential benefits of non-orthogonal multiple access (NOMA) in achieving energy-efficient mobile edge computing (MEC) in wireless networks. To this end, we consider an uplink communication system where the edge users (EUEs) adopt NOMA protocol to offload their own tasks to the edge access points in the presence of cellular users (CUEs) performing regular uplink transmissions. We first characterize the energy consumption of our considered system. Then, taking the delay constraints of the CUEs and EUEs into consideration, we show how the energy consumption of the system can be optimized by judiciously determining the task offloading allocation, the subchannel allocation, as well as the power allocation. In order to solve the non-convex problem, an iterative Stackelberg-game-based scheme is proposed, in which the EUEs perform the task and power allocation as leaders, while the CUEs perform the subchannel allocation as followers. Numerical results show that, compared to exiting solutions, our proposed NOMA-based scheme can significantly reduce the energy consumption of the system, and the performance improvement becomes more profound when the delay constraints of the CUEs and EUEs become stringent.

Keeping patients' sensitive information secured and untampered in the e-Health system is of paramount importance. Emerging as a promising technology to build a secure and reliable distributed ledger, blockchain can protect data from being falsified, which has attracted much attention from both academia and industry. However, with limited computational resources, medical IoT devices do not have efficient ability to fulfill the functionalities as a full node in wireless blockchain network (WBN). Facing this dilemma, Mobile Edge Computing (MEC) brings us dawn and hope through offloading the high resource demanding blockchain functionalities at the IoT devices to the MEC. However, aiming to maximize the mining profit, most of existing offloading strategies have ignored the other needs of wireless devices, e.g., faster transaction writing. In this paper, according to different needs, blockchain nodes are firstly divided into two categories. One is blockchain users whose needs are faster transaction uploading, the other is blockchain miners whose goals are maximum revenue. Then, to maximize both the utilities of blockchain users and blockchain miners, a Stackelberg game is introduced to formulate the interaction between them. From the simulation results, this game is proved to converge to a unique optimal equilibrium.

Mobile edge computing (MEC) enables the users of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the users. In this paper, we investigate how non-orthogonal multiple access (NOMA) techniques can be exploited to achieve energy-efficient MEC in multi-cell networks. To this end, we first characterize the energy efficiency of the considered system, taking into account the impact of both intra- and inter-cell interference in multi-cell networks. We then jointly optimize the subchannel allocation, power allocation, and the computation resource allocation to maximize the energy efficiency of NOMA-enabled MEC, while simultaneously satisfying the maximum tolerable delay constraints of the users. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in multi-cell networks compared to the existing baselines.

To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks. As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN). By providing computational and storage resources at the edge, MEC can reduce latency for end users. Hence, this article investigates MEC for 5G and IoT comprehensively. It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading. In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT. Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT. Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios.

The Internet of Things has such a profound impact that we have witnessed crowdsensing has emerged as the most popular sensing paradigm where participants sense and aggregate data to the platform by smart devices. However, the participants may not be willing to involve in data sensing and aggregation if they are not sufficiently compensated or their personalized private information are disclosed. In order to overcome the above issues, this article proposes a payment-privacy protection level (PPL) game, where each participant submits his sensing data with a specified PPL while the platform chooses a corresponding payment to the participant. Additionally, we derive the Nash equilibrium point of the game. Considering that the payment-PPL model is unknown in practice, we employ a reinforcement learning technique, i.e., Q-learning to obtain the payment-PPL strategy in a dynamic payment-PPL game. We further use the deep Q network (DQN), which combines a deep-learning technique with Q-learning to accelerate the learning speed. Through extensive simulations, we verify that our proposed algorithm using DQN achieves superior performance in terms of utilities of both platform and participants and data aggregation accuracy compared with the one using Q-learning.

Molecular communication (MC), in which molecules serve as the carrier for data transmission, plays an essential role in nanonetworks. In this article, a mobile diffusive-drift MC model is investigated, which consists of a mobile transmit nanomachine (TN) and a mobile receive nanomachine (RN). The depleted molecule shift keying (D-MoSK) modulation is utilized in this model to perform end-to-end communication. To explore the performance of D-MoSK, we derive the closed-form expressions of symbol error rate (SER) as well as the channel capacity, and then we give out the numerical results. It is observed from the numerical results that, if compared with the molecule shift keying modulation, the D-MoSK modulation can exhibit better performances in terms of SER, channel capacity, and complexity under the employed model. Also, the impacts of several crucial parameters on the performance are evaluated and discussed comprehensively. The obtained results are expected to provide guidance significance for the design of a practical mobile diffusive-drift MC system.

Molecular communication (MC) is a promising technique of using molecules to realize communication between nanomachines for Internet of Bio-Nano Things in the body area nanonetwork. Due to the properties of diffusion and the attenuation of molecular transmission, the diffusion-based MC confronts with challenges in terms of the communication range and the signal detection accuracy. To extend the coverage, the intermediate nanomachine is deployed as relay between transmitter and its intended receiver. In this article, amplify-and-forward (AF) relaying is researched, and the performance under diverse signal detection schemes is analyzed, including mean square error (MSE) detection, maximum a posteriori probability detection, minimum error probability (MEP) detection under stationary fluid environment, and the MEP detection with a drift velocity simulation. The key parameters, such as the number of released molecules, receiving radius, and the relay position, influencing on the AF relaying performance under different detection methods are explored. The simulation results show that the MEP detection can achieve the best performance gain for the AF relay with a drift velocity channel. In particular, when the number of released molecules is 500, the gain is up to 35 dB.

The rapid development of the Internet of Things with the requirements of ultrareliability and ultralow latency has imposed huge challenges on the radio access network operation and maintenance. Using artificial intelligence technologies can provide the accurate fault diagnosis rapidly and efficiently, but it is usually hampered by the lack of historical data as well as the certified fault labels. To deal with these challenges, in this article, an unsupervised deep transfer learning-based fault diagnosis method in fog radio access networks is proposed. Specifically, a transfer learning-based density-based spatial clustering of applications with noise method is first utilized to detect and label fault data in each interval by using the core-level information. Then, an unsupervised deep transfer learning method combining a convolutional neural network with a domain adversarial neural network is applied to classify the categories of unlabeled fault data by using cell-level information. The experimental results show that the proposed method can reduce the missed detection rate than the traditional method, and has better fault diagnosis accuracy than the reference methods.

The joint user association and cache placement problem is challenging in fog radio access networks (F-RANs) due to its difficulty to present the optimal solution with low complexity. Motivated by the recent development of artificial intelligence, we divide the original optimization problem into two subproblems. In particular, the user association problem is solved by a reinforcement-learning-based algorithm in which the enhanced fog access point content placement profiles and the fronthaul constraint are considered. On the other hand, since the popularity profile of the contents is hard to acquire in practice, a stacked autoencoder-based scheme is presented to predict the content popularity, which considers both the local and global user request status within a specified time interval. Based on the popularity prediction, the edge content placement problem is solved by a deep-reinforcement-learning-based algorithm, aiming at maximizing the F-RAN network payoff. Moreover, the complicated interactions and the cyclic dependency among the short time-scale user association and the long time-scale content popularity prediction and placement problems are studied by applying the Stackelberg game theory. The simulation validates the accuracy of the analytical results and proves that the proposal can further improve the performance of F-RANs.

Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle (V2V) links, and high signaling overhead of centralized resource allocation approaches become bottlenecks. In this article, we investigate a joint optimization problem of transmission mode selection and resource allocation for cellular V2X communications. In particular, the problem is formulated as a Markov decision process, and a deep reinforcement learning (DRL)-based decentralized algorithm is proposed to maximize the sum capacity of vehicle-to-infrastructure users while meeting the latency and reliability requirements of V2V pairs. Moreover, considering training limitation of local DRL models, a two-timescale federated DRL algorithm is developed to help obtain robust models. Wherein, the graph theory-based vehicle clustering algorithm is executed on a large timescale and in turn, the federated learning algorithm is conducted on a small timescale. The simulation results show that the proposed DRL-based algorithm outperforms other decentralized baselines, and validate the superiority of the two-timescale federated DRL algorithm for newly activated V2V pairs.

As a novel sensing paradigm, crowdsensing has gained great attention due to large-scale user participation, low cost and wide data source, replacing traditional sensor based sensing in intelligent transportation, environmental monitoring, urban public management, etc. In crowdsensing, however, user privacy leakage is a common but fatal problem, where the participants in crowdsensing might not provide their data if their sensing data expose their personal private information or even lead to malicious attacks. Additionally, it is still challenging for the platform to consider the randomness of sensing task arrival, the dynamic participation of participants and the complexity of task allocation. To this end, an online control mechanism is presented to maximize the profit of platform while guaranteeing system stability and providing personalized location privacy protection. By exploiting Lyapunov optimization theory, we transform the optimization problem into a queue stability problem, decomposing it into three subproblems further. Through rigorous theoretical analysis, we prove that our time-averaged profit is approximately optimal. We also carry out extensive simulations to verify the superiority of our proposed mechanism.

Multi-access edge computing (MEC) can enhance the computing capability of mobile devices, while non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two strategies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task delay minimization in multi-user NOMA-MEC networks, where multiple users can offload their tasks simultaneously through the same frequency band. We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts. We aim to minimize the task delay among users by optimizing their tasks partition ratios and offloading transmit power. The delay minimization problem is first formulated, and it is shown that it is a nonconvex one. By carefully investigating its structure, we transform the original problem into an equivalent quasi-convex. In this way, a bisection search iterative algorithm is proposed in order to achieve the minimum task delay. To reduce the complexity of the proposed algorithm and evaluate its optimality, we further derive closed-form expressions for the optimal task partition ratio and offloading power for the case of two-user NOMA-MEC networks. Simulations demonstrate the convergence and optimality of the proposed algorithm and the effectiveness of the closed-form analysis.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology for the next-generation wireless communication system. Introducing NOMA into the fog radio access networks (F-RANs) is able to provide simultaneous transmissions to multiple users and significantly enhance F-RAN performance. However, due to the increasing number of users and the constraint of caching storage capacity, there exists a tradeoff between NOMA transmission performance and fronthaul saving. In this paper, a hierarchical game framework is presented to solve the joint optimization problem of user access mode selection and content popularity prediction in NOMA based F-RANs. More specifically, the access mode selection problem is formulated as an evolutionary game. The proposals' evolutionary payoff expressions are derived by stochastic geometry tool, and the cost functions are related to the fog access point (F-AP) content placement profile as well as the fronthaul constraint. Moreover, the problem of what contents the F-AP should cache is modeled as a content popularity prediction problem, and based on both local and global user request states, a machine learning algorithm is presented to solve it. Simulation results validate the accuracy of analytical results and demonstrate our proposed algorithms can further improve the performance of NOMA based F-RANs.

The impact of communication transmission delay on the original blockchain, has not been well considered and studied since it is primarily designed in stable wired communication environment with high communication capacity. However, in a wireless scenario, due to the scarcity of spectrum resource, a blockchain user may have to compete for wireless channel to broadcast transactions following media access control (MAC) mechanism. As a result, the communication transmission delay may be significant and pose a bottleneck on the blockchain system performance and security. To facilitate blockchain applications in wireless industrial Internet of Things (IIoTs), this article aims to investigate whether the widely used MAC mechanism, carrier sense multiple access/collision avoidance (CSMA/CA), is suitable for wireless blockchain networks or not. Based on tangle, as an example to analyze the system performance in term of confirmation delay, transaction per second and transaction loss probability by considering the impact of queueing and transmission delay caused by CSMA/CA. Next, a stochastic model is proposed to analyze the security issue taking into account the malicious double-spending attack. Simulation results provide valuable insights when running blockchain in wireless network, the performance would be limited by the traditional CSMA/CA protocol. Meanwhile, we demonstrate that the probability of launching a successful double-spending attack would be affected by CSMA/CA as well.

Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance the spectral efficiency and support massive connections in fog radio access networks (F-RANs). In this paper, with the aim of maximizing the weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. To solve this problem, we first propose the optimal resource allocation scheme. Specifically, the monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the global optimal solution. In order to reduce the computational complexity, we then propose the suboptimal resource allocation scheme. In particular, the original problem is decomposed into separated RB and power allocation problems. The RB allocation problem is modeled as a many-to-one matching game and a modified swap-enabled matching algorithm is proposed. The power allocation problem is converted into a convex form through some approximations and solved by a successive convex approximation algorithm. Simulation results demonstrate that the suboptimal scheme can achieve almost the same performance as the optimal scheme, while requiring much less computational complexity. In addition, the superiority of NOMA-enabled F-RANs over the conventional OMA-enabled F-RANs is verified.

To meet the wide range of 5G use cases in a cost-efficient way, network slicing has been advocated as a key enabler. Unlike the core network slicing in a virtualized environment, radio access network (RAN) slicing is still in its infancy and the corresponding realization is challenging. In this paper, we investigate the realization approach of fog RAN slicing, where two network slice instances for hotspot and vehicle-to-infrastructure scenarios are concerned and orchestrated. In particular, the framework for RAN slicing is formulated as an optimization problem of jointly tackling content caching and mode selection, in which the time-varying channel and unknown content popularity distribution are characterized. Due to the different users' demands and the limited resources, the complexity of original optimization problem is significant high, which makes traditional optimization approaches hard to be directly applied. To deal with this dilemma, a deep reinforcement learning algorithm is proposed, whose core idea is that the cloud server makes proper decisions on the content caching and mode selection to maximize the reward performance under the dynamical channel state and cache status. The simulation results demonstrate the performance in terms of hit ratio and sum transmit rate can be significantly improved by the proposal.

Wireless content caching has recently been considered as an efficient way in fog radio access networks (F-RANs) to alleviate the heavy burden on capacity-limited fronthaul links and reduce delivery latency. In this paper, an advanced minimal delay association policy is proposed to minimize latency while guaranteeing spectral efficiency in F-RANs. By utilizing stochastic geometry and queueing theory, closed-form expressions of successful delivery probability, average ergodic rate, and average delivery latency are derived, where both the traditional association policy based on accessing the base station with maximal received power and the proposed minimal delay association policy are concerned. Impacts of key operating parameters on the aforementioned performance metrics are exploited. It is shown that the proposed association policy has a better delivery latency than the traditional association policy. Increasing the cache size of fog-computing based access points (F-APs) can more significantly reduce average delivery latency, compared with increasing the density of F-APs. Meanwhile, the latter comes at the expense of decreasing average ergodic rate. This implies the deployment of large cache size at F-APs rather than high density of F-APs can promote performance effectively in F-RANs.

Mobile edge computing (MEC) can provide considerable computing capabilities for Internet of Things (IoT) devices, especially for applications with latency sensitive tasks. By applying non-orthogonal multiple access (NOMA) in MEC, multiple users can offload their tasks simultaneously on the same frequency band. In this paper, the minimization problem of task completion time is investigated for the NOMA enabled multi-user MEC networks. We adopt \emph{partial offloading}, in which each user's task can be partitioned, while the formulated problem is quasi-convex. Thus a bisection search (BSS) algorithm is proposed to achieve the minimum task completion time for the multi- user case. To reduce the complexity and evaluate the optimality of the BSS algorithm, we further derive closed- form expressions for the optimal task partition ratio and offloading power for a two-user NOMA-MEC network. Simulations demonstrate the convergence and optimality of the proposed BSS algorithm and the effectiveness of the optimal approach.

The emerging demands of the immersive virtual reality (VR) experience require current and future wireless networks to provide ultra-low end-to-end latency. Against this backdrop, fog radio access networks (F- RANs), which take full advantages of both fog computing and caching technologies, are anticipated as a promising solution for meeting the stringent latency requirement of mobile VR delivery. In this paper, we propose a mobile VR delivery framework, in which certain VR videos and computing tasks are cached at and offloaded to the edge of F-RANs, respectively. In the considered framework, we jointly optimize the bandwidth, caching, and computing resource allocation in order to minimize the average latency. To this end, we first derive the closed-form expression of the average latency. Based on which, we then analytically examine the optimal resource allocation decision. Moreover, through the numerical results, we reveal the non-trivial trade-offs among communication, caching, and computation, showing how the bandwidth, caching, and computing capabilities can have significant impacts on the average latency.

