In the context of Internet of Things (IoT) networks, this work investigates an active reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system and establishes an integrated analytical frame-work that rigorously characterizes both the inherent double-fading effect of RIS-assisted propagation and the power-gain coupling introduced by the active RIS. A weighted sum-rate (WSR) maximization problem is then formulated under the constraints of access point (AP) power budgets, the RIS power consumption limitations, and the receiver energy harvesting requirements. To manage the resulting highly non-convex structure, the original problem is equivalently reformulated as a weighted minimum mean squared error (WMMSE) minimization problem, which enables the development of an efficient alternating optimization algorithm. At each iteration, the MMSE decoding vectors are firstly updated in closed form, while the energy signal covariance matrices, the AP information beamformers, and the active RIS reflection coefficients are subsequently optimized via semi-definite programming, successive convex approximation, and augmented Lagrangian method, respectively. Extensive simulation results demonstrate that the adoption of an active RIS effectively mitigates the double-fading effect and achieves performance comparable to that of passive RIS with significantly fewer reflecting elements, while the proposed joint optimization strategy yields substantial WSR gains and further enlarges the performance gap between the optimized active and passive RIS schemes.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems and simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) are considered as promising technologies for enhancing the performance of wireless communication systems. In this paper, we investigate the performance of a STAR-RIS-aided CF-mMIMO system under channel aging, which has been ignored in previous studies. Firstly, we propose a linear minimum mean squared error (LMMSE) aggregated channel estimator and formulate statistical channel state information (CSI) properties for the subsequent system performance analyses. Then, closed-form expressions for the uplink and downlink spectral efficiencies (SEs) of the STAR-RIS-aided CF-mMIMO system under channel aging are explored, where for the uplink, the two-layer large-scale fading decoding (LSFD) and the simple centralized decoding (SCD) are utilized, respectively, and for the downlink, the maximal ratio (MR) precoding and fractional power control (FPC) are adopted. Moreover, the optimal LSFD coefficients that maximize the uplink SE is presented. Afterwards, for further enhancement of SEs, a novel optimization scheme is presented, which optimizes the passive beamforming (PB) of the STAR-RIS to minimize the normalized mean square error (NMSE) of the aggregated channel estimation. The simulation results reveal that the STAR-RIS-aided CF-mMIMO system achieves superior uplink and downlink performance compared to both the RIS-aided CF-mMIMO system and the conventional CF-mMIMO system without RIS over aging channel. Furthermore, the results show that the PB optimization can significantly reduce the NMSE of channel estimation, thereby improving the estimation accuracy and SEs under channel aging.

The cellular Internet of Things (IoT) enhanced by federated learning (FL) is a potential paradigm to leverage the vast amount of data generated by the IoT devices and offer various intelligent applications. Through its distributed learning manner, the privacy and delay problems of the learning process are well handled. Nevertheless, in the cellular IoT, FL requires multiple rounds of model parameters exchanging between the parameter server and multiple clients over unstable wireless links, which largely constrains the communication efficiency. Regarding this problem, we propose the training data cost ratio to evaluate the communication efficiency, and then by maximizing this metric, client scheduling, transmitting power, and bandwidth are jointly formulated. The formulated problem is decomposed via problem transformation and derivations, and then, the Lagrange method and greedy-based algorithms are developed to solve the subproblems efficiently. Simulation results verify the advantages of our algorithm in communication efficiency improvement. Moreover, it reveals that the proposed metric and joint optimization substantially obtain superior tradeoff between learning performance and resource consumption compared to the client number oriented optimization.

