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.

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.

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.

The advent of multi-layer reconfigurable intelligent surfaces (RIS) has introduced new possibilities for integrating communication, sensing, and computation within the wave domain. Existing models, however, often rely on simplified mathematical abstractions, failing to accurately represent the physical behavior of multi-layer RIS-assisted systems. To bridge this gap, we present a physically consistent model for multi-layer RIS-enabled multi-user communications. The model is based on electromagnetic (EM) theory and Chu’s antenna principles, incorporating mutual coupling (MC) effects through an impedance-parameterized multi-port circuit framework. Contrary to the conventional view of MC as a performance-limiting factor, our analysis demonstrates that, when properly managed, MC can enhance system efficiency. To this end, we design an optimization algorithm that iteratively updates the RIS phase control matrix and transmits power using gradient ascent combined with successive convex approximation (SCA), ensuring that physical constraints and MC effects are fully integrated. Simulation results demonstrate the proposed model’s ability to accurately predict system behavior and highlight the potential performance gains achievable through MC-aware design, offering a practical foundation for optimizing multi-layer RIS in advanced communication systems.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

5G non-public network (NPN) can provide customized and dedicated network services for various vertical industries. The operation of 5G NPN is a crucial aspect for the deployment and application of 5G NPN. This paper studies the development of 5G NPN operation. Furthermore, this paper proposes a three-stage evolution path, framework and the guaranteed requirements for 5G NPN operation. Some examples are also provided to achieve NPN optimization goal by the framework. The paper provides insights and guidance for the vertical industries of 5G NPN operation, as well as suggests potential directions for future work on 5G NPN operation.

In recent years, 5G/5G-A technology has fast developed and found widespread deployment, meeting the diverse requirements of application scenarios across various industries. In this paper, we introduce the principle and advantages of 5G/5G-A private network. Then, we introduce the construction of 5G/5G-A private network. Furthermore, we design an intelligent operation system of 5G/5G-A private network, which includes six key modules with over twenty functionalities. This intelligent operation system can effectively support the operation of 5G/5G-A private network. Lastly, this paper introduces the 5G/5G-A private network applications in a realistic vehicle factory.

In this paper, we present a two-layer precoding approach for integrated sensing and communication (ISAC) multi-input multi-output (MIMO) systems. The proposed approach comprises an outer layer precoder that leverages channel state information for beam scheduling and reduces the dimensionality of the channel matrix, thereby improving the achievable sum rate. In addition, the inner layer precoder incorporates a beamforming approach for the sensing function, integrating MIMO radar and multi-user MIMO communication systems. This approach helps to satisfy the signal-to-interference-plus-noise ratio requirements for communication and utilizes the minimum mean square error criterion to approximate the beam pattern of the MIMO radar, achieving a superior sensing performance. Our research demonstrates the capability of improving the performance of ISAC MIMO systems.

Abstract Vehicular platoons are among the most advanced driving assistance systems that may generate considerable fuel savings and increase traffic efficiency. However, communication between vehicles in a platoon is always vulnerable to eavesdropping due to the broadcast nature of wireless channels. To address this issue, we investigate security issues from the physical layer perspective for multi-hop vehicular platooning. The dynamic shielding secured transmission scheme is proposed to guarantee the confidential transmission of private information. Specifically, the neighboring vehicles can alternately act as friendly shielders, which transmit jamming signals to interfere with the eavesdroppers without knowing the channel state information, and thus the secrecy capacity would increase. The mathematical derivation of secrecy capacity is obtained for performance analysis. Meanwhile, the numerical results verify the properties, efficiency, and adaptability of the proposed scheme.

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.

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.

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.

Fog radio access networks (F-RANs), in which the fog access points are equipped with communication, caching, and computing functionalities, have been anticipated as a promising architecture for enabling virtual reality (VR) applications in wireless networks. Although extensive research efforts have been devoted to designing efficient resource allocation strategies for realizing successful mobile VR delivery in downlink, the equally important resource allocation problem of mobile VR delivery in uplink has so far drawn little attention. In this work, we investigate a mobile VR F-RAN delivery framework, where both the uplink and downlink transmissions are considered. We first characterize the round-trip latency of the system, which reveals its dependence on the communication, caching, and computation resource allocations. Based on this information, we propose a simple yet efficient algorithm to minimize the round-trip latency, while satisfying the practical constraints on caching, computation capability, and transmission capacity in the uplink and downlink. Numerical results show that our proposed algorithm can effectively reduce the round-trip latency compared with various baselines, and the impacts of communication, caching, and computing resources on latency performance are illustrated.

5G network consumes huge investment cost, including 5G network construction, 5G network operation and maintenance etc. Therefore, China Unicom and China Telecom take the initiative to jointly build the 5G network (wireless network sharing, independent construction mode of core network). Its aim is to reduce 5G overall investment cost, and rapidly realize the continuous and wide-area 5G service capability, as well as improve the network efficiency and asset operation efficiency. This paper focuses on the automatic data configuration model of 5G co-construction and shared base stations. By interacting with the core network and wireless network, this model can identify and match different 5G network modes such as SA and NSA (including dual-anchor scenarios and single-anchor scenarios). On this basis, the data required for automatic activation of the shared base stations is obtained from the wireless side and automatically configured on the core network side. This technology meets the practical needs of both sides for fast and efficient construction of 5G network. It can realize the automatic opening and fast on-line of co-construction and shared BS on the side of the core network.

EPS-Fallback is generally adopted for voice service in the coexistence period of 4/5G network. Its process is very complex and prone to failure. How to guarantee users perception is an important challenge. Based on multi-resources data, this paper proposes a fault diagnosis method for EPS-Fallback. It can effectively improve the fault diagnosis ability and the efficiency of network operation and maintenance. Also, it can save labor costs.

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.

In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

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.

Ultra-dense network (UDN) is one of the enabling technologies in the fifth generation (5G) wireless communications, and the application of joint transmission (JT) is extremely important to deal with severe inter-cell interferences in UDNs. However, most of the current works done in performance analysis on JT schemes in the literature were based largely on simulation results due to the difficulties in quantitatively identifying the number of desirable and interfering transmitters in a UDN setup. In this work, we are motivated to propose an analytical approach to investigate the performance of JT schemes with a unified approach based on stochastic geometry, which is in particular useful for studying different JT schemes as well as conventional transmission schemes without JT. Using the proposed approach, we can unveil the statistical characteristics (i.e., expectation, moment generation function, and variance) of desirable signal and interference powers of a given user equipment (UE), and thus the system performances, such as average signal-to-interference-plus-noise ratio (SINR) and area spectral efficiency, can be evaluated analytically. The simulation results are illustrated to verify the effectiveness of the proposed approach.

In a traditional complementary coded CDMA (CC-CDMA) system, signals are spread using the direct sequence (DS) technique to suppress multipath interference (MI) and multiple access interference (MAI). However, DS spreading suffers from very poor bandwidth efficiency. This paper proposes a scrambling-based CC-CDMA system, namely complementary coded scrambling multiple access (CCSMA). A CCSMA system offers a unified platform to integrate CC-CDMA, multiple-input multiple-output (MIMO), and scrambling technique seamlessly. Utilizing complementary codes in scrambling modulation, a CCSMA system is able to suppress MI and MAI effectively. With the help of space domain scrambling, the CCSMA system offers much higher bandwidth efficiency than a CC DS-CDMA system. Bit error rate and system capacity are evaluated explicitly in this paper, verifying that CCSMA is a viable multiple access technique for providing high capacity in downlink MIMO channels.