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated to minimize the average download delay. Considering the non-linear and coupled multi-variable nature of the original optimizing problem, we transform it into an equivalent integer linear programming problem with decoupled variables. Then, we decomposed the transformed problem into two subproblems which can be solved separately by each fog access point (F-AP). Considering the non-deterministic polynomial hard (NP-hard) nature of the two decomposed subproblems, we propose an improved pigeon inspired optimization (PIO) based cooperative edge caching scheme, which utilizes Cauchy perturbation and self-adaptive factor to avoid pre-mature convergence and achieve a better search performance, respectively. Our proposed scheme not only allows F-APs to make cache decisions with low computational complexity, but also has very low message passing overhead. Simulation results show that our proposed scheme can greatly decrease the average download delay.

In order to achieve efficient communication in the fifth generation (5G) networks, non-orthogonal multiple access (NOMA) technique has been utilized in fog radio access networks (F-RANs). In this paper, we investigate the resource allocation problem in a NOMA-enabled downlink F-RAN. To maximize the weighted sum rate of NOMA users served by fog-computing-based access points (F-APs), the resource block (RB) allocation and power allocation are optimized. Specifically, we decouple the problem into RB allocation and power allocation problems. The former is modeled as a many-to-one matching game and we propose a modified swap-enabled matching algorithm to solve it, which takes interference threshold into consideration. The later is a non-convex problem, we transform it into a tractable one via some approximations and get the closed-form expressions of power allocation coefficients. Finally, we combine the both to propose a joint resource allocation algorithm, which is preformed iteratively to obtain the optimal result. Simulation results are provided to show the performance of the algorithm.

In fog-radio access networks (F-RANs), the loadings of backhaul is the bottleneck to fully explore the potential of cloud computing capability, which provide abundant computation resources to execute the computation tasks. In this paper, the performance of computation offloading F-RANs is studied to keep a balance between the tradeoff between the costs and the gains of different computation task processing modes. First, we focus on an opportunistic computation offloading strategy in F-RANs, and the computation offloading probability is analyzed based on a stochastic geometry-based model. Second, the computation offloading procedure in F-RANs can be modeled as a Jackson network of queueing system. A closed-form expression of average delay performance is derived, and the global optimal solution of the ratio of computation tasks handled by the cloud computing center is also provided to minimize the average processing delay. Finally, the simulation results are shown to verify the accuracy of analytical results and evaluate the performance gains of hybrid computation offloading in F-RANs.

The problem of proximity detection is often encountered in autonomous driving and traffic safety related applications, which require low-latency proximity detection with relatively low communication cost. However, (i) most existing proximity detection solutions focus on the Euclidean space which cannot be used in road network space, and (ii) the solutions for road networks focus on static road networks and thus cannot be applied in time-aware road networks. Motivated by these, we first design a low-latency proximity detection architecture based on Mobile Edge Computing (MEC) to achieve low communication latency, and then propose a proximity detection method including a client-side algorithm and a server-side algorithm, aiming at reducing the communication cost. Experimental results show that our MEC based proximity detection architecture and our proximity detection method can reduce the communication latency and the communication cost effectively.

Featuring edge caching and computing, fog radio access networks have been seen as promising architectures. However, the joint optimization of cache and radio resource can put heavy burden on the resource manager in the cloud. To overcome this issue, hierarchical radio and cache resource management is studied in this paper. The core idea is to fully utilize the resource management capabilities of fog access points (FAPs) to divide time-domain resource on a small timescale by participating a coalitional game while make the resource manager allocate cache resource on a large timescale to maximize a long-term utility. Based on defined FAP preference, a low-complexity and distributed coalition formation algorithm is first developed under per-FAP fronthaul capacity constraints. Then, facing the challenges incurred by no closed form, discrete variables and the curse of dimensionality, multi-agent reinforcement learning (MARL) based caching is proposed for the resource manager. In the proposal, multiple agents are created, one for each content-FAP pair, who jointly learn a caching strategy via the interaction with a history network environment. By simulation, the effectiveness of the MARL based caching is demonstrated.

Molecular communication (MC) is a new communication engineering paradigm where molecules are employed as information carriers. MC via diffusion is the most promising approach for the communication between nanomachines. Intersymbol interference (ISI) caused by the Brownian motion of diffusion molecules will seriously affected the reliability of communication. Meanwhile, with the increase of communication distance, the signal attenuation is serious, which leads to the decline of communication quality. In this paper, a decode-and-forward (DF) relay in diffusion-based molecular communication systems is proposed to improve communication quality, in which the depleted molecule shift keying (D-MoSK) coding is used to reduce ISI. The channel performance including the BER and capacity is analyzed. Meanwhile, the relationship among BER, capacity, and the key parameters, including the number of the released molecules, receiving radius, and relay position, is investigated. The simulation experiments show that the proposal D-MoSK coding can improve the communication reliability significantly, in which the performance gain can be maximized through optimizing the position of relay and the receiving radius.

Resource allocation in fog radio access networks (F- RANs) is a hot topic but with challenges, especially combined with non-orthogonal multiple access (NOMA) technique which is promised to enhance spectral efficiency (SE). In NOMA-based F-RANs, radio subchannels occupied by remote radio heads (RRHs) can be reused by fog access points (F-APs) and power domain NOMA technique is applied for the link from F-APs to fog user equipments (FUEs). In this paper, an energy efficiency (EE) maximization problem consisting of subchannel reuse assignment and NOMA-based power allocation is formulated for NOMA-based F-RANs. To solve it efficiently, the problem is modeled as a Stackelberg game and two game-theoretic algorithms are developed for two sub-problems, respectively. In particular, The subchannel reuse assignment is modeled as a one-to-many matching game and the power allocation is modeled as a non-cooperative game. The low complexity of proposed algorithms make the problem tractable and fast to converge to the Nash equilibrium (NE). Simulation results show that the proposed algorithms achieve the significant performance gains on system EE, users' latency with an accurate and low complexity way compared to other baselines.

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance spectral efïciency (SE) and support massive connections in fog radio access networks (F-RANs). In this paper, to maximize weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. Based on the suboptimal scheme with low computational complexity proposed in last work, the optimal algorithm is proposed to provide an upper bound of the system performance. In particular, monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the globally optimal solution. Simulation results demonstrate the performance of proposed algorithms and verify the superiority of NOMA-enabled F-RANs over OMA scenarios.

An optimization problem on the joint content caching and mode selection for slice instances in fog radio access networks (F-RANs) is researched in this paper, characterizing the unknown content popularity distribution and time-varying channel assumptions. In particular, hotspot and vehicle-to-infrastructure scenarios are considered and corresponding network slice instances are orchestrated in F-RANs. Considering different users' demands and limited resources, there exists a significant high complexity in solving the original optimization problem with traditional optimization approaches. Motivated by the advantages of deep reinforcement learning in solving sophisticated network optimizations, a deep reinforcement learning based algorithm is proposed, wherein the cloud server takes intelligent actions to maximize the hit ratio and sum transmit rate. The performances of the proposed algorithm are demonstrated to be significantly improved.

With dynamic renewable energy generation and power demand, microgrids (MGs) exchange energy with each other to reduce their dependence on power plants. In this article, we present a reinforcement learning (RL)-based MG energy trading scheme to choose the electric energy trading policy according to the predicted future renewable energy generation, the estimated future power demand, and the MG battery level. This scheme designs a deep RL-based energy trading algorithm to address the supply-demand mismatch problem for a smart grid with a large number of MGs without relying on the renewable energy generation and power demand models of other MGs. A performance bound on the MG utility and dependence on the power plant is provided. Simulation results based on a smart grid with three MGs using wind speed data from Hong Kong Observation and electricity prices from ISO New England show that this scheme significantly reduces the average power plant schedule and thus increases the MG utility in comparison with a benchmark methodology.

The edge cache is an effective way to reduce the heavy traffic load and the end-to-end latency in radio access networks (RANs) for supporting a number of critical Internet of Things (IoT) services and applications. It has been verified to provide high spectral efficiency (SE), high energy efficiency (EE), and low latency. Along with several key techniques that have been applied, such as device-to-device communication and predictive caching, the edge cache techniques in RANs for IoT are becoming diversified. This paper comprehensively surveys the recent advances of the edge cache in RANs, including the key techniques and the corresponding performances. In particular, the key techniques are presented from the viewpoints of the deployment location of edge caches, content placement strategy, and coded caching. An advanced hierarchical edge cache structure is presented, and the main impacts on SE, EE, and latency of the key techniques are mainly summarized. Several open issues and challenges are identified as well to spur future investigations, in which the joint optimization of radio and cache resources, the edge cache with mobile edge computing and network intelligence, privacy, and security are discussed.

Fog radio access networks (F-RANs) are seen as potential architectures to support services of Internet of Things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL)-based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.

The growing popularity of Internet of Things (IoT) with the requirements of highly reliable and low latency has imposed huge challenges to current cellular networks. Using small aerial platforms like unmanned aerial vehicles (UAVs) to assist terrestrial base stations (BSs) is attractive, but it often challenged by the lack of UAV access selection and resource allocation algorithm to balance the network performance and service cost. In this paper, we study the UAV access selection and BS bandwidth allocation problems in a UAV assisted IoT communication network, where a hierarchical game framework is presented. The complicated interactions among UAVs and BSs as well as the cyclic dependency is studied by applying the Stackelberg game theory. Wherein, the access competition among groups of UAVs is formulated as a dynamic evolutionary game and solved by an evolutionary equilibrium. On the other hand, the problem of how much bandwidth should BSs allocate to the UAVs is modeled as a noncooperative game, where the existence and the uniqueness of Nash equilibrium is analyzed. Stochastic geometry tool is used to model the position distribution of network nodes and drive the payoff expressions by taking into account different network parameters. The analytical results for the proposed hierarchical game model and the corresponding solutions are evaluated via simulations, which verify both the validity of our analysis and the effectiveness of the proposed algorithms.

Non-orthogonal multiple access (NOMA) has been recently considered in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. In addition, simultaneous wireless information and power transfer (SWIPT) is a promising solution to maximize the energy efficiency. In this paper, for the first time, we investigate the integration of SWIPT in mmWave massive MIMO-NOMA systems. As mmWave massive MIMO will likely use hybrid precoding (HP) to significantly reduce the number of required radio-frequency (RF) chains without an obvious performance loss, where the fully digital precoder is decomposed into a high-dimensional analog precoder and a low-dimensional digital precoder, we propose to apply SWIPT in HP-based MIMO-NOMA systems, where each user can extract both information and energy from the received RF signals by using a power splitting receiver. Specifically, the cluster-head selection algorithm is proposed to select one user for each beam at first, and then the analog precoding is designed according to the selected cluster heads for all beams. After that, user grouping is performed based on the correlation of users' equivalent channels. Then, the digital precoding is designed by selecting users with the strongest equivalent channel gain in each beam. Finally, the achievable sum rate is maximized by jointly optimizing power allocation for mmWave massive MIMO-NOMA and power splitting factors for SWIPT, and an iterative optimization algorithm is developed to solve the non-convex problem. Simulation results show that the proposed HP-based MIMO-NOMA with SWIPT can achieve higher spectrum and energy efficiency compared with HP-based MIMO-OMA with SWIPT.

The emerging virtual reality (VR) experience demands ultra-high-transmission-rate and ultra-low-latency deliveries, which is challenging for the current cellular networks. Since fog radio access networks (F-RANs) take full advantages of both edge fog computing and caching technologies and benefit different quality-of-service requirements, it is anticipated that high-quality VR experience could be well addressed in F-RANs. This paper presents an F-RAN-based mobile VR delivery framework, in which the core idea is to cache parts of the VR videos in advance and run a certain processing procedure at the edge of F-RANs. To optimize resource allocation at both mobile VR devices and fog access points (F-APs), a joint radio communication, caching and computing decision problem is formulated to maximize the average tolerant delay with meeting a given transmission rate constraint. This problem is formulated as a multiple choice multiple dimensional knapsack problem and solved with the Lagrangian dual decomposition approach. Furthermore, the optimal joint caching and computing decision is analyzed in a specific case with a closed-form expression of the average tolerant delay. The communications-caching-computing tradeoff at both mobile VR devices and F-APs is revealed, and the numerical results demonstrate that local caching and computing capabilities have significant impacts on the average tolerant delay. The proposed mobile VR delivery framework is promising in improving spectral efficiency by maximizing average tolerant delay while meeting high transmission rate requirements.

The inherent broadcast characteristics of the visible light communication (VLC) channel makes VLC downlinks susceptible to unauthorized terminals in many actual VLC scenarios, such as offices and shopping centers. This paper considers a multiple-input-single-output (MISO) VLC scenario with multiple light fixtures acting as the transmitter, a VLC receiver as the legitimate user, and an eavesdropper attempting to intercept the undisclosed information. To improve the confidentiality of VLC links, a physical-layer anti-eavesdropping framework is proposed to obscure the unauthorized eavesdroppers and diminishes their capability of inferring the information through smart beamforming over the MISO VLC wiretap channel. To cope with the intractable problem of finding the theoretically optimal solution of the secrecy rate and utility for the MISO VLC wiretapping channel, a reinforcement learning (RL)-based VLC beamforming control scheme is proposed to achieve the optimal beamforming policy against the eavesdropper. Furthermore, a deep RL-based VLC beamforming control scheme is proposed to handle the curse of dimensionality for both observation space and action space and avoid the quantization error of the RL-based algorithm. Simulation results show that the proposed learning-based VLC beamforming control schemes can significantly decrease the bit error rate of the legitimate receiver and increase the secrecy rate and utility of the anti-eavesdropping MISO VLC system, compared with the benchmark strategy.

Non-orthogonal multiple access (NOMA) is promising to further improve spectral efficiency (SE) and decrease transmit latency in fog radio access networks (F-RANs) through serving multi-users in the same frequency-time resource block simultaneously, while the complexity of user association is challenging to exploit the corresponding performance gains. In this paper, a performance analysis framework for the user association in NOMA based F-RANs is proposed and the closed-form analytical results are developed by using stochastic geometry tool. In particular, we propose two user association algorithms based on evolutionary game and reinforcement learning, respectively. The performance model jointly considering quality of service, delay cost, and power consumption is formulated as a payoff function, and the corresponding performance expressions are derived for these two user association algorithms. Numerical and simulation results demonstrate that the derived expressions are accurate, and the NOMA based F-RAN can provide over 50% performance gains on SE compared to the orthogonal multiple access scheme. Furthermore, these two proposed user association algorithms work well with high convergence, which can effectively enhance the overall performance and the fairness of users.

In this paper, user association and power allocation are investigated in a non-orthogonal multiple access (NOMA)-based multi-cell network. In order to perform successive interference cancellation (SIC) techniques for removing the intra-base station (BS) interference, the optimal decoding order is derived for all users associated with the same BS. In an effort to improve the system, a sum rate maximization problem is formulated by jointly designing user association and power allocation. Two game theory based algorithms are proposed to obtain the stable user structure by dividing users into different BSs' clusters, where the sub-optimal and global optimal solutions can be achieved. The properties of the proposed algorithms, including complexity, convergence, stability and optimality, are analyzed. Based on the quality-of-service (QoS) constraint, the closed-from solutions for power allocation are derived, and thus the expressions for the sum rate of all users in each cluster is obtained. Moreover, the case that the QoS threshold cannot be achieved by all users in each cluster is considered. Simulation results demonstrate that: i) the proposed user association algorithms and the closed-form solutions for power allocation can significantly enhance the sum rate and outage probability; and ii) the proposed NOMA-based system is capable of achieving promising gains over the conventional orthogonal multiple access (OMA)-based framework in the multi-cell scenario.

The densification deployment of small cells emerging into 5G cellular networks can achieve high capacity, but is faced with the challenge of how to manage energy consumption and inter-cell interference well in time-varying channels. In this paper, we propose a reinforcement learning based downlink power control algorithm to manage interference for the ultra-dense small cell networks. More specifically, base stations of the small cells use Q-learning to select the downlink transmit powers. A transfer learning method called hotbooting is applied to further accelerate the learning speed and save the energy consumption based on the estimated user density without being aware of the network and channel model of the other small cells. Simulation results demonstrate this scheme significantly improves the network throughput and saves the energy consumption compared with the benchmark, a data-driven based transmission power adaptation scheme.