Thanks to the customization of channel propagation, reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (MIMO) is recognized as a competitive candidate technique for the future communication system. However, only the limited performance gain can be afforded by passive RIS due to the double-fading effect in RIS-aided links. In this article, we consider the CF massive MIMO system with the assistance of the active RIS, which is capable of reflecting and amplifying the incident signal, to enable the Internet of Things network. We analyze the tradeoff between the number of active RIS reflecting elements (REs) and the amplification coefficient. Considering the power constraint at the active RIS, we formulate a sum-rate maximization problem to jointly optimize the user transmission power, the receive beamforming, and the RIS reflecting precoding. Since the original problem is nonconvex, we decouple it into three subproblems and then design an alternating optimization algorithm to solve them iteratively. Using the Lagrangian dual reformulation and generalized Rayleigh quotient theory, we derive the closed-form solutions for both the user transmission power and the uplink receive beamforming. We also develop a low-complexity method to acquire the RIS reflecting precoding based on the primal-dual subgradient theory. Compared to the passive RIS, the active RIS can significantly improve the system performance with fewer REs. Moreover, the proposed optimization scheme effectively mitigates the drawbacks of the active RIS under the high-transmission power regime and enhance its benefits. Finally, the proposed alternating optimization algorithm is validated by numerical results.

In cell-free massive multiple-input multiple-output (CF-mMIMO) systems, seamless communication coverage is achieved through the dense deployment of numerous access points (APs), significantly enhancing spectral efficiency (SE) for users and overall system capacity. However, the implementation of this approach demands substantial deployment costs and unavoidably necessitates the use of non-ideal hardware. This paper investigates the achievable rate of users in the uplink CF-mMIMO systems that employ nonlinear power amplifiers (PAs) and low-resolution analog-to-digital converters (ADCs) at user equipment (UE) and APs, respectively. In particular, we derive a closed-form expression for the achievable uplink user rate and conduct a comprehensive analysis of various factors, including the number of APs, UE density, number of AP antennas, and ADC resolution. To mitigate the interference among UEs and maximize the sum rate, we propose a graph neural network (GNN) assisted actor-critic algorithm (DMAGNN-AC) for power allocation. The established framework overcomes the representation bottleneck of DRL in high-dimensional unstructured state spaces and provides physically interpretable feature embeddings. In comparison to the full power output, the proposed power allocation scheme is capable of doubling the rate. Furthermore, to address the detrimental impact of low-resolution ADCs on the rate, we develop an enhanced algorithm, multi-agent deep Q-integrated network (MADQIN), which optimizes the allocation strategy of ADC resolutions. Finally, the effectiveness of the proposed schemes is validated by the presented simulation results.

This paper concentrates on the synergetic effect of the hybrid multicast/unicast/device-to-device (D2D) transmission for downlink cell-free massive multiple-input multiple-output (MIMO) Internet-of-Things (IoT) systems. By leveraging on the acquired imperfect channel state information (CSI) both for multicast, unicast, D2D links, particularly, we first derive the closed-form solutions for the multicast, unicast, and D2D transmission links, respectively. After that, based on the practical power consumption model, the achievable sum energy efficiency (EE) analysis is conducted as well by exploiting the achievable sum rate. At last, extensive simulation results are provided to validate the performance of the proposed framework. The obtained results are given to provide promising preliminary insights on the potential of deploying multicast/unicast/D2D in the cell-free massive MIMO topology. It is revealed by the above analysis that some guiding rules of the practical deployment of future. It is noteworthy that when we select the 5, 6, and 8 bits, we can achieve the maximum sum EE, the tradeoff from the total power consumption to the sum rate, and the maximum sum rate, respectively.

As one of the most critical tasks currently, it is essential to excavate the status update performance. In this paper, we combine access control and pilot design to minimize average age of information (AoI) in cell-free (CF) massive multiple-input multiple-output (MIMO) system with grant-free (GF) random access, where the positions of both the access points (APs) and machine-type communication devices (MTCDs) are geographically distributed as independent Poisson point processes (PPPs). Based on the establish two-disk computation model, we frist formulate the access successful probability by leveraging the stochastic geometry for GF transmission. Then, the closed-form expression of the average AoI is approximately derived. The derived results enable us precisely quantify the impact of network parameters on the system-level performance. To minimize the average AoI, a joint access control and pilot design scheme is proposed. Finally, simulation results are provided to furnish invaluable insight into system performance and certificate the validity of the proposed algorithm.