Adversary unmanned aerial vehicles (UAVs) seriously threaten public security and user privacy. In this paper, we propose a reinforcement learning (RL) based defense framework to address malicious UAVs close to a target estate such as a company or an institute. This framework uses Q-learning to choose the defense policy such as jamming the global positioning system signals (GPS) and hacking, and laser shooting. According to the defense history and the current security status of the target estate, this scheme can improve the UAV defense performance in the dynamic game without being aware of the UAV attack policy and environment model in the area of interests. Simulation results show that this scheme can reduce the risk rate of the estate and improve the utility compared with the benchmark scheme against malicious UAVs.

Achieving large confidential capacity under the wiretap channel model is a challenge due to the narrow modulation bandwidth and total transmission power constraints. The confidential capacity of a system can be improved through a non‐orthogonal multiple access technique that can obtain the highest transmission power in a downlink network. A clustering method is applied to network users who require data with similar contents. Based on the channel gain of each user, cluster heads are selected as agents for the corresponding clusters; then, the total transmission power is shared among the cluster heads. Before the power allocation process, the signal‐to‐interference‐plus‐noise ratio of the cluster heads is derived by considering clipping noise to ensure fairness. On this basis, an optimal power allocation scheme is proposed using Lagrangian dual theory. A case is presented to validate the performance of the proposed power allocation scheme. The comparison of the numerical results with those of other schemes shows that the proposed method achieves better performance regarding both secrecy sum capacity and outage probability.

To support the typical application scenarios defined in the fifth generation wireless network, such as the enhanced mobile broadband, massive machine type of communication, ultra-reliable and low-latency communication, virtual Mobility Management Entity (vMME) is a promising solution, which runs on universal servers and network functions virtualization instead of conventional hardware-dedicated mobility management entity. Among different vMME mapping solutions, the decomposing of MME into multiple components is a prospective approach to implementing distributed and virtualized mobility management. In this paper, the optimization of mobility management is addressed by using NFV and service function chain. A general signaling processing flow of vMME based on service function chain is analyzed. The performance of vMME is formulated considering the total signaling communication overhead cost, the total signaling communication overhead cost on backhauls as well as the migration overhead cost of state data under different function component placement of vMME. Since this optimization problem is NP-hard and the computation complexity is O(nk), a heuristic approach consisting of Min-TSCOC, MinTSCOCB, and Min-MOCSD are presented, which aims to obtain the optimal solutions to the optimization problems by using genetic algorithm. The simulation results show that it is beneficial to decompose the function of mobility management, and the performance gains from vMME for different network function composition strictly depend on four mobility events.

Taking full advantage of centralized cooperation and edge caching, fog radio access network has been considered as a promising paradigm to provide high spectral efficiency and energy efficiency. However, the overheads on fronthaul transmission and content caching also significantly affect the affordability, which makes it an increasingly urgent problem to achieve the performance improvement with reasonable overheads. In this paper, the economical energy efficiency (E3) metric is adopted to comprehensively consider the impacts on different aspects. Under the constraints of maximum transmitting power, cache status, and fronthaul capacity, a resource allocation problem is formulated, in which throughput, energy consumption, as well as cost on fronthaul transmission and content caching are jointly considered. The problem is solved using fractional programming, weighted minimum mean square error approach, and greedy algorithm, and an adaptive transmitting method selection algorithm is proposed. The simulation results demonstrate the effectiveness and gains of the proposed algorithm, and the corresponding key factors impacting on E3are accordingly analyzed and evaluated.

This paper provides a comprehensive survey of the impact of the emerging communication technique, non-orthogonal multiple access (NOMA), on future wireless networks. Particularly, how the NOMA principle affects the design of the generation multiple access techniques is introduced first. Then the applications of NOMA to other advanced communication techniques, such as wireless caching, multiple-input multiple-output techniques, millimeter-wave communications, and cooperative relaying, are discussed. The impact of NOMA on communication systems beyond cellular networks is also illustrated, through the examples of digital TV, satellite communications, vehicular networks, and visible light communications. Finally, the study is concluded with a discussion of important research challenges and promising future directions in NOMA.

Device-to-device (D2D)-enabled cloud radio access networks (C-RANs) are potential solutions for further improving spectral efficiency (SE) and decreasing latency by allowing direct communication between two users. However, due to the need to acquire global channel state information (CSI) and to execute centralized algorithms, heavy burdens are placed on the fronthaul and the baseband unit (BBU) pool. To alleviate these burdens, a distributed approach to mode selection and resource allocation for potential D2D pairs under pre-determined resource allocation of C-RAN users is proposed, in which pairs of users are endowed with decision-making capabilities. The proposed procedure is divided into three stages: communication mode and subchannel selection, utility value determination, and reinforcement-learning-based strategy update. The core idea is that the D2D pairs self-optimize the mode selection and resource allocation without global CSI under several practical constraints. Simulation results show that enabling D2D can significantly improve SE for C-RANs. Furthermore, the impacts of the fronthaul capacity, the centralized signal processing capability of the BBU pool, and the distance between the D2D transmitter and the remote radio head are demonstrated and analyzed.

In this paper, a simultaneous wireless information and power transfer-based traffic offloading scheme is proposed for cloud radio access networks with energy harvesting-aided device-to-device (D2D) communications. Wherein, the traffic offloading via D2D communications is utilized to alleviate the heavy burden on the capacity-limited fronthauls, while the energy harvesting design is adopted to stimulate offloading by compensating the energy consumption at the D2D transmitters. Different from the conventional works that harvest energy from ambient radio-frequency signals, dedicated energy signals are designed to provide a more flexible wireless power supply for the D2D transmitters with the data-energy beamforming technique. However, transmitting the dedicated energy signals impairs the performances of the wireless information transfer. To achieve a better balance between the traffic offloading gains and the wireless power transfer costs, a weighted sum-rate maximization problem is formulated. Nevertheless, the binary variables introduced by the offloading decision and the constraints on the data-energy beamforming make the optimization problem non-convex. To solve this intractable problem, a layered optimization method with the iterative optimizing and the ellipsoid method is proposed. Furthermore, an algorithm with lower complexity is proposed with a separated data-energy beamforming design. Simulation results reveal that a significant sum-rate gain can be achieved via the proposed scheme.

Wind power forecasting (WPF) is significant to guide the dispatching of grid and the production planning of wind farm effectively. The intermittency and volatility of wind leading to the diversity of the training samples have a major impact on the forecasting accuracy. In this paper, to deal with the training samples dynamics and improve the forecasting accuracy, a data mining approach consisting of K-means clustering and bagging neural network (NN) is proposed for short-term WPF. Based on the similarity among historical days, K-means clustering is used to classify the samples into several categories, which contain the information of meteorological conditions and historical power data. In order to overcome the over fitting and instability problems of conventional networks, a bagging based ensemble approach is integrated into the back propagation NN. To confirm the effectiveness, the proposed data mining approach is examined on real wind generation data traces. The simulation results show that it can obtain better forecasting accuracy than other baseline and existed short-term WPF approaches.

A key problem of content caching networks is that extra radio resource blocks are consumed to push content objects, which leads to a decline of spectrum efficiency. To solve this problem, a non-orthogonal multiple access-based multicast (NOMA-MC) scheme is proposed in this paper, where pushing and multicasting content objects can be accomplished simultaneously, and thus the spectrum efficiency can be improved significantly. To evaluate the performance of the NOMA-MC scheme, an explicit expression of outage probability is derived, which shows that full diversity gains can be achieved in the single-cell scenario. Moreover, the theoretical results can be extended to the multi-cell scenario by establishing a stochastic geometry-based network model, which show that the NOMA-MC scheme can achieve better performance than the conventional orthogonal multiple access-based multicast scheme. Then, the joint design of power allocation and content matching is studied to enlarge the performance gains of the NOMA-MC scheme, and two distributed optimization algorithms are proposed by solving a hospitals/residents matching problem. Finally, simulation results are provided to verify the analytical results, and also demonstrate the performance gains of the NOMA-MC scheme.

This paper considers the application of non-orthogonal multiple access (NOMA) to a multi-user network with mixed multicasting and unicasting traffic. The proposed design of beamforming and power allocation ensures that the unicasting performance is improved while maintaining the reception reliability of multicasting. Both analytical and simulation results are provided to demonstrate that the use of the NOMA assisted multicast-unicast scheme yields a significant improvement in spectral efficiency compared with orthogonal multiple access (OMA) schemes which realize multicasting and unicasting services separately. Since unicast messages are broadcast to all the users, how the use of NOMA can prevent those multicast receivers intercepting the unicasting messages is also investigated, where it is shown that the secrecy unicasting rate achieved by NOMA is always larger than or equal to that of OMA. Simulation results are provided to verify the developed analytical results and demonstrate the superior performance of the proposed NOMA scheme.

As an advanced paradigm, the cloud radio access network (C-RAN) promises high spectral efficiency (SE) and energy efficiency (EE). However, the capacity-constrained front-haul has become a key performance bottleneck in C-RANs. Besides SE and EE metrics, the cost related to different kinds of fronthaul is another major concern for operators. In this paper, an economical SE (ESE) metric is proposed to jointly take traditional EE and the impact of wired/wireless fronthaul cost into account. Aiming to maximize ESE, a non-convex beamformer design problem with fronthaul capacity and transmit power constraints is formulated, and an algorithm containing outer and inner loops is proposed to deal with this non-convexity. In particular, in the outer loop, the bisection search method is adopted to transform the primal problem into an equivalent subproblem; while in the inner loop, owing to the equivalence between the subproblem and the weighted sum rate, the subproblem is solved by the weighted minimum mean square error approach. Simulation results demonstrate that the proposed ESE is more reasonable than SE and EE for the fronthaul-constrained C-RAN. Furthermore, the proposed optimization solution can significantly improve ESE, in which the impact of fronthaul cost on ESE is evaluated.

In cloud radio access networks (C-RANs), the baseband units and radio units of base stations are separated, which requires high-capacity fronthaul links connecting both parts. In this paper, we consider the delay-aware fronthaul allocation problem for C-RANs. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity delay-aware fronthaul allocation algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for sufficiently small residual interference. Finally, the proposed fronthaul allocation algorithm is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

Device-to-device (D2D) enabled cloud radio access networks (C-RANs) are potential solutions for further improving spectral efficiency (SE) and decreasing latency by allowing direct communication between two user equipments. Due to the acquirement of global channel state information (CSI) and the execution of centralized algorithms in the uplink D2D enabled C-RANs, heavy burdens are put on fronthaul and the baseband unit pool. To tackle this challenge, a game-theoretic approach to mode selection and resource allocation for potential D2D pairs is proposed with a distributed manner, in which pairs are endowed with decision-making capabilities. The proposal is categorized into three stages: communication mode and subchannel selection, remote radio head (RRH) association, and reinforcement learning based strategy update. The core idea is that D2D pairs autonomously optimize the mode selection and resource allocation without global CSIs under several practical constraints. Simulation results show that enabling D2D can significantly improve SE for C-RANs. Furthermore, the performance gain is mainly determined by the fronthaul capacity and the distance between D2D transmitters and RRHs.

Fog computing based radio access network is a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. With the help of the new designed fog computing based access points (F-APs), the user-centric objectives can be achieved through the adaptive technique and will relieve the load of fronthaul and alleviate the burden of base band unit pool. In this paper, we derive the coverage probability and ergodic rate for both F-AP users and device-to-device users by taking into account the different nodes locations, cache sizes as well as user access modes. Particularly, the stochastic geometry tool is used to derive expressions for above performance metrics. Simulation results validate the accuracy of our analysis and we obtain interesting tradeoffs that depend on the effect of the cache size, user node density, and the quality of service constrains on the different performance metrics.

Cloud-radio access networks (C-RANs) are considered as energy efficient network architectures. Nevertheless, a dilemma in C-RAN architecture is to simultaneously optimize both spectral efficiency (SE) and energy efficiency (EE). In this paper, the tradeoff between SE and EE are studied considering a downlink multicasting transmission scenario in a C-RAN. In particular, to effectively mitigate the severe interference caused by densely deployed RRHs and improve the energy efficiency, a thinning scheme of RRHs is proposed. With a fixed diluted density of deployed active RRHs, the explicit closed-form expressions for coverage probability, SE and EE are derived by applying stochastic geometry. By using the obtained theoretical result as a utility function, a problem of optimizing the selection radius of RRHs is formulated to make a tradeoff between SE and EE for different preferences. Then, scalarization methods are proposed to give efficient solution. Numerical results validate the correctness and precision of performance analysis and show that the proposed approaches can generate an optimal selection radius of the thinning scheme.

A multi-carrier one-way relay network is considered, in which a source wishes to send information to a destination via a full-duplex amplify-and-forward relay. We perform power allocation across different subcarriers for the source and the relay, as well as subcarrier pairing at the relay, so as to maximize the achievable sum rate subject to individual power budget constraint at each transmit node, and therefore formulate the joint power allocation and subcarrier pairing optimization problem. Due to the interdependency in the power allocation between self-interference subcarrier and outgoing subcarrier at the relay node, we propose a suboptimal scheme to trade the performance for computational complexity. Then dual decomposition technique is employed for power allocation, while the subcarrier pairing problem is solved by Hungarian algorithm. Simulation results show that the proposed resource allocation scheme can significantly improve system performance.

In this paper, we focus on optimization of simultaneous wireless information and power transfer (SWIPT) in uplink cloud radio access network (C-RAN). The key idea is to design transceiver architecture and implement the SWIPT strategy at remote radio heads (RRHs). The minimum mean-square-error (MMSE) is considered as the performance metrics with transmit power constraint and energy constraint. Toward the goal of MMSE, the precoders and the detectors are iteratively updated in proposed scheme. However, because of the energy constraint, the precoding optimization problem with a specified detection matrix become non-convex. To solve this problem, a new precoding design based on the Lagrangian dual relaxation (LDR) is developed. Meanwhile, conditions to make sure the optimization problem has its physical interpretation and LDR become convex are determined. Finally, simulation results demonstrate that the proposed transceiver design can significantly improve the system performance.

To guarantee the rapidly increasing applications of electrical consumer, ultra small cells have been proposed to provide high spectral efficiency. However, which are severely constrained by the inter-cell interference (ICI) when the system load is high, and the enhanced inter-cell interference coordination (eICIC) is key to improving performance. To quantify the performance gains from eICIC to the load-varied small cells, a stochastic-geometry model is established in this paper, and the successful coverage probability with a dynamic frequency resource allocation (DFRA) scheme adaptive to the varied system load is researched, in which the small cell base station (SCBS) location is modeled as a Poisson point process. The proposed DFRA scheme can decrease ICI through the cooperation of adjacent SCBSs, which results in the correlated spectral usage in adjacent SCBSs. Several approximations are used to handle this kind of correlation, and a Bayesian theory based evaluation method is proposed to derive the asymptotic expression of successful coverage probability. Simulation results show that these approximated expressions match well with the simulated results, and the proposed DFRA can effectively decrease ICI and significantly increase the success coverage probability.

In cloud radio access networks (C-RANs), a substantial amount of data must be exchanged in both backhaul and fronthaul links, which causes high power consumption and poor quality of service (QoS) experience for real-time services. To solve this problem, a cluster content caching structure is proposed in this paper, which takes full advantages of distributed caching and centralized signal processing. In particular, redundant traffic on the backhaul can be reduced because the cluster content cache provides a part of required content objects for remote radio heads (RRHs) connected to a common edge cloud. Tractable expressions for both effective capacity and energy efficiency performance are derived, which show that the proposed structure can improve QoS guarantees with a lower cost of local storage. Furthermore, to fully explore the potential of the proposed cluster content caching structure, the joint design of resource allocation and RRH association is optimized, and two distributed algorithms are accordingly proposed. Simulation results verify the accuracy of the analytical results and show the performance gains achieved by cluster content caching in C-RANs.