In the time-division-duplexing (TDD)-based cell-free (CF) massive multiple-input multiple-output (MIMO) system, the channel reciprocity needs to be recovered via a reciprocity calibration operation due to the random circuit impact on the transceiver radio frequency. In this paper, we study the effect of the calibration error on the TDD CF massive MIMO system under non-ideal calibrations. Assuming the spatially correlated Ricean fading channel, we derive the closed-form expression of the downlink achievable rate, which takes both the channel estimation error and the calibration error into account. Some novel insights of the calibration error in the CF massive MIMO system are gathered from the analytical results. It is shown that the downlink achievable rate is more sensitive to the calibration error at the user side. In order to provide a uniformly good service for each user, we employ the geometric programming (GP) to solve the max-min power optimization problem to maximize the minimum user rate. Additionally, we utilize the scaled alternating direction method of multipliers to develop a calibration error-aware beamforming scheme to mitigate the impact of calibration errors, improving the downlink sum-rate. Numerical results demonstrate that the proposed GP-based algorithm significantly improves the 95%-likely per-user downlink achievable rate with a fast convergence behavior. Moreover, the proposed calibration error-aware beamforming scheme enhances the downlink sum-rate and outperforms other benchmark schemes.

We explore the synergistic advantages of integrating intelligent reflecting surface (IRS) with rate splitting multiple access (RSMA) in a downlink cell-free massive multiple-input multiple-output (MIMO) system to meet the stringent requirements of ultra-reliable and low-latency communications. Taking into account the estimation errors, statistical channel knowledge, finite blocklength, and spatial correlation among IRS elements, a tight closed-form expression for the achievable rate is derived, which serves as a tool for evaluating the achievable rate across various system configurations. To enhance the weighted sum-rate (WSR) while adhering to the latency and reliability constraints, we formulate a joint WSR maximization problem with respect to both IRS phase shifts and power control coefficients. Given the non-convex nature of this problem, we develop an alternating optimization strategy that decouples the original problem into two distinct sub-problems. Specifically, the IRS phase shift design is reformulated as a min-max normalized mean squared error problem, enabling an efficient closed-form solution, whereas the power control optimization is addressed using a geometric programming approach. Numerical results validate the synergistic gain of integrating IRS with RSMA in terms of achievable rate and demonstrate that the proposed optimization scheme significantly enhances the WSR while fulfilling the latency and reliability requirements.

Abstract Traffic volume is increasing dramatically due to the quick development of technologies like online gaming, on‐demand video streaming, and the Internet of Things (IoT). The telecommunications industry's large‐scale expansion is increasing its energy usage and carbon footprint. Given the desire to minimize energy consumption and carbon emissions, one of the most essential concerns of future communication networks is ensuring rigorous performance restrictions of IoT services while improving energy efficiency. In this regard, a convolutional neural network‐based hierarchical reinforcement learning approach is provided to lower total energy consumption and carbon emissions in the dynamic service function chaining situations. This method can more effectively lower energy consumption and carbon emissions when compared to other hierarchical algorithms based on conventional deep neural networks and non‐hierarchical algorithms. The suggested method is tested in three typical complicated networks with different network parameters to show its suitability in different network scenarios.

In this paper, we consider the cell-free massive multiple-input multiple-output (MIMO) system for supporting ultra-reliable and low-latency communication (URLLC) transmission, where a large number of access points (APs) serve a small number of users in the short packet regime. Assuming channel aging and channel spatial correlation, we derive the closed-form expression of the downlink achievable rate with the normalized conjugate beamforming (NCB). Under the goal of maximizing the minimum user rate, we formulate a max-min power optimization problem with a power constraint at each AP. However, it is challenging to solve this problem because the objective function is a complicated function of power coefficients. To tackle this difficulty, we use a path-following method to approximate the objective function to a logarithmic function and transform the polynomial constraint into a monomial. Thus, we can iteratively solve the original problem by reformulating it as a series of geometric programming problems. Numerical results verify the tightness of the closed-form expression for the downlink achievable rate in the short packet regime. Both channel aging and channel spatial correlation significantly degrade the system performance of CF massive MIMO URLLC systems. Moreover, Using NCB and the proposed max-min power allocation can effectively alleviate this impairment and improve the system performance.