A harvest-use-store power splitting (PS) relaying strategy with distributed beamforming is proposed for wireless-powered multi-relay cooperative networks in this paper. Different from the conventional battery-free PS relaying strategy, harvested energy is prioritized to power information relaying while the remainder is accumulated and stored for future usage with the help of a battery in the proposed strategy, which supports an efficient utilization of harvested energy. However, PS affects throughput at subsequent time slots due to the battery operations including the charging and discharging. To this end, PS and battery operations are coupled with distributed beamforming. A throughput optimization problem to incorporate these coupled operations is formulated though it is intractable. To address the intractability of the optimization, a layered optimization method is proposed to achieve the optimal joint PS and battery operation design with non-causal channel state information (CSI), in which the PS and the battery operation can be analyzed in a decomposed manner. Then, a general case with causal CSI is considered, where the proposed layered optimization method is extended by utilizing the statistical properties of CSI. To reach a better tradeoff between performance and complexity, a greedy method that requires no information about subsequent time slots is proposed. Simulation results reveal the upper and lower bound on performance of the proposed strategy, which are reached by the layered optimization method with non-causal CSI and the greedy method, respectively. Moreover, the proposed strategy outperforms the conventional PS-based relaying without energy accumulation and time switching-based relaying strategy.

Relay power control for two-way (TW) full-duplex (FD) relay network is studied in this letter. In the prior literature, the relay is assumed to always use its full transmit power in a TW FD relay network. However, high transmit power will increase loopback self-interference at the relay node and thus make the incoming link to the relay become the bottleneck for the end-to-end performance. Therefore, it is suboptimal to always utilize the full power capability of the relay. Thus, we propose three relay power control schemes, which can, respectively, maximize 1) the data rate of an individual end node, 2) the minimum data rate of the two end nodes, and 3) the sum data rate of the two end nodes. Numerical results show that, compared to using full relay power, the proposed relay power control schemes can improve system performance while reducing relay power consumption.

This paper studies an untrusted relay channel, in which the destination sends artificial noise simultaneously with the source sending a message to the relay, in order to protect the source's confidential message. The traditional amplify-and-forward (AF) scheme shows poor performance in this situation because of the interference power dilemma. Providing better security by using stronger artificial noise will consume more power of the relay, impairing the confidential message's transmission. To solve this problem, this paper proposes a modulo-and-forward (MF) operation at the relay with nested lattice encoding at the source. For the proposed MF scheme with full channel state information at the transmitter (CSIT), theoretical analysis shows that the MF scheme approaches the secrecy capacity within 1/2 bit for all channel realizations, and, hence, achieves full generalized security degrees of freedom (G-SDoF). In contrast, the AF scheme can only achieve a small fraction of the G-SDoF. For the MF scheme without CSIT, the total outage event, defined as either connection outage or secrecy outage, is introduced. Based on this total outage definition, analysis shows that the proposed MF scheme achieves the full generalized secure diversity gain (G-SDG) of order one. On the other hand, the AF scheme can achieve a G-SDG of only 1/2 at most.

The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm that incorporates cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets. Characterizing cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improve the energy efficiency (EE) in H-CRANs. An energy-efficient optimization objective function with individual fronthaul capacity and intertier interference constraints is presented in this paper for queue-aware multimedia H-CRANs. To solve this nonconvex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework. Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power, and intertier interference constraints, which can be regarded as a weighted sum EE maximization problem and solved by a generalized weighted minimum mean-square error approach. The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint.

In this paper, we study the stochastic optimization of cloud radio access networks (C-RANs) by joint remote radio head (RRH) activation and beamforming in the downlink. Unlike most previous works that only consider a static optimization framework with full traffic buffers, we formulate a dynamic optimization problem by explicitly considering the effects of random traffic arrivals and time-varying channel fading. The stochastic formulation can quantify the tradeoff between power consumption and queuing delay. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed into a per-slot penalized weighted sum rate maximization problem, which is shown to be nondeterministic polynomial-time hard. Based on the equivalence between the penalized weighted sum rate maximization problem and the penalized weighted minimum mean square error (WMMSE) problem, the group sparse beamforming optimization-based WMMSE algorithm and the relaxed integer programming-based WMMSE algorithm are proposed to efficiently obtain the joint RRH activation and beamforming policy. Both algorithms can converge to a stationary solution with low-complexity and can be implemented in a parallel manner, thus they are highly scalable to large-scale C-RANs. In addition, these two proposed algorithms provide a flexible and efficient means to adjust the power-delay tradeoff on demand.

The cloud radio access network (C-RAN) is an emerging network architecture that holds the promise of coping with the explosive growth of mobile wireless data. In this paper, by considering the stochastic traffic arrivals and time-varying channel conditions, we address the stochastic optimization of joint remote radio head (RRH) activation and linear beamforming to maintain delay performance and minimize average network power consumption in downlink slotted C-RAN. We first formulate the joint optimization as a group sparse beamforming problem. Based on Lyapunov optimization technique, the stochastic optimization problem is then transformed into a queue-aware joint RRH activation and beamforming problem, which can be greedily solved at each slot. Finally, a low-complexity stationary algorithm with guaranteed convergence and closed-form expressions is proposed. The proposed algorithm can be implemented in a parallel manner, thus it is highly scalable to a large-sized C-RAN. Extensive numerical simulations validate the effectiveness of the proposed algorithm.

This paper investigates the problem of resource and power allocation in device-to-device (D2D) underlays given a specific secrecy rate constraint. The objective is to optimize the pairing of D2D links with cellular user equipment (CUE) uplink channel resources, and to allocate their respective powers to combat against eavesdroppers for secrecy rate improvement. The proposed method first determines a set of candidate D2D links with the required signal-to-interference-plus- noise ratio level for each CUE to narrow the number of combinatorial sharing options. Afterwards, an optimization problem is formulated for maximizing the overall secrecy rate under user power constraints and minimum required secrecy rates. Finally, numerical results demonstrate the resulting performance.

Characterizing user access methods in heterogeneous cloud radio access networks (H-CRANs)is critical for performance optimization. Different from the user access in cloud radio access networks, the inter-tier interference from macro base station has a great impact on user access in H-CRANs. In this paper, after considering the inter-tier interference, the ergodic rates of downlink H- CRANs for two proposed user access methods, namely distance based and cluster based, are analyzed. The corresponding mathematical expressions of ergodic rates have been derived. In particular, the closed-form expression for the upper bound of ergodic rate is proposed. Simulation results corroborate the accuracy of the derived expressions for these two methods. Furthermore, the cluster based user access method outperforms the distance based user access method when the intensity of remote radio heads is sufficiently high.

To realize an efficient utilization of harvested energy and improve throughput with the help of a battery, a harvest-use-store power splitting (PS) relaying strategy with distributed beamforming is proposed for the wireless-powered multi-relay scenario in this paper. To this end, harvested energy via PS can be accumulated and stored for future usage, which affects throughput at subsequent time slots due to the battery operations including the charging and discharging. As a result, PS and battery operations are coupled with distributed beamforming, such that the throughput optimization problem is intractable looking. To address the intractability of the optimization, a layered optimization method with an ideal non-causal channel state information (CSI) assumption is proposed. As a result, the optimal joint PS and battery operation design in the proposed strategy is derived in a decoupled manner. Simulation results confirm the accuracy of the proposed method, and revealed that the proposed strategy has significant performance gains over the conventional designs.

Cloud radio access networks (C-RANs) have drawn considerable interests due to the significant improvements of spectral and energy efficiencies. Since most signal processing functions are moved to the centralized baseband unit (BBU), remote radio heads (RRHs) in C-RANs can be regarded as soft relays to transfer the received signals. The centralization characteristics in C-RANs make traditional channel estimation and training design approaches inefficient, and the requirements of perfect channel state information (CSI) would not be satisfied in turn. To solve this problem, a segment training based individual channel estimation scheme and the corresponding training design are proposed for C-RANs in this paper. Particularly, the channel estimator in terms of the sequential minimum mean-square-error (MMSE) is developed through a prior knowledge of long-term channel correlation statistics and previous channel estimates. The optimal training design for the developed estimator is derived by minimizing the estimation mean-square-error (MSE). Further, the optimal training design for the channel estimation of radio access links is computed by applying the eigenvalue decomposition (EVD). Numerical results show that performance gains of the proposed channel estimation and training design schemes are significant.

An optimal resource allocation scheme for orthogonal frequency division multiplexing (OFDM) based multiuser two-way full duplex (FD) relaying is discussed in this paper. Compared with traditional half duplex (HD) relaying, FD mode can double data rate under perfect interference cancellation, which, however can not be achieved under realistic conditions. Our goal is to maximize the throughput of the whole system by allocating available power and subcarriers under residual self-interference. We formulate a nonlinear optimization problem under individual power constraints. An optimal solution is obtained by using Lagrange dual decomposition method. In addition, a suboptimal solution which reduces the computational complexity significantly is also provided in this paper. Numerical results indicate advantage of the optimal resource allocation scheme proposed in this paper.

In this paper, we study the performance of ultradensely distributed antenna system in multi-floor buildings with high user density. To reduce the pilot overhead, we consider multi-floor pilot reuse. We derive the closed-form approximations of the sum-rate for the system using linear receivers, including the linear minimum-mean-squared-error receiver and the maximal ratio combining (MRC) receiver. We demonstrate the spectral efficiency per unit volume of the system and show that the ultradensely distributed antenna system is a promising way to achieve the spectral efficiency target of 5G.

In this paper, we consider the delay-optimal fronthaul allocation problem for cloud radio access networks (CRANs). The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity delay-optimal fronthaul allocation algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for sufficiently small cross link path gains. Finally, the proposed fronthaul allocation algorithm is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

A dilemma in cloud-radio access networks (C-RANs) is to balance the cluster scale and the cooperative gains. In this paper, cluster formation for downlink transmissions in C-RANs is studied. In particular, with a fixed intro-cluster cooperation strategy, an explicit expression for the successful access probability is derived by applying stochastic geometry. By using the obtained theoretical result as a utility function, the problem of grouping remote radio heads is formulated as a coalitional formation game, and then two distributed algorithms based on the merge and split approach are obtained as efficient solutions for the cases with and without cluster size constraints, respectively. Compared with grand cluster formation, which is a centralized method, simulation results show that the proposed approaches can achieve better performance with smaller cluster settings.

Considering that full-duplex relaying (FDR) can achieve higher spectral efficiency than traditional half-duplex relaying (HDR), a spectrum sharing protocol based on FDR is proposed in this paper. We investigate a two-phase FDR spectrum sharing protocol in cognitive two-way relay networks. Analytical expressions for the outage probabilities of the primary and secondary systems under the proposed protocol are derived in a closed form. Monte-Carlo simulations are carried out to confirm the analytical expressions. Results show that the performance of our two-way FDR spectrum sharing protocol depends on the residual loop interference (LI) at the relay node. When the residual LI is small, the proposed FDR spectrum sharing protocol can significantly improve the outage performance. Furthermore, if the residual LI is small enough, the two-way FDR spectrum sharing scheme can outperform its HDR counterpart, both for the primary system and the secondary system.

Distributed base station caching is an efficient way to reduce the network traffic load in advanced cellular networks, but suffers from a significant network delay due to the multi-hop transmission, leading to a poor quality of service. The presence of a few shortcut direct links transforms a regular network to a small world network resulting in achieving properties of high clustering coefficient and low average path length. In this paper, we propose a delay-optimized small world model for base station caching network to reduce the average network delay by decreasing multi-hop distance. In particular, we first study the degree-based and closeness-centrality shortcut addition strategies. To enhance these two strategies, we propose the delay-optimized shortcut addition strategy to decrease average network delay. The numerical results show that the proposed delay-optimized small world model can reduce the average network delay under remaining the clustering coefficient. Meanwhile, the average network delay with the proposal is 31.26% lower than that with the degree-based strategy, and is 18.78% lower than that with the closeness-centrality strategy.

The performance analysis under various precoding strategies in heterogeneous cloud radio access networks (H-CRANs) is increasingly highlighted. In this paper, we focus on suppressing the inter-tier interference between macro base stations (MBSs) and remote radio heads (RRHs), which is severely affected by the application of precoding schemes. Two precoding schemes including interference cancelation (IC) and beamforming (BF) are characterized for performance evaluation. The average bit error rate (BER) and system sum capacity under these two schemes are derived with closed-form expressions. Monte Carlo simulations are performed to verify our analytical results and demonstrate the effectiveness of precoding schemes. Furthermore, we explore this problem analytically and demonstrate that BF is preferred when the signal-to-noise ratio (SNR) of MBSs is relatively low and IC outperforms BF at the high SNR region.

Cloud radio access networks (C-RANs) have attracted considerable attention because of the capability of meeting the exponential increasing traffic demand in the future communication systems. In this paper, we consider the segment training based channel estimation in C-RANs. As the classical minimum mean-square-error estimator has cubic complexity in the dimension of the covariance matrices, due to the inversion operation, we propose a low-complexity channel estimator by means of the \emph{L}-degree matrix polynomial expansion, which can significantly reduce the computational complexity without degrading much performance. The numerical results are presented to verify the proposed channel estimators, and the simulation results show there are significant performance gains from our proposal.

As the evolution of cloud radio access networks (CRANs), heterogeneous cloud radio access networks (H- CRANs) are now recognized as promising paradigm to achieve high spectral and energy efficiency through taking advantages of both heterogeneous networks and C- RANs. In H-CRANs, the heterogeneous processing node (HPN) guarantees the basic quality of service (QoS) requirement for the user equipment, while remote radio heads (RRHs) are deployed to provide enhanced QoS performances. Inter-tier interference between HPNs and RRHs should be coordinated for achieving high throughput gains in H-CRANs. In this paper, the transmit power for both RRHs and HPNs are researched to mitigate this inter-tier interference. The throughput maximizing problem with and without interference coordination under the power and interference constraints are developed. Since this kind of optimization problem is not convex, these two non- convex optimization problems are transformed into the form of matrix. Through the Perron-Frobenius theory, the optimal power allocation solution is derived. Simulation results show that the proposed solution is converged, and it can achieve significant performance gains.

To support the unrelenting demand of high spectral efficiency and gigabit data rates driven by fast developing smart applications and internet of things, heterogeneous cellular networks (HCNs) with the multiple-antenna configuration have been presented as promising paradigms. In this paper, the downlink transmission performances of K-tier HCNs with multiple-antenna configurations are analyzed, where base stations (BSs) in each tier may differ in terms of the spatial density, transmit power, cell-bias factor, and the number of transmit antennas. Particularly, the service success probability is analytically developed with the stochastic geometry, which can evaluate the transmission reliability and congestion. The impacts of the cell association and user scheduling on the service success probability are derived with closed-form expressions, and the spatial multiplexing gains from the multiple-antenna configuration are exploited. The Monte Carlo simulation results demonstrate that the derived expressions for the service success probability are matched well, and indicate that the proper number of antennas should be chosen, which is strictly related to the densities of BSs and users in each tier.

The dynamic optimization problem for radio resource allocation in two-tier heterogeneous networks (HetNets) under the hybrid access mode is researched in this paper. To make the average utility of user throughput arbitrarily close to the optimum and maintain system queue stability under instantaneous and average power constraints, radio resource allocation optimization problem is formulated, and the corresponding solution that takes admission control, cell association, congestion control, subchannel and power allocation into account altogether is proposed, which is based on the Lyapunov optimization framework. The solution decomposes the optimization problem into three subproblems, in which the first two are linear and the third is mixed-integer non-linear. Both analysis and simulation results have verified that the proposal can achieve a significant utility performance gain under maintaining the queue stability with an [O(V), O(1/V)] tradeoff between throughput-utility optimality and traffic queue congestion.

Indoor localization technology has received increasing attention because the hobbies and interests of human can be mined from the location data. Wireless local area network (WLAN)-based fingerprinting localization methods have become attractive owing to their advantages of open access and low cost. However, for localization in realistic large areas, three problems persist: (1) excessive memory requirements in the offline phase; (2) high computational complexity in the online phase; and (3) how to select the access point (AP) sets with best distinction capability. A novel method of localization based on adaptive cluster splitting (ACS) and AP reselection is proposed in this paper. The suggested method can significantly reduce the requirements of offline storage and online computing capacity while improving the localization accuracy. The expected result is demonstrated in a theoretical deduction, simulation, and with experiments in a realistic environment.