In the context of Internet of Things, this letter considers a cell-free (CF) massive multiple-input–multiple-output (MIMO) system for ultrareliability and low-latency communication (URLLC) assisted by multiple reconfigurable intelligent surfaces (RISs). We derive the closed-form expression of the downlink achievable rate under multiple correlated RISs and pilot contamination. To mitigate the impact of pilot contamination and improve the fairness among users, we minimize the maximum normalized mean-squared error (NMSE) of the channel estimation by jointly optimizing the pilot power coefficient and the RIS phase shifts. Due to the nonconvexity of the original problem, we design an alternating optimization algorithm to solve the substitutable two subproblems using fractional programming and sequential convex approximation. Numerical results validate the proposed algorithm in terms of decreasing the maximum user NMSE and converging. Moreover, the 95%-likely per-user downlink achievable rate is also improved.

Unmanned aerial vehicles (UAVs) are widely recognized as crucial supplementary component of 6G networks. Owing to the key attributes of UAVs (mobility, flexibility, and adjustable altitude), UAVs can serve as flying base stations (BSs), flying relays and mobile terminals in order to expand the service coverage and derive more applications. Softwarization is regarded as dominant attribute of network architecture of 6G, mainly realized by network function virtualization (NFV) and software defined networking (SDN). In this paper, we concentrate on researching the resource orchestration and allocation of end-to-end (E2E) slice services in softwarized UAVs-assisted 6G terrestrial networks. Problem models of UAVs-assisted 6G terrestrial networks and E2E slice are firstly introduced. Then, the problem formulation of resource orchestration and allocation of E2E slice is presented. Afterwards, one novel framework design, abbreviated as ReOrcAll-UAVs-6G, is detailed. When receiving one E2E slice, our ReOrcAll-UAVs-6G checks the available softwarized resources. If having available softwarized resources, our ReOrcAll-UAVs-6G turns to serving the slice and fulfilling slice’s tailored resource demands. During the orchestration and allocation phase, wireless and wired resource requests of this slice are considered and executed. Evaluation work and gained results of ReOrcAll-UAVs-6G and selected approaches are illustrated and analyzed. Gained results reveal that our ReOrcAll-UAVs-6G achieves apparent performance advantage, comparing with all selected approaches.

This paper investigates the achievable rate and spectral efficiency (SE) of an uplink cell-free massive multiple-input multiple-output Internet-of-Things (mMIMO-IoT) network over Ricean fading channels, where both access points and user equipments (UEs) are equipped with multiple antennas. We derive tight closed-form expressions for the lower-bound achievable rate and SE under maximum ratio combining and imperfect channel state information (CSI). Moreover, we propose a target-signal-to-interference-plus-noise-ratio-tracking opportunistic power control (TOPC) algorithm with gradual soft UE removal to mitigate the effects of unsupported UEs. The proposed TOPC algorithm is fully distributed, as each UE updates its transmit power based on local CSI. Numerical results show that adding more antennas at the UEs can enhance the achievable rate, but may degrade the achievable SE due to the increased pilot overhead. Moreover, the Ricean fading channels offer much higher achievable rate and SE than the Rayleigh fading channels, and our TOPC algorithm exhibits satisfactory performance in various aspects.

With the rapid development of Internet of Things, edge computing, SDN/NFV and other network communication technologies, the demand for dynamic service function chain orchestration is increasing. Because the machine learning method based on neural network can flexibly predict and select the next optimal action according to the existing data model, the algorithm based on neural network has been more and more applied in the dynamic orchestration of service function chain in recent years. This paper analyzes the current research situation of service function chain orchestration algorithm based on neural network from two aspects of algorithm types and experimental simulation results, and puts forward the existing problems of service function chain orchestration algorithm based on neural network and the direction of further research and expansion in the future.

Multiple-input multiple-output (MIMO) communication system and reconfigurable intelligent surface (RIS) are widely accepted as two promising approaches towards the secure data transmission in 6G networks, including the aerial part. In this paper, we jointly research one RIS-aided cell-free massive MIMO system for 6G aerial networks where one RIS is deployed around access points (APs) and users. By deploying one RIS in cell-free MIMO system, we can create favorable propagation conditions in a low-cost way. We first formulate the max-min fairness problem in order to maximize the achievable rate among all the users. In order to tackle this problem, we propose a joint design framework for the transmit beamforming at APs and the phase shifts at RIS by alternating optimization. In the process of alternating optimization, the transmit beamfoming design (active beamforming) problem can be solved by a second-order-cone program (SOCP), the phase shifts design (passive beamforming) problem can be transformed into a semidefinite program (SDP) making use of a semidefinite relaxation (SDR). In addition, we propose an approximation projection for the discrete phase shifts design problem. The simulation results reveal that the proposed scheme brings a significant performance gain to the cell-free MIMO systems powered by an RIS compared with the case of no RIS.

Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated.

Mobile edge computing (MEC) is a promising paradigm to handle the mismatch between computation-intensive applications and resource-limited devices. Nevertheless, as most Internet of Things (IoT) terminals are battery-limited, the computation gain of MEC may be compromised due to insufficient battery energy for task offloading. Wireless power transfer (WPT) and full duplex (FD) communications are economical charging and transmission methods for battery-limited IoT terminals. However, when integrating wireless power transfer and FD into MEC, the incentive problem should be jointly addressed with task offloading, because the WPT facilities and their powered IoT nodes belong to different service operators. In this paper, we investigate the efficiency of WPT from the perspective of battery saving, and propose an efficient wireless powered task offloading and incentive mechanism in FD MEC-enabled cellular IoT networks. The battery saving efficiency, which addresses both the total cost of WPT and saved energy of battery, is proposed as the performance metric. By adopting this metric as the utility function of the network operator (NO), the task offloading and incentive problem are jointly formulated as a Stackelberg game. We then propose an efficient alternating direction iteration-based algorithm to solve its equilibrium efficiently. Simulation results demonstrate the benefits of our algorithm in battery saving by comparisons with utility oriented benchmarks. Moreover, it reveals the tradeoff between the utility of NO and battery saving, which verifies the positive effects of FD communications and WPT in improving the efficiency of battery saving.

Abstract Here, we investigate the precoding schemes for the downlink data transmission of a time‐division duplex cell‐free massive multiple‐input multiple‐output (MIMO) system with channel aging, which arises from the user mobility. Closed‐form spectral efficiency (SE) expressions of the downlink with the normalized conjugate beamforming (NCB), and the full‐pilot zero‐forcing (FZF), are derived, which are used for the analytical system performance evaluation. Then, a novel combinatorial precoding scheme with enhanced system SE performance, which adopts either NCB or FZF according to each user channel aging condition, is proposed. Moreover, a pilot allocation strategy is proposed to alleviate the extra interference brought by the combinatorial precoding scheme. Also, a statistical channel cooperative power control is employed to further improve the performance for all the above precoding schemes. Numerical results show that the proposed precoding scheme can substantially improve the average downlink SE.