User-cooperation-based multihop wireless communication networks (MH-WCNs) as the key communication technological component of mobile social networks (MSNs) can be exploited to enhance data rates and extend coverage. As one of the most promising and efficient user cooperation techniques, network coding can increase the potential cooperation performance gains among selfishly driven users in MSNs. To take full advantages of network coding in MH-WCNs, a network coding transmission strategy and its corresponding channel estimation technique are studied in this paper. Particularly, a four-hop network coding transmission is presented first, followed by an extension strategy for the arbitrary 2N-hop scenario (N ≥ 2). The linear minimum mean square error (LMMSE) and maximum-likelihood (ML) channel estimation methods are designed to improve the transmission quality in MH-WCNs. Closed-form expressions in terms of the mean square error (MSE) for the LMMSE channel estimation method are derived, which allows the design of the optimal training sequence. Unlike the LMMSE method, it is difficult to obtain closed-form MSE expressions for the nonlinear ML channel estimation method. In order to accomplish optimal training sequence design for the ML method, the Cramér-Rao lower bound is employed. Numerical results are provided to corroborate the proposed analysis, and the results demonstrate that the analysis is accurate and the proposed methods are effective.

Heterogeneous cloud radio access networks (H-CRANs) are potential solutions for improving both spectral and energy efficiencies by embedding cloud computing into heterogeneous networks. The interference among remote radio heads (RRHs) can be suppressed with centralized cooperative processing in the base band unit (BBU) pool, while the inter-tier interference between RRHs and macro base stations (MBSs) is still challenging in H-CRANs. In this paper, to mitigate this inter-tier interference, a contract-based interference coordination framework is proposed, in which three scheduling schemes are involved, and the downlink transmission interval is divided into three phases accordingly. The core idea of the proposed framework is that the BBU pool covering all RRHs is selected as the principal that would offer a contract to the MBS, and the MBS as the agent decides whether to accept the contract or not according to an individual rational constraint. An optimal contract design that maximizes the rate-based utility is derived when perfect channel state information (CSI) is acquired at both principal and agent. Furthermore, contract optimization under the situation in which only partial CSI can be obtained from practical channel estimation is addressed as well. Monte Carlo simulations are provided to confirm the analysis, and simulation results show that the proposed framework can significantly increase the transmission data rates over baselines, thus demonstrating the effectiveness of the proposed contract-based solution.

To decrease the training overhead and improve the channel estimation accuracy in uplink cloud radio access networks (C-RANs), a superimposed-segment training design is proposed. The core idea of the proposal is that each mobile station superimposes a periodic training sequence on the data signal, and each remote radio head prepends a separate pilot to the received signal before forwarding it to the centralized base band unit pool. Moreover, a complex-exponential basis-expansion-model based channel estimation algorithm to maximize a posteriori probability is developed. Simulation results show that the proposed channel estimation algorithm can effectively decrease the estimation mean square error and increase the average effective signal-to-noise ratio (AESNR) in C-RANs.

The simultaneous wireless transfer of information and power with the help of a relay equipped with multiple antennas is considered in this letter, where a “harvest-and-forward” strategy is proposed. In particular, the relay harvests energy and obtains information from the source with the radio-frequent signals by jointly using the antenna selection (AS) and power splitting (PS) techniques, and then the processed information is amplified and forwarded to the destination relying on the harvested energy. This letter jointly optimizes AS and PS to maximize the achievable rate for the proposed strategy. Considering that the joint optimization is according to the non-convex problem, a two-stage procedure is proposed to determine the optimal ratio of received signal power split for energy harvesting, and the optimized antenna set engaged in information forwarding. Simulation results confirm the accuracy of the two-stage procedure, and demonstrate that the proposed “harvest-and-forward” strategy outperforms the conventional amplify-and-forward (AF) relaying and the direct transmission.

In this paper, the channel estimation in uplink multiple access relay networks (MARNs) with analog network coding protocol has been researched. We apply the superimposed training (ST) scheme where each relay puts a separate training sequence on the top of the received one before forwarding to destination, and design a maximum likelihood based channel estimation algorithm for the composite source-relay-destination and individual relay-destination links. The optimal training sequences as well as the superimposed training power are also derived in closed forms. To make our study more complete, the channel estimation in the time-selective fading environment is further considered, and a correlation-based channel estimation (CBCE) algorithm is developed by taking advantage of time-domain channel autocorrelation nature. Simulation results show that the presented ST scheme can effectively improve the performance of multi-user detection in MARNs, and the proposed CBCE algorithm significantly outperforms the existing channel estimation methods.

This paper studies optimal resource allocation for multiple network-coded two-way relay in orthogonal frequency division multiplexing systems. All the two-way relay nodes adopt amplify-and-forward and operate with analog network coding protocol. A joint optimization problem considering power allocation, relay selection, and subcarrier pairing to maximize the sum capacity under individual power constraints at each transmitter or total network power constraint is first formulated. By applying dual method, we provide a unified optimization framework to solve this problem. With this framework, we further propose three low-complexity suboptimal algorithms. The complexity of the proposed optimal resource allocation ORA algorithm and three suboptimal algorithms are analyzed, and it is shown that the complexity of ORA is only a polynomial function of the number of subcarriers and relay nodes under both individual and total power constraints. Simulation results demonstrate that the proposed ORA scheme yields substantial performance improvement over a baseline scheme, and suboptimal algorithms can achieve a trade-off between performance and complexity. The results also indicate that with the same total network transmit power, the performance of ORA under total power constraint can outperform that under individual power constraints. Copyright © 2012 John Wiley & Sons, Ltd.

The base station (BS) density configuration is a key factor to improve energy efficiency (EE) performances. In this paper, BS density configurations for achieving the optimal EE performance in two-tier cellular networks are analyzed, where the Poisson point process (PPP) is used to model the BS spatial distribution. To make the EE performances trackable and analyzable, the BS density optimization in each tier is transformed into an equivalent problem that jointly optimizes "the sum and the ratio of BS densities". This equivalent optimization problem is not necessarily convex, while its monotonicity with different power consumptions of BSs is analyzable. Considering the optimal BS density configuration is not unique and the closed-form solution for achieving the best EE performance is extremely difficult to be derived, a dynamic gradient based iterative algorithm by solving the quadratic functions is proposed. Furthermore, the quantitative analysis of EE performances based on the data fitting method shows that the approximately linear relationship between the optimal BS density and the user density holds only under specific conditions. Simulation results have demonstrated the effectiveness of the proposed algorithm and verified relevant conclusions.

This paper proposes a robust channel estimation strategy for two-way multi-antenna relay networks, where the two sources are not perfectly synchronized with each other. The relay is allowed to first detect the signal arriving order (SAO) and then estimate the channel matrix of source-to-relay link in order to construct the relay precoding matrix. In particular, the SAO detection is formulated as a composite hypothesis testing problem, and effectively tackled by using the generalized likelihood ratio testing (GLRT) method. Moreover, a two-step estimation algorithm for composite source-to-source channels is developed aiming at reducing the error probability of data detection, and the optimal training sequences to minimize the Cramér-Rao bound of the estimation mean square error (MSE) is derived. Simulation results show that the proposed channel estimation strategy can effectively mitigate the estimation error due to the asynchronous transmission, thus significantly outperforming the existing channel estimation method.

The deployment of small cell base stations (SCBSs) overlaid on existing macro cellular networks can potentially improve the coverage and capacity of cellular wireless systems. However, a massive deployment of SCBSs also leads to an undesirable increase in energy consumption and severe intratier and inter-tier interference. In this paper, the joint design of discontinuous transmission (DTX) and power control (PC) is considered to minimize the sum energy consumption in heterogeneous small cell networks. Under the queueing delay constraints, the optimal design for maintaining the queue stability and improving the energy efficiency performance is derived via Lyapunov optimization. To facilitate the distributed implementation, an online joint DTX&PC algorithm (OJDPA) is developed using non-cooperative game theory approach. With the stability analysis, the (O(1/V, O(V))) power-delay tradeoff can be obtained by the proposed OJDPA. Simulation results corroborates the power-delay tradeoff of OJDPA and confirms that the OJDPA can achieve prominent energy efficiency performance, while still maintaining strong queueing stability.

Spectrum sharing for cognitive two-way relaying is considered in this paper. A secondary relay is used to assist two-way communications between two primary users, and as a return, the secondary relay is allowed to transmit its own secondary signal by using some of the licensed primary spectrum. Three different spectrum sharing strategies are studied in this paper. In the baseline superposition coding (SC) strategy, the relay superimposes its secondary signal on the relayed primary signals in the same phase, and in the SC strategy with secondary interference cancellation (SC-SIC), the interference from the superimposed secondary signal on the primary signals can be removed; whereas in the time division (TD) strategy, the relay uses two distinct phases to transmit the relayed primary signals and its own secondary signal. Therefore, relay power or transmission time is treated as the shared resource and needs to be allocated between primary and secondary transmissions in the three strategies. We develop resource allocation results which can minimize outage probability of the secondary signal, and meanwhile, can satisfy data rate targets of the two primary users. Our results show that, although the SC-SIC strategy requires the least shared resource to achieve the data rate target for primary users, the TD strategy can provide the best performance for secondary transmission because it can completely avoid mutual interference between primary and secondary signals.

Cognitive two-way relaying is studied in this paper. A secondary relay is used to assist bidirectional communication between a pair of primary users. As a return, the secondary relay is allowed to transmit its own secondary signal on the licensed primary spectrum. For this purpose, superposition coding is employed at the relay to superimpose the secondary signal on the primary signals. Three typical cognitive two-way relaying strategies are considered in this paper, namely, amplify-andforward (AF), XOR- based decode-and-forward (DF-XOR) and superposition based DF (DF-SUP). Outage probabilities of the two primary users under these three strategies are derived for Rayleigh fading channels. Their performances are compared with direct transmission and another recently proposed cognitive two-way relaying strategy which enables interference cancelation of the secondary signal at the primary users. Our results show that a major portion of relay power has to be allocated for relaying primary signals because the superimposed secondary signal always plays as an interference to the primary signals. Finally, we propose several potential solutions to suppress this interference to enhance the performance of cognitive two-way relaying.

In this paper, we study a cognitive two-way relay network in which orthogonal frequency division multiplexing (OFDM) technique is adopted within the secondary system and a pair of secondary source nodes communicate with each other through a relay node by using amplify-and-forward two-way relaying strategy. We propose an optimal power allocation scheme to maximize the smaller data rate of the secondary users while satisfying both individual node power and aggregation interference constraints. We decompose the optimization problem into two subproblems and solve them by using dual decomposition approach. Furthermore, we propose a distributed power allocation algorithm. Finally, numerical simulations and comparisons are presented to illustrate the performance of the proposed scheme.

Energy harvesting is necessary to make the wireless network self-sustaining and self-organizing regardless of the traditional power grid. How to allocate the limited radio resources in the energy harvesting wireless network is a challenging work. This paper focuses on optimizing the time and power related resources in the multi-cell scenario to maximize the throughput constraining of the changeable energy in the base station. The optimal off-line resource allocation strategy is proposed, based on analysis of structural properties of the optimal total power sequences. To decrease the complexity of the optimal solution, a low complexity suboptimal off-line algorithm is presented based on the nature of the concave function. Furthermore, inspired by the off-line algorithm, an on-line resource allocation algorithm is proposed as well. Simulation results show the suboptimal offline algorithm closely tracks the performance of the optimal solution. And the proposed on-line algorithm also has brilliant performance compared with several kinds of algorithms under different system settings.

Device-to-Device (D2D) technology provides a potential way to improve cellular network throughputs. However, severe interference is introduced with universal frequency reuse, which significantly degrades the network performance. In this paper, we investigate distributed power control strategies in a D2D underlaid cellular network. An enhanced single-leader-multiple-followers Stackelberg game model is presented, where the quality-of-service (QoS) constraints of both the macro base station user (leader) and the D2D users (followers) are considered simultaneously in the price update by using a discount factor. The conditions for the uniqueness of Stackelberg equilibrium (SE) are proposed, and a distributed power control algorithm related to the price update for the game model is proposed to achieve SE. The simulation results show that besides the reasonability and fairness in power allocation, our proposed scheme provides commendable QoS protection, and there exists a tradeoff between the QoS of the leader and followers by adopting different price policies.

Future mobile wireless communication networks will be featured as heterogeneity in order to enhance network performance and improve user experience. For better adaption to network challenges over its complexity and vulnerability, cell outage detection technique, a promising intelligent part of self-organizing networks (SON), has drawn considerable attention to deal with unexpected network faults. Our work is devoted to cell outage detection in a two-tier macro-pico network. Based on observation of performance metrics in time domain, we employ a classification algorithm called K-nearest neighbor (KNN) to achieve automatic anomaly detection. With some reasonable assumptions and a LTE-A system simulator, numerical experiments are implemented to demonstrate the efficiency of the proposed algorithm. Finally, localization for anomaly data and performance evaluation are further carried out to validate the classification accuracy.

Using Device-to-device (D2D) communications in a cellular network is an economical and effective approach to increase the transmission data rate and extend the coverage. Nevertheless, the D2D underlaid cellular network is challenging due to the presence of inter-tier and intra-tier interferences. With necessarily lower antenna heights in D2D communication links, the fading channels are likely to contain strong line-of-sight components, which are different from the Rayleigh fading distribution in conventional two-tier heterogeneous networks. In this paper, we derive the success probability, spatial average rate, and area spectral efficiency performances for both cellular users and D2D users by taking into account the different channel propagations that they experience. Specifically, we employ stochastic geometry as an analysis framework to derive closed-form expressions for above performance metrics. Furthermore, to reduce cross-tier interferences and improve system performances, we propose a centralized opportunistic access control scheme as well as a mode selection mechanism. According to the analysis and simulations, we obtain interesting tradeoffs that depend on the effect of the channel propagation parameter, user node density, and the spectrum occupation ratio on the different performance metrics. This work highlights the importance of incorporating the suitable channel propagation model into the system design and analysis to obtain the realistic results and conclusions.

Resource sharing between primary users (PUs) and secondary users (SUs) in cognitive radio (CR) networks is built on strict interference limitations. However, such limitations may be easily violated by SUs using imperfect SU-to-PU channel state information (CSI). In this paper, we propose a robust decentralized CR network design by explicitly taking into account imperfect SU-to-PU CSI from a game theoretical perspective. We formulate the CR network design as a generalized Nash equilibrium problem (GNEP), where the SUs compete with each other over the resources made available by the PUs, who are protected by the robust aggregate interference constraints. We establish a framework-based generalized variational inequality (GVI) theory to analyze the formulated robust GNEP. It is shown that the solution to the robust GNEP can be obtained by solving a GVI, which can be addressed by a distributed pricing mechanism in the CR network, where the SUs play a priced NEP with given prices and the PUs are in charge of setting prices. Then, we propose distributed algorithms, along with their convergence properties, for the SUs to solve the priced NEP and for the PUs to update prices, respectively. We also provide an efficient method to compute the optimal transmit strategy of each SU via convex optimization.

Wireless network coding can significantly improve the spectrum efficiency for relaying transmission when receivers can acquire accurate channel state information (CSI). In this paper, the channel estimation problem for two-way relaying channels is considered where two sources exchange information through an amplify-and-forward relay employing analog network coding protocol. By taking advantage of the apriori information of wireless channels to further improve channel estimation accuracy, the maximum a posteriori (MAP) based estimation schemes are developed to estimate the composite source-source channel coefficients and the amplitude of individual source-relay channels with apriori knowledge of channel distribution information (CDI). Variations of MAP estimation algorithms are also developed for systems under practical constraints where channel CDI needs to be estimated. In particular, scale MAP estimator as well as a long term estimation algorithm is developed to effectively control the negative impact of CDI estimation error on MAP estimation performance. The simulation results show that the MAP based estimation strategies consistently outperform maximum likelihood estimation methods in the measure of mean square error, thus establishes the advantage of presented MAP based schemes.

The impact of downlink co-channel interference (CCI) is increasingly highlighted in heterogeneous networks (Het-Nets) where the macrocell base stations (MBSs) and low-power nodes share the same spectrum resource. In this paper, we focus on mitigation of downlink CCI from MBS to picocells, which are severely affected, through the application of coordinated beam forming (CBF) technique. The closed-form expressions of average throughput for an arbitrary user with and without using CBF are derived, where a trade-off is observed between suppressing the CCI to picocells and increasing the throughput of the macrocell. Furthermore, to maximize the HetNet throughput, two adaptive algorithms are proposed to determine the CBF set of picocells based on the instantaneous throughput and the average throughput, respectively. The analysis of average throughput is validated by simulation results, and total throughput of the HetNet is improved by use of our proposed algorithms.

Power allocation for orthogonal frequency division multiplexing (OFDM) based two-way relay link in cognitive relay networks is discussed in this paper. Two secondary source nodes exchange their information through a secondary relay node by using amplify-and-forward two-way relaying strategy. The goal of power allocation is to maximize the sum data rate of the secondary system subject to the constraint that the aggregation interference of the secondary system on the primary system does not exceed the given threshold. In addition, individual node power constraint is applied for both secondary source nodes and the secondary relay node. We solve the optimization problem by using dual decomposition approach, and further propose a distributed power allocation algorithm. Numerical results demonstrate the advantage of the proposed algorithm over uniform power allocation. Besides, power-constrained as well as interference-constrained regions are shown in our numerical experiments.

With the rapid development of the mobile wireless system, the operator is experiencing unprecedented challenges on service maintenance and operational expenditure, which drives the demand for realizing automation in current networks. The cell outage detection is considered as an effective way to automatically detect network fault. Our work presents an automated cell outage detection mechanism in which a clustering technique called Dynamic Affinity Propagation (DAP) clustering algorithm is introduced. Performance metrics are collected from the network during its regular operation and then fed into the algorithm to produce optimal clusters for further anomaly detection. The proposed mechanism has been implemented in the LTE-Advanced simulation environment, through which we have successfully detected the configured cell outages and located their specific outage areas.

In this paper, a network coding broadcast (NC-BC) scheme is proposed to improve the spectral efficiency for multiple-input and multiple-output (MIMO) transmissions. Particularly by network coding the base station broadcast message with each user message respectively, NC-BC can save the radio resource allocated for base station broadcasting, which is necessary in conventional time division-based scheme. Moreover, the iterative precoding design for NC-BC is studied for approaching the optimal performance, and the sub-optimal precoding design with low complexity is also provided. The simulation results show that the proposed NC-BC scheme with precoding optimization can further improve the system transmission performance.

ABSTRACT Outage performance is analyzed for opportunistic decode‐and‐forward cooperative networks employing orthogonal space–time block codes. The closed‐form expressions of diversity order and the end‐to‐end outage probability at high signal‐to‐noise ratio regime are derived for arbitrary relay number (K) and antenna configuration (N antennas at the source and each relay, ND antennas at the destination) under independent but not necessarily identical Rayleigh fading channels. The analysis is carried out in terms of the availability of the direct link between the source and the destination. It is demonstrated that the diversity order is min{N, ND} ⋅ KN if the direct link is blocked, and if the direct link is available, the diversity order becomes min{N, ND} ⋅ KN + NND. Simulation and numerical results verify the analysis well. Copyright © 2011 John Wiley & Sons, Ltd.

Joint scheduling and resource allocation in uplink orthogonal frequency division multiplexing (OFDM) systems is complicated, and even gets intractable with large subcarrier and user number. This paper investigates the resource allocation for OFDM-based multi-user multiple-access relay channels (MARC) with network coding. We formulate a joint optimization problem considering source pairing, subcarrier assignment, subcarrier pairing and power allocation to maximize the sum-rate under per-user power constraint. The problem is addressed in polynomial time by optimizing three separate subproblems, and afterwards three low-complexity suboptimal algorithms are further proposed. The simulation results demonstrate performance gain of the proposed algorithms versus per-node transmit power and source nodes number, respectively.

Spectrum sharing between primary and secondary systems in cognitive two-way relay networks is discussed in this paper. A two-phase spectrum sharing protocol is proposed, in which a secondary (cognitive) user acts as the relay to assist the bidirectional primary transmission. Analog network coding (ANC) is employed for two-way relaying between the two primary users, and spectrum sharing is achieved by superimposing the secondary signal on the network-coded primary signals at the secondary relay. We analytically derive the outage probability of the primary users under our proposed two-phase protocol, and identify the spectrum sharing regions within which the spectrum sharing protocol can achieve higher (or at least equal) outage performance for the primary users than direct transmission without spectrum sharing. Results show that our proposed two-phase spectrum sharing protocol can outperform another recently proposed three-phase protocol which is based on decode-and-forward (DF) network coding operation.

Distributed antenna systems (DAS) are deployed not only for enhancing coverage, but also for reducing the transmission power consumption, which have emerged as the promising candidate for energy-efficient wireless communications. In this paper, orthogonal and co-channel resource allocation schemes considering energy efficiency in single-cell DAS are analyzed. For orthogonal resource allocation scheme, the interference is avoided by sacrificing the available resources. While for the co-channel resource scheme, the spectrum efficiency improves under the expense of increasing the interference. The close-form expressions of spectrum and energy efficiency of these two schemes are derived, by which the impact of transmission power on spectrum and energy efficiency are theoretically compared. Both the theoretical analysis and simulation results indicate that increasing transmission power beyond a reasonable level will seriously impair energy efficiency. Furthermore, the DAS with co-channel resource allocation scheme is recommended to utilize in the green communication systems.

The performance of heterogeneous networks is seriously decreased due to the uplink co-channel interference (CCI), especially the interference to low-power nodes which are located within the coverage of macrocells. In this paper, we take into consideration the application of spatial interference coordination (IC) technique to mitigate the uplink CCI from macrocell users to the picocell. The lower bound of average signal to interference plus noise ratio (SINR) in uplink transmission is developed as an analytical expression for the user of picocells applied with spatial IC. Furthermore, to determine the suitable coordination set, we propose two adaptive IC algorithms, which are based on the metrics of instantaneous SINR and the lower bound of average SINR, respectively. It is observed from the simulation results that the throughput of picocells is greatly enhanced by the proposed algorithms.

In this paper, physical-layer network coding with spatial modulation in two-way relaying channels is studied. Two multi-antenna source nodes exchange information via a denoise-and-forward (DNF) relay node, and space shift keying modulation is employed in both source nodes and relay. The error performance in terms of average symbol error probability is evaluated. To further enhance the transmission quality, the denoise mapping operator is concerned to be optimized, and a graph-based approach is adopted to make the problem more tractable. Effective sub-optimal algorithm based on dichotomizing method is presented to reduce the solving complexity. Simulation results in terms of symbol error rate and throughput show that the DNF protocol with optimized mapper outperforms that with random or no mapper.

Carrier aggregation (CA) has been proposed within 3GPP to aggregate multiple component carriers (CCs) in LTE-Advanced system to achieve higher peak data rate. In this paper, we propose CA between FDD and TDD carriers in order to utilize FDD and TDD spectrums more efficiently. By comparing the differences in control channel between LTE FDD and TDD specifications, three problems in the case of CA between FDD and TDD carriers are identified, and we then propose the respective solutions and analyze their pros and cons. It is shown that CA between FDD and TDD carriers can provide system flexibility and performance benefits for the operators which own both FDD and TDD spectrums.

This paper investigates optimal resource allocation in orthogonal frequency division multiplexing (OFDM) based multiple two-way relay systems operating with analog network coding (ANC) protocol. We formulate a joint optimization problem considering power allocation, relay selection and subcarrier paring to maximize the sum capacity under individual power constraints on each node. By applying dual method, an unified optimization framework is provided and the problem is solved efficiently. Based on this framework, we further propose three suboptimal algorithms. The complexity of optimal algorithm (ORA) and three suboptimal algorithms are evaluated, and the analysis results show that the complexity of ORA is only a polynomial function of the number of subcarriers and relay nodes. Simulation results demonstrate that the proposed ORA scheme yields substantial performance improvement over baseline scheme, and suboptimal algorithms can achieve a trade off between performance and complexity.

This paper proposes the separate-SMDP approximation technique to solve the dimensionality problem of the semi-Markov decision process (SMDP) model for radio resource management (RRM). The proposed separate-SMDP model approximates the original complex SMDP linear programming (LP) problem by building a series of simple SMDP LP problems. The estimated policy results are obtained by solving these LP problems in a distributed manner. Related algorithms are proposed to support the model in actual HWN RRM process. This technique not only greatly decreases the computational complexity of SMDP and solves its dimensionality problem, but also adapts well to actual RRM and provides a new horizon for utilizing SMDP.

An improved high-spectral-efficiency coding-rotated modulation (CRM)-iterative demodulation/decoding (ID) scheme in the orthogonal frequency division multiplexing (OFDM) system is proposed. For the rotated QAM, it unveils the optimum rotation matrix depending on the modulation order, the code rate and the receiver scheme (ID or non-ID). The suboptimal solution of the rotation angle is proposed, and a novel two-dimensional time/frequency component interleaver is also put forward to make the best use of the modulation diversity, time diversity and frequency diversity. Besides, code-matched four-dimensional QAM rotation matrices and the corresponding time/frequency component interleaver are proposed to improve the error performance furthermore. The analysis of extrinsic information transfer chart (EXIT) chart is also given to explain the convergence characteristic. Simulation results have turned out that this new scheme can significantly outperform the conventional BICM scheme.

In this paper, we consider a secrecy relaying communication scenario where all nodes are equipped with multiple antennas. An eavesdropper has the access to the global channel state information (CSI), and all the other nodes only know the CSI not associated with the eavesdropper. A new secrecy transmission protocol is proposed, where the concept of interference alignment is combined with cooperative jamming to ensure that artificial noise from transmitters can be aligned at the destination, but not at the eavesdropper due to the randomness of wireless channels. Analytical results, such as ergodic secrecy rate and outage probability, are developed, from which more insightful understanding of the proposed protocol, such as multiplexing and diversity gains, can be obtained. A few special cases, where outage probability cannot be decreased to zero regardless of SNR, are also discussed. Simulation results are provided to demonstrate the performance of the proposed secrecy transmission protocol.

There has been a growing interest in analog network coding, which can improve system throughput and spectrum efficiency significantly in wireless systems. Two-way relay scenario is considered in this paper, where the base station (BS) and the relay are both equipped with multiple antennas, and each of K user equipments (UEs) in the cell has a single antenna. An advanced multi-user scheduling scheme is proposed for the analog network coded two-way relay channel, where UEs and BS exchange information via the relay node during two time slots. Because each UE with single antenna has poor transmission and reception capability, the scheduling scheme is carefully designed to decrease multi-user interference and optimize beamforming gain from BS to relay. Analytical results of sum rate and outage probability have been developed to demonstrate that the multi-user diversity gain can be achieved by the proposed scheme. Meanwhile, the user fairness of the proposed scheme is on the same order as that of round robin (RR), and the computation complexity only increases linearly with the number of users in the serving cell. Monte-Carlo simulation is conducted to exhibit the performance gains from the proposed scheduling scheme. Results show that the proposed channel correlation based user selection scheme outperforms existing traditional scheduling schemes significantly, even when the cellular user payload is low.

ABSTRACT We consider the problem of optimal power allocation and optimal user selection in a layered multicast transmission over quasi‐static Rayleigh fading channels. A scheme based on superposition coding is proposed in which basic multicast streams and enhanced multicast streams are superimposed and transmitted by a base station, while users with worse channel conditions can only decode basic multicast streams, and users with better channel conditions can decode both basic and enhanced multicast streams. In this paper, subject to fixed user selection ratios, the optimal power allocation for each stream that maximizes average throughput is investigated, and the impact of power allocation on average outage probability is discussed. Finally, subject to fixed transmit power and power allocation, the optimal user selection ratio for enhanced multicast streams is also studied. Numerical results show that the optimized layered multicast scheme outperforms the conventional multicast scheme in terms of average throughput. Copyright © 2010 John Wiley & Sons, Ltd.

To obtain better cooperative diversity on relay networks. A joint network and channel coding (JNCC) scheme based on turbo codes and corresponding iterative decoding strategy are proposed for uplink multiple-access relay channel (MARC) systems. The proposed scheme is considered by using different processing ways for different users and turbo network coding for the relay where only the extra redundancy is forwarded by the relay, designing iterative decoding strategy at destination to achieve the improvement of bit error rate (BER) performance. By doing this, a better error correction capability is embedded to the proposed JNCC scheme providing a better help to the destination when decoding different users original messages. Simulation results demonstrate that the proposed scheme substantially improves the BER performance to the referenced schemes in AWGN and Rayleigh fading channels respectively.

In this study, the authors propose an incremental opportunistic decode-and-forward (ODF) cooperation scheme employing orthogonal space–time block codes (OSTBC) under Rayleigh fading channels. Different from existing research, the authors use ODF and OSTBC together to maximise the diversity gain, while incremental relaying is implemented to improve transmission efficiency. Besides, a more practical system model is introduced where multiple geographically isolated nodes are equipped with multiple antennas and the large scale fading is taken into consideration. The exact expression of end-to-end outage probability is derived in the whole signal-to-noise ratio (SNR) region for arbitrary relay number, antenna configuration and incremental relaying trigger value. Through asymptotic analysis, the authors also obtain the closed-form expression of outage probability in high SNR region and derive the expression of diversity order. The results show that the proposed scheme outperforms traditional direct transmission and fixed ODF in arbitrary SNR region. Meanwhile, the outage probability and diversity order depend directly on the relay number, antenna configuration and incremental relaying trigger value. The analytical results can be utilised to facilitate network planning and optimisation. Simulation results are presented to verify the authors analysis.

Network coding can deliver multiple data streams simultaneously and make full use of broadcast nature of wireless channels. The authors propose two diversity-enabled network-coding (NC) schemes to optimise wireless uplink scheduling. The existing scheduling protocols normally have to allow the users with relatively low channel gains to transmit, and it can maintain fairness but reduce congregated throughput. The main idea of the proposed scheme is to always schedule users with the best channel condition, while the use of NC encourages the scheduled users to help others which have not been served previously. Delay and capacity performance for different network coded scheduling schemes are analysed. Round-robin and pure opportunistic scheduling are evaluated for performance comparison. In order to show the effectiveness of the proposed schemes, NC schedulers are applied to a time division duplexing code division multiple access wireless cellular networks. System-level simulation was carried out based on the third generation partnership project specifications. Per-sector average throughput and cumulative distribution function of user average throughput are adopted as the performance metrics. Analytical and simulation results show that the proposed NC schedulers can achieve a better tradeoff between fairness and throughput than those without NC.

We consider two end users which have asymmetric traffic requirements in terms of both data rate and outage probability. They exchange information in Rayleigh flat-fading channels, and a half-duplex relay node is employed to assist the bidirectional communication between them, using the analog network coding (ANC) protocol. The provision of power levels for each node is investigated so as to minimize the total energy consumed to satisfy the asymmetric traffic requirements. By comparing energy consumptions between ANC strategy and direct transmission strategy, the relay operating region is also identified in which relay-assisted transmission can yield higher energy efficiency than direct transmission. It also indicates that the relay node is best positioned at the middle point of the two end nodes for any asymmetric traffic requirements from the perspective of energy consumption minimization.

In this paper, we study the design of network coding for the multiple two-way relaying channels where multiple pairs of sources wish to exchange information with their partners. All nodes are equipped with multiple antennas, and we focus on a particular scenario where the sources have less antennas than the relay. For such a case, the application of existing protocols developed for the scenario with a single pair has to rely on the time sharing approach, which will result in some loss of reliability and throughput. In this paper, we develop a new network coding transmission protocol which can be viewed as a combination of traditional beamforming and the recently developed approaches of signal alignment. By using such a protocol, all M pairs of source nodes can accomplish information exchanging within two time slots. We have developed analytical results, such as outage probability and diversity-multiplexing tradeoff, which demonstrate that the proposed transmission protocol can achieve a larger multiplexing gain than the time sharing approach. Furthermore, the proposed network coding scheme is extended to the special case where source nodes only have a single antenna. Simulation results have been provided to demonstrate the performance of the proposed network coding schemes.

In this paper, we study a special case of multi-way relaying channel, to which traditional beamforming cannot achieve the best performance due to insufficient antennas. A new transmission protocol is proposed by aligning the messages from the same pair with the help of relay precoding. As a result, inter-pair interference can be avoided and intra-pair interference can be coped with by using network coding. Then analytic results, such as the ergodic sum rate and the outage probability, are developed for the proposed protocol. The numerical results are also provided to demonstrate the performance of our proposed scheme. To improve the diversity gain of the proposed scheme, an optimal scheme is also presented.

Considering a cooperative multicast system where 2 sources transmit data to 2 users with the help of 1 relay (2-1-2), the performance of a network coded cooperative (NCC)transmission scheme is evaluated. Due to the use of network coding, more than one source node can be served simultaneously by one cooperative transmission. As a result, the time resource consumed by the cooperative transmission is reduced and the spectral efficiency is significantly increased. In this paper, to facilitate performance evaluation, three information-theoretic metrics, i.e., ergodic capacity, outage probability and diversity gain are studied. Both analytical and numerical results show that the NCC scheme can significantly improve ergodic capacity, while achieving almost the same outage probability and the same diversity gain as the conventional scheme.

Interference coordination is the key to reduce intercell interference in a TD-LTE system. Because of implementation complexity, partial and soft frequency reuse schemes are hardly suitable to TD-LTE systems. The decode-and-forward relay scheme, which may generate some additional interference, is presented to extend coverage and improve performance of TD-LTE systems. To mitigate the relay related interference, a directional relay topology is proposed in this paper. The radio resource is reused between the isolated RSs to improve the spectrum efficiency. The joint interference coordination and directional relay can suppress most inter-cell and inter-relay interferences and improve the capacity. The signal to interference plus noise ratio (SINR) distributions and throughput for the traditional non-relay scenario and the proposed directional relay scenario are derived. The analysis and simulation results show the effectiveness of the proposal scheme.

An outage probability analysis model is presented to evaluate performances and show key factors impacting on performances for the heterogeneous relaying scheme utilized to complete the hierarchical convergence of multiple radio access networks (RANs). The diversity gains related to the multiple access schemes and power constraints are analyzed. Our analytical and simulation results show that the outage probability mainly depends on the number of cooperative relay nodes, multiple access scheme, normalized power, and radio channel characteristics. Meanwhile, the power constraint for relay nodes does not have much impact on the outage performance especially when the broadcast/multicast radio channel condition is good enough.

In this paper, we study the outage performance of decode-and-forward (DF) opportunistic communication systems with the Nth best relay selection scheme under independent but not necessarily identical Rayleigh fading channels. In traditional opportunistic cooperation, only the best relay helps forward the source's messages to the destination. However, in practical systems, the best relay may be unavailable due to scheduling and overload problems. In this case, another suboptimal but available relay may be selected to participate in cooperation. Without loss of generality, we analyze the performance when the Nth best relay is selected to cooperate and take the availability of the direct link between the source and the destination into consideration. In particular, we derive the exact expression of outage probability for arbitrary signal-to-noise (SNR), then we obtain the closed-form expression in high SNR region. Through asymptotic analysis, the diversity order which reflects the effect of the direct link is obtained and it is linearly related with the number of relays and the selection criterion N. Simulation results are presented to verify the analysis.

In the planning of cellular networks, link budget calculation is the first step to get the maximum allowable cell radius that guarantees a target signal to interference plus noise ratio (SINR) with a certain level of outage probability at the cell border. The interference margin as one of key inputs for link budget is used to compensate for co-channel interference. In this paper, a mathematical analysis for interference margin as a function of system load is proposed in orthogonal frequency division multiple access (OFDMA) cellular networks. The analysis is derived from OFDMA features based on a general expression of exponential effective SINR mapping (EESM). The proposed approach can be easily applied to the evaluation of interference from neighboring cells, and help to improve the accuracy of the link budget calculation in OFDMA systems. All the theoretical analysis given in this paper have been proved by Monte Carlo simulations.

The Physical Downlink Control Channel (PDCCH) is used to signal dynamic resource assignment information in the Long Term Evolution (LTE) system. In the presence of numerous active users, the system performance is likely to be hindered by shortage of control channel resource. In this paper, several simple algorithms are brought forward for making efficient PDCCH resource allocation. We first propose a minimum aggregation level algorithm (Min-AL) to maximize the total number of scheduled users by exploiting multi-user diversity gain, followed by algorithms with the purpose of improving the blocking performance of cell-edge area through co-channel interference avoidance (CCI-A) and priority boosting (PB). Simulation results have shown that the Min-AL algorithm achieves the best system performance at the expense of cell-edge performance, while both CCI-A and PB algorithms are effective in reaching a compromise between system and cell-edge performance.

This paper considers the network coded relay protocol utilized in the cellular systems. The time division multiple access (TDMA) based opportunistic network coded cooperation(TDMA-ONCC) scheme, whose central idea is to perform the opportunistic network coded cooperation based on the observed instantaneous channel qualities of source-to-relay and relay-to-destination links, is researched. Performances in terms of ergodic capacity and outage probability are derived for both TDMA-ONCC and the single relay based opportunistic selecting Decode-and-Forward (SR-OSDF) schemes. The tight closed form expressions for the outage probability are derived. Numerical results show that the proposed expressions for both the ergodic capacity and the outage probability are close to the theoretical analysis results. The results show that the TDMA-ONCC scheme significantly outperforms the SR-OSDF scheme due to the network coded cooperation gain.

In this paper, we investigate the outage performance of decode-and-forward (DF) based opportunistic cooperative communications with channel estimation errors at the receivers under Rayleigh fading channels, in which the effect of the direct link has been explicitly evaluated. To be specific, the exact expression of outage probability has been derived for arbitrary values of signal-to-noise ratio (SNR) when pilot power is fixed. Through asymptotic analysis, the closed-form expressions of outage probability in the high SNR region and the diversity order are obtained. The analysis shows that the channel estimation errors due to limited pilot power impose a significant impact on the outage performance, leading to the diversity order be 0 even when the direct link is available. Although deploying more relays can mitigate the estimation errors effectively, the performance limit caused by the estimation errors becomes more obvious as SNR grows. For specific pilot power configuration and relay deployment, the outage probability remains unchanged even if data power approaches to infinity. Furthermore, simulation results are presented to verify our analysis.

In time division-high speed packet access plus (TDHSPA+) system, multiple input multiple output (MIMO) technique is adopted to improve spectrum efficiency and transmission reliability. However, most current literature are focused on the large spacing antenna setup, which is impractical for user equipment (UE) because of the size limitation. Therefore, dual polarization antenna scheme is introduced in the system to provide multiplexing gain and/or diversity gain. In this paper, we derive the dual polarization channel model for performance evaluation. Furthermore, the performance of dual polarization scheme under different MIMO architectures is presented and analyzed. It is shown that polarization scheme can achieve similar average throughput and successful transmission ratio compared with spatial separation scheme in various scenarios.

Wireless cooperative relay is an efficient way in obtaining diversity in distributed manner, and it can also be used as a way in extending the cell coverage in cellular systems. In this paper, assuming receiver channel state information (CSI) only, the ergodic and outage capacity analysis are presented for amplify-and-forward (AF) multiple-input multiple-output(MIMO) relay channels deploying orthogonal space-time block codes (OSTBCs). Under flat Rayleigh fading environment, we derive closed-form analytical expressions for the calculation of the ergodic capacity and outage capacity for both the OSTBC transmission with and without the direct link. Numerical results show that all the derived analytical expressions are very tight and are valid for evaluations of the ergodic and outage capacity under various antenna configurations. The results also show how the number of transmit antennas and receives antennas affect the overall channel capacity under fading channel environments.

In the next generation wireless systems, it's quite likely that a large number of user terminals are not able to transmit and/or receive over the entire system bandwidth, as much wider system bandwidth, e.g., 100 MHz, is expected to be employed in the future. As a new and challenging problem, user terminal bandwidth limitation is considered in this paper, and multiuser resource allocation for orthogonal frequency division multiplexing (OFDM) downlink with terminal bandwidth limitation is discussed. The optimal multiuser resource allocation strategy is first presented; however, due to the prohibitive computational expense of the optimal strategy, two suboptimal resource allocation strategies with significantly reduced complexity are proposed as well. The performance of the two suboptimal strategies is given via simulations, and is discussed for various scenarios.

Although the established capacity results for the classic three-node relay channel rely on the assumption of fullduplex relay nodes, it's practically difficult for radio transceivers to support simultaneous signal transmission and reception. Timedivision half-duplex relay strategies have thus been proposed to address this limitation. Amplify-and-forward (AF) and decode-and-forward (DF) are two well-known half-duplex relay strategies in the literature. Later, DF approach was extended by the so-called variable-rate two-phase relay strategy, in which the transmission rates for both phases are not necessarily the same. More recently, a novel half-duplex relay strategy based on the information theoretical concepts of broadcast channel (BC) and multiple access channel (MAC) models is proposed. In this approach, the source broadcasts distinct data flows to the destination and the relay respectively during the first phase, and in the second phase the relay forwards the received information whereas the source sends another data flow to the destination. In this paper, we will first develop optimal resource allocation, in terms of power and time, for these half- duplex relay strategies, and then compare their performance under various link conditions.

According to the number of successful decoded relay nodes with good destination-to-relay and relay-to-destination link qualities, a simple adaptive relay selection algorithm and its corresponding protocol is presented to achieve a good diversity and multiplexing gains. When the antenna number of the destination is less than the number of successful decoded relay nodes, the opportunistic relay selection scheme is utilized for achieving the diversity gain. Otherwise, the multiplexing selection scheme is adopted to achieve the multiplexing gain. We analyze the outage probability performance, and the asymptotic expression in terms of the outage probability for the proposal is derived. The diversity-multiplexing tradeoff (DMT) and ergodic capacity for the proposed algorithm are also evaluated. Compared with the individual opportunistic relay selection scheme and multiplexing selection scheme, the proposed adaptive relay selection algorithm can provide better capacity and lower outage probability.

The conventional cooperative protocols have to face some problems in practical broadcast and multicast systems because feedback channel is unavailable. An adaptive cooperative relay protocol, which can be adopted in future broadcast systems, is proposed in this paper. The outage probability performance of this protocol is analyzed over Nakagami-m fading channel, and the asymptotic expression of outage probability for proposed protocol is derived. It is compared with the conventional Decode-and-Forward and Amplify-and-Forward protocols. A combination of numerical simulation and theoretical analysis, including the optimal numbers of relays and the impact on the system performance are further analyzed for different channel states. According to these results, the advantages and characteristics of this protocol are confirmed.

Cooperative base station for downlink multiple-input–multiple-output (MIMO) system is known as a critical radio technology for worldwide interoperability for microwave access (WiMAX) communications. This study proposes to use zero-forcing beamforming for cooperative base stations in a downlink multicell MIMO system. It is well known that beamforming requires perfect knowledge of channel state information (CSI) at the transmitter, and in practice the perfect CSI may not be available because of channel estimation errors. In this study, using approximate capacity loss analysis the authors are able to analyse the effect of channel estimation errors on system capacity in a cooperative base station system with zero-forcing beamforming. A power allocation policy is proposed to reduce the capacity loss under per base station power constraints. Numerical results show that the approximate capacity loss is very close to the real capacity loss, which can be reduced with the help of the power allocation policy.

Abstract Due to the TDMA (time division multiple access)/time division duplex (TDD) specialization in the uplink (UL) of code division multiplex access (CDMA) systems, some advanced techniques, such as smart antenna (SA) and multi‐user detection (MUD), are utilized conveniently in time division‐synchronous code division multiplex access (TD‐SCDMA) systems. These advanced techniques have great impacts on the capacity and radio resource management (RRM) schemes. In this paper, the UL capacity and load models specified for TD‐SCDMA systems are proposed, in which the impacts from SA and MUD techniques are considered, and the UL load can be estimated based on the total received power in the base station. According to the proposed theoretical capacity and load evaluation models, the call admission control (CAC) strategies suitable for TD‐SCDMA systems are presented. Since there are two kinds of SA schemes (i.e., tracking beam antenna (TBA) and switched beam antenna (SBA)) utilized in TD‐SCDMA systems, the efficient CAC algorithms suitable for these two SA schemes are designed and evaluated, which are based on principles of the interference increase estimation. All simulation results show that the proposed CAC strategies can work efficiently and improve performances of TD‐SCDMA systems dramatically. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract This paper proposes a novel protocol based on cooperative relaying between users. The protocol takes advantage of the fact that data traffic is bursty and silent periods exist between data bursts. Rather than wasting these idle time slots, users can take advantage of them to perform cooperative relaying, obviating the need for dedicated system resources for relaying, hence using the spectrum more efficiently. The benefit of this user cooperation for the higher network layers is analyzed. The system was modeled in terms of the maximum stability region and the maximum stable throughput. Our results show that our proposed protocol provides significant performance gains compared to conventional time‐division multiple‐access (TDMA) systems, as well as cooperative relaying protocols, namely selection relaying and incremental relaying. Copyright © 2009 John Wiley & Sons, Ltd.

Considering a wireless relay network composed of a source, several relays and a destination, a linear block network coding (LBNC) scheme is proposed to combat the lossy nature of wireless channel. The relays not only replicate the messages received but also perform network coding according to a linear block generator matrix. A special case of LBNC, parity-check network coding (PCNC) scheme is firstly theoretically analyzed and the explicit Average Outage Probability (AOP) and Block Outage Probability (BOP) are derived. For comparison, advanced relay cooperation (ARC), where relays only replicate and forward messages without coding them, is also presented and analyzed. Numerical results show that PCNC performs better than ARC when SNR is high and the s - r link is in good condition. For general LBNC scheme, it is very complicated to calculate the precise outage probability due to the difference of generator matrices, and we turn to simulation to evaluate the outage performance. The result shows that the performance of (M + C, M) LBNC can be estimated by ([M/C] + 1, [M/C]) and ([M/C] + 1, [M/C]) PCNC.

In this paper, capacity performance analysis is presented for multiple-input multiple-output (MIMO) relay channels with transmit antenna selection and maximal-ratio combining receive (TAS/MRC) in amplify-and-forward (AF) relay networks operating over flat Rayleigh fading channels. Assuming that the source and destination are equipped with Ns and Nj antennas, respectively, and communicate with each other with the help of a single-antenna relay, we derive the cumulative distribution function (CDF), probability density function (PDF) and moment generation function (MGF) for the system end-to-end SNR. Based on these, we then present closed-form analytical expressions for the calculation of the ergodic capacity and outage capacity for the AF MIMO relay channels with TAS/MRC. Numerical results show that all the derived analytical expressions are very tight and are valid for evaluations of the ergodic and outage capacity under various antenna configurations. The results also show how the number of transmit antennas and receives antennas affect the overall channel capacity under fading channel environments.

Considering a wireless relay network composed of a source, several relays and a destination where the source transmits data packets to the destination through both direct and relay paths, a multi-timeslots network coding (MTNC) scheme is proposed to combat the lossy nature of wireless channel where the relay nodes with satisfying relay-destination (r - d) channel state firstly perform network coding on the successfully decoded data packets received from the source at previous timeslots and then forward the coded version to the destination. We focus on three metrics - Ergodic capacity, outage probability and symbol error rate to evaluate the performance of MTNC scheme and the replication-based relay (RR) scheme where a "best" relay node is selected out to replicate and forward the data packet. Precise closed form expression of ergodic capacity, tight estimated closed form expression of outage probability (OP) and symbol error rate (SER) are derived for both schemes, and also numerical results are shown to prove the gain of network coding. However, with increasing of the relay number, the improvement of outage probability and SER is limited.

In this paper, we investigate resource allocation for decode-and-forward (DF) opportunistic cooperative networks, where the availability of the direct link between the source and the destination has been taken into consideration. In addition to separate power allocation and location optimization, we also consider joint resource allocation to minimize the outage probability. Under Rayleigh fading channels, the closed-form solution is derived for both separate and joint resource optimizations. Numerical results show that the proposed optimized systems outperform the unoptimized ones, and the minimum outage probability can be achieved by joint power-location optimization.

Composite design for energy minimization across PHY/MAC layers of the protocol stack as well as the underlying hardware in multi-hop wireless network is presented. We use truncated ARQ for two-fold purpose i) initialization of cooperative communications without CSI overhead and ii) to provide time diversity for MRC reception when the channel coherent time is comparable to packet transmission time. Alamouti and Tarokh space--time block codes are used to realize 3/4 rates codes, MQAM modulation, transmit, and receive nodes, and MAC layer truncated ARQ for error rate and energy consumption performance of cooperative communication. The convexity of average energy consumption per packet function is proved under certain conditions that can be solved by Lagrange multiplier method.

We propose a forwarding scheme by superposition coding for wireless relaying, in which a relay will not forward a message to a destination if the destination has successfully decoded the message directly from a source. In our scheme, flag signal and data signal are superimposed and transmitted by the source, and according to whether the flag signal has been successfully decoded by the relay, the relay can determine whether to forward the data signal to the destination. Numerical results show that our scheme can outperform classical decode-and-forward cooperation scheme in terms of spectral efficiency and outage probability.

This paper investigates a simple and effective power allocation method to maximize the Effective Configuration Duration(ECD) in wireless sensor networks, which aims to minimize the signaling overhead needed to perform relay nodes selection, Cooperative Set update and power allocation, resulting in obvious power saving and lifetime extension. Compared with traditional allocation schemes, this method jointly considers the residual energy of sensors and the mean of channel power gains, thus limits the feedback burden and increases the topology stability. Two classical power allocation methods are evaluated for comparison, and numerical results show that the proposed scheme can significantly extend ECD over a wide range of parameter values.

Hybrid automatic repeat request (HARQ) protocol typically operates in multi-process stop-and-wait (SAW) mode to fully utilize channel capacity. A prior packet, due to retransmission(s), may arrive at the receiving side later than the packets that follow it but use other HARQ processes, thus leading to out-of-order reception. Medium access control (MAC) layer is responsible for packet reordering operations so as to offer in- sequence delivery to upper layer. An arriving packet should be temporarily stored in the reordering buffer, if there is at least one prior packet that has not arrived yet. Nevertheless, protocol stalling may occur if a packet is discarded at the transmitter due to excessive failed retransmissions, or if a negative acknowledgement (NACK) signal is inversed as an acknowledgement (ACK). A timer-based mechanism is suggested by 3GPP to handle protocol stalling. In this paper, we propose an adaptive timer- based stalling avoidance mechanism as the enhancement to the current 3GPP specifications. In our proposal, the duration of the timer is set or updated through dynamic monitoring on the status of each HARQ process. We show with illustrative examples and simulations that the proposed mechanism reduces considerably extra packet delay incurred during reordering operations.

We consider a two-hop relaying network in which orthogonal frequency division multiplexing (OFDM) is employed for the source-to-destination, the source-to-relay and the relay- to-destination links. Amplify-and-forward (AF) and decode-and- forward (DF) policies are both discussed with or without two-hop diversity, respectively, for the relaying network with a sum-power constraint. An unified approach is used for optimal power allocation in the four different relaying scenarios. First, equivalent channel gains are developed for any given subcarrier pair in each scenario, and then optimal power allocation can be obtained by applying the classic water-filling method. Moreover, we provide the proof to the optimality of sorted subcarrier pairing for AF and DF relaying without diversity, which, combined with optimal power allocation, can offer further performance gain.

Network coding is seen as a promising technique to improve network throughput. In this paper, network coding in cooperative relay networks and user cooperative networks is investigated and an optimized power allocation scheme is provided. By comparing with traditional relay forwarding in a simple two-user scenario, network coding shows a better system performance with a lower hardware cost and higher spectral efficiency, and this scenario can be extended to general multi-user environment without much cost.

An energy efficient cooperative MIMO selection scheme is proposed for uniform load distribution in cluster based wireless sensor networks. The intrinsic data flow direction in multi hop cluster based sensor networks causes uneven load distribution in the network. The transit clusters and the clusters near the base station carry more network traffic than the other clusters. Cooperative MIMO can artistically reduce the per bit energy consumption, Space-Time Block Codes are designed to achieve maximum diversity for a given number of transmit and receive antennas with very simple decoding algorithm. In radio fading channel, STBC require less transmission energy than SISO technique for the same Bit Error Rate and can be employed practically in Wireless Sensor Networks by using the cooperative MIMO scheme. Considering Alamouti and Tarokh space-time block codes, the number of antennas at both the transmission and the reception sides are selected with respect to the cluster load. By using cooperative MIMO transmission instead of SISO, it is shown that the load based adaptive selection of cooperative nodes in clusters renders uniform energy consumption in the network.

Installing multiple antennas at user equipments (UEs) may pose great challenges. In virtual multiple-input multiple-output (MIMO) concept, multiple single-antenna UEs can be grouped to transmit signals at the same time. Their signals can be separated at the base station (BS) by the same methods already used in conventional point-to-point MIMO scenarios. An important problem in virtual MIMO application is which users can be scheduled simultaneously to achieve a good balance between various system targets, e.g. aggregate throughput and user fairness. In this paper, a novel scheduling scheme is proposed which combines the advantages of proportional fair and maximum rate rules, based on successive interference cancellation (SIC) MIMO receiver architecture. Simulation results show that the proposed scheme can achieve a good tradeoff between system throughput and user fairness. Compared with other scheduling schemes already proposed in virtual MIMO context, e.g., orthogonality pairing scheduling (OPS), the proposed scheme can be justified by its low computational complexity and flexibility.

Multimedia broadcast/multicast service (MBMS) is an important part of the UTRAN evolution and supports downlink streaming and download-and-play type services to large groups of users. For enhanced MBMS (E-MBMS) under 3GPP long term evolution (LTE) system, there are two ways to transmissions being performed: multi-cell transmissions and single-cell transmissions. One requirement identified to be supported is that the capability of the network to detect at least one MBMS user interested receiving one given MBMS service in the cell which belongs to above scenarios. It is significant to avoid unnecessary MBMS transmission in a cell where there is no MBMS user especially for single-cell transmissions mode. In this investigation, a low complex method is discussed to solve the detection on MBMS interested users in one cell and we also introduce code diversity strategy into the feedback signal transmission. Theoretical analysis and simulation results all show that the efficiency of proposed detection scheme is obvious which can dramatically reduce the average MBMS service polling time and control overhead.

We consider a multi-hop wireless sensor network in which a large number of sensor nodes are grouped into cooperative clusters. Multi-hop transmission is carried out between the source and the destination nodes by concatenating consecutive cluster-to-cluster hops. For each hop, both the transmit and the receive clusters can exploit node cooperation such that cooperative distributed multiple-input multiple-output (MIMO) channels can be formed. As proposed in [1], a time-division protocol is employed for transmissions within a cluster and between clusters, i.e., the intra-cluster slot is used for broadcasting within the transmit cluster, and the inter-cluster slot is used for transmissions between clusters. Distinguished from the scheme in [1] that space-time codes (STC) are utilized for inter-cluster transmissions, random beamforming is proposed in this paper, which is shown to outperform STC in term of energy efficiency provided that the number of nodes within receive cluster is adequately large. We demonstrate that even with moderate number of nodes within receive cluster, e.g., 10 nodes, random beamforming can offer higher energy efficiency for inter-cluster links, at 5% outage rate. The "random" nature incorporated in random beamforming scheme assigns the clusterhead node within receive cluster in an alternate manner, thus balancing energy consumption within receive cluster.

In practical systems, due to the time-varying radio channel, the channel state information (CSI) may not be known well at both transmitters and receivers. For most of the current multiuser multiple-input multiple-output (MIMO) schemes, they suffer a significant degression on the performance due to the mismatch between the true and estimated CSI. To alleviate the performance penalty, a robust downlink multiuser MIMO scheme is proposed in this paper by exploiting the channel mean and antenna correlation. These channel statistics are more stable than the imperfect CSI estimation in the time-varying radio channel, and they are used, in the proposed scheme, to minimize the total mean squared error under the sum power constraint. Simulation results demonstrate that the proposed scheme effectively mitigates the performance loss due to the CSI mismatch.

The energy saving mechanism is very important for supporting the mobility and extending the battery life in the wireless packet access network. For example, the sleep-mode has been standardized in both IEEE 802.16e and 3G long term evolution (LTE) systems. According to the definition in the standards, an adaptive energy saving mechanism (AESM) for enhancing the standardized energy saving mechanism (ESM) is proposed, in which the sleep interval is determined by both the system load and traffic properties. The proposed AESM determines the initial sleep interval and the period sleep interval adaptively according to the proposed parameters gamma and Psi, which are the multiplying step size of the sleep interval and the traffic's property respectively. In order to demonstrate the proposal validity, the theoretical analysis based on the Markov chain model is presented, and the total energy consumption for the heavy load and the light load is compared. Simulation results illustrate that the proposed scheme can achieve a good balance between the energy saving efficiency and the transmission response time delay.

In TD-SCDMA systems, the multi-user detection (MUD) and adaptive smart antenna techniques are adopted in both uplink and downlink. Considering the complexity of cancelling inter-cell interference, the technique of MUD in this paper mainly focuses on the interference suppression of intra- cells. The adaptive smart antenna technique is adopted to suppress the interference from both intra- and inter-cells. Since these two motioned techniques are not adopted in WCDMA and cdma2000 systems, the corresponding capacity and load evaluating models for TD-SCDMA systems must be reinvestigated. In this paper, a theoretical analysis model for CDMA capacity constraints is presented firstly when employing the adaptive smart antenna and MUD techniques in both uplink and downlink, in which the factors of average beamforming interference and intra-cell MUD factors are proposed for evaluating the impacts of adopting these key techniques. Based on the theoretical model, the pole capacity and load estimation models are deduced. The simulation results demonstrate the theoretical analytical models specified for TD-SCDMA systems available. According to our proposals, the TD-SCDMA network planning and optimization can be processed efficiently.

In this paper, we propose a new multimedia broadcast network architecture, in which the cellular base stations act as digital fixed relays and cooperate with the broadcast station to deliver messages to the whole network, forming the so-called cooperative broadcast network . This concept fully exploits the benefits of cooperative relaying . Through the evaluation of coverage and throughput by theoretical analyzing and simulation, it is consistently shown that this scheme can dramatically extend the broadcast station coverage range without any capacity penalty, and it can also provide very high data rate service in an almost-ubiquitous manner throughout the network. Also we can come to the conclusion that the relay strategy is especially efficient for mobile terminals at the edge of the cell that would have poor channel conditions.

Frequency allocation is a critical issue for cellular relaying networks. In this paper we present spectrum-efficient frequency allocation policies that are applicable for fixed two-hop cellular relaying networks. The frequency allocation problem is decomposed into two sub-problems: inter-cell and intra-cell frequency allocation. The goal is to make more users to be served with satisfactory service quality. Through numerical analysis, the basic inter-cell frequency allocation scheme is proposed, including frequency reuse pattern and reuse factors for each type of links. Then we propose a method to realize safe intra-cell frequency reuse, in the sense that higher spectrum efficiency is achieved through this tighter frequency reuse while the signal- to-interference (SIR) quality for the links of concern is still guaranteed. Simulations show that our schemes are beneficial to obtain more satisfied users and low outage ratio, when compared to the cases without intra-cell frequency reuse scheme and the cases with fixed intra-cell frequency reuse scheme.

The paper investigates the advantages of employing spatial reuse on the deployment of relay stations in a multi-hop network. To quantify the gain and interference from spatial reuse, the concepts "interference level" and "reuse factor" are introduced innovatively. By comparing the performance of relay-based multi-hop systems with conventional single-hop systems, we conclude in this paper that a large number of relay stations can lead to better spectral efficiency if the distance is long enough. The relationship between optimal number of hops of a multi-hop chain and distance from the source station to the destination station is also derived.

The inter-cell interference in the downlink for IEEE 802.16 wireless metropolitan area network (MAN) mainly depends on the distance between the base station (BS) and the subscriber station (SS). In order to improve the spectrum efficiency and make the frequency reuse factor approach 1.0, the wireless scheduling for supporting the diverse quality of service (QoS) should consider the other-cell interference. In this paper, the wireless packet scheduling algorithms are based on the jointly considering inter-cell interferences, service status, and channel conditions. The advanced scheduling algorithms are proposed for Unsolicited Grant Service (UGS), real time polling service (rtPS), non real time polling service (nrtPS), and best effort (BE) services. Numerical results reveal that the proposed interference coordination based scheduling algorithm improves the overall network throughput, provides an efficient user throughput, and guarantees the diverse QoS requirements.

The inter-cell interference in the downlink for IEEE 802.16 wireless metropolitan area network (MAN) mainly depends on the distance between the base station (BS) and the subscriber station (SS). In order to improve the spectrum efficiency and make the frequency reuse factor approach 1.0, the wireless scheduling for supporting the diverse quality of service (QoS) should consider the other-cell interference. In this paper, the wireless packet scheduling algorithms are based on the jointly considering inter-cell interferences, service status, and channel conditions. The advanced scheduling algorithms are proposed for Unsolicited Grant Service (UGS), real time polling service (rtPS), non real time polling service (nrtPS), and best effort (BE) services. Numerical results reveal that the proposed interference coordination based scheduling algorithm improves the overall network throughput, provides an efficient user throughput, and guarantees the diverse QoS requirements.

In this paper, channel aware adaptive resource allocation scheme in two-hop wireless relay networks (WRNs) is proposed to achieve maximum system throughput and better delay performance. Both the routing selection and resource scheduling algorithm are designed to accomplish the resource allocation scheme. Considering the network throughput, a routing algorithm is proposed to select the optimal RS based on an optimization problem. A fairly resource scheduling algorithm based on the Signal-to-Noise-Rate (SNR) is provided, which combined both the link and data scheduling. Both the routing and scheduling algorithms are designed with channel state considered. Theoretical analysis and simulation results show our proposed channel aware resource allocation scheme is efficient and can enjoy a good network performance in two-hop WRNs.

The capacity of relay channel is determined not only by the number of relays but also the relations among them. Cooperation among relays will greatly improve the network capacity by further reducing information uncertainty. Based on this theory, this paper introduces cooperative two-relay channel models with or without path loss, as well as the method for their capacity analysis. In these models, the work mode of the relays has two schemes, that is, the two relays select the same or different codebooks respectively. On calculating the capacity, we will analyze it without path loss firstly. Then based on this very result, we will further analyze the capacity with path loss. A numerical example without path loss is given in order to certify the result. It shows that the capacity is confined to the combination of the broadcast channel and multi-access channel and by finding the best way of power allocations we can achieve the maximum channel capacity.

Many system load evaluations have been proposed, and some applications, such as admission control strategies, have been discussed. It is observed, however, that, due to the TDD-CDMA specialization in the uplink (UL), i.e., multiuser detection (MUD) technology is used and the transmission is discontinuous per frame, all those schemes which are based on the FDD mode should be carefully investigated for the TDD system. The well-known formulation of UL background noise rise (BNR) for FDD-CDMA is modified and enhanced, and a modified BNR is presented for TDD-CDMA. Pole capacity, load and capacity estimation of the UL based on modified BNR are all described. The distribution of the parameter, j, which is used to define the ratio of other cell to own cell interference, is discussed based on the picocell scenario. Finally, in order to demonstrate the efficiency of the load strategy for TDD-CDMA, an admission control strategy based on modified BNR is evaluated and simulated. All works demonstrate that modified BNR gives a very good representation for capacity and load estimation in TDD-CDMA systems.

Compared to uplink (UL), the analysis on downlink (DL) is much more challenging because of the DL power allocation problem. In this paper, we propose some new theoretical approaches to investigate the DL system performance. A new investigation approach for the DL pole capacity, which is based on the total transmission power, is presented firstly. Additionally, a simple theoretical analysis in the downlink power increase estimation due to new or modified bearer for real and nonreal time traffic service is described. Furthermore, an advanced downlink power allocation, based on the dynamic control of the maximum code power, is proposed. An advanced static system level simulator to evaluate capacity is developed. By comparing our analytical results with simulations, we show that our analytical approaches have very good match with simulation results.

This paper presents a handover performance analysis suitable for implementation in TDD-CDMA cellular network. Firstly, RSCP (received signal code power)-triggered and SIR (signal to interference ratio)-triggered hard handover measurement criteria for TDD-CDMA are analyzed theoretically and their performances are compared by simulations. Secondly, the handover parameter T/spl I.bar/margin, which is defined as the margin of measurement value used in adding/dropping users to/from active set or full candidate set to avoid ping-pong effect, is carefully set and addressed. Thirdly, the optimum value of measurement reporting period is investigated. Finally, the influence of measurement error is described. As outcome of these analysis and simulations, some simulation results are illustrated and the conclusion is drawn that SIR-triggered scheme is more available to make measurement criteria. Simulations are carried out in a hexagonal macro cellular network with a dynamic system level simulator.