Compared with 5G networks, 6G networks are guaranteed to provide various tailored end-to-end network services and emerging cloud-edge applications. Network slicing (NS) is regarded as the key enabler of 6G networks. Softwarization and virtualization technologies, such as software-defined networking and network function virtualization, are accelerating the way toward NS of 6G networks. The resource allocation issue in 6G NS is very crucial, worthy more research attention. In this article, we propose one efficient resource allocation algorithm, labeled asTailoredSlice-6G, so as to realize the tailored slices in 6G. When receiving one slice request, ourTailoredSlice-6Gwill identify the slice resource type in the first place. Then, ourTailoredSlice-6Gwill select its most suitable subalgorithm to do the resource allocation and slicing deployment. Each type of slice corresponds to its specific resource allocation subalgorithm, inserted in theTailoredSlice-6Galgorithm. In addition, each subalgorithm inTailoredSlice-6Gis guaranteed to run within polynomial time. Thus,TailoredSlice-6Ghaving the potential to be promoted to real networking application. To highlight the merits ofTailoredSlice-6G, we do the comprehensive simulation. Simulation results vividly reveal that ourTailoredSlice-6Goutperforms the selected heuristics that are representative in the literature.

Energy harvesting (EH)-enabled cellular Internet of Things (IoT) is a promising solution to handle the charging and accessing of massive IoT nodes. However, limited by the high-frequency band of future 5G, the radius of the small base station (SBS) is reduced, hence greatly increasing the cost of the network operators (NOs). In this article, we consider the joint cell association, cell sleeping (CS), and incentive decision problem for EH-aided cellular IoT with full-duplex (FD) self-backhauls. We formulate a Stackelberg game to investigate the coordination between the utilities of NO and energy transmitters (ETs), where both the features of FD self-backhauls and CS are introduced to reduce the expense of NO. We then propose an alternative direction algorithm to solve the equilibrium of the game efficiently, where the relationship of the formulated constraints and variables are utilized to transform the original problem into two subproblems. We propose a two-level Lagrangian relaxation to solve the first subproblem, while the other is proved to be convex and solved by an efficient iteration. Simulation results demonstrate the benefits of our algorithm in utility improvement and expense reduction. Moveover, it shows that our algorithm can obtain high efficiency by adjusting the tradeoff between the number of active SBS and transmitting power of ETs according to the network deployment.

In this article, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet of Things (IoT) networks relying on the cell-free massive multiple-input–multiple-output (CF-mMIMO) technique is investigated. In such a network, the access points (APs) beam the radio-frequency (RF) energy toward IoT sensors during the DL wireless power transfer phase. Tight closed-form expressions for DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are, respectively, derived, which enable us to analyze the behaviors of CB and NCB schemes in terms of both HE and achievable rate. Apart from this, to guarantee sensor fairness with respect to the HE and achievable rate, a max–min power control strategy based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based power control is able to determine the optimal solution in closed form and is more memory efficient than the convex-solver-based counterpart. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations.

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems in the presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions of the achievable secrecy rate relied on the additive quantization noise model are derived. Based on these analytical results, we quantify the impacts of key system parameters, such as the antenna array number, DAC resolution, Rician$\mathcal K$-factor, and balance factor between data and artificial noise power on secrecy enhancement. Several interesting insights are attained by assuming that Eve can or cannot perfectly remove inter-mobile-terminal interference. Moreover, we also propose a power control algorithm that maximizes the achievable secrecy rate, which can be represented as a series of second-order-cone programs for which efficient solvers exist. All the theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments.

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism.

Recently, novel tactile and flexible network services and applications emerge, along with their explosive growth of mobile data traffic. However, current internet cannot fulfill the demands of these network services. The upcoming 5G network, including the tactile internet, is designed by adopting softwarization and virtualization technologies, aiming at removing the rigidity of dedicated network hardware and implementing various network services in a flexible manner. One key technical issue in 5G network is the virtual resource allocation, known as virtual network embedding (VNE). However, existing studies focus on allocating virtual resource in the fixed underlying network, ignoring the effect of the mobile end nodes. As unmanned aerial vehicles (UAVs) will play an important role in 5G era, we incorporate UAVs into the 5G network in order to expand the coverage and agility of novel network services. In this paper, we conduct a research on the virtual resource allocation in UAVs-integrated 5G networks. The formal problem model for UAVs-integrated 5G networks is involved. A novel profit model, quantifying the UAV mobility for telecommunication service provider (TSP), is proposed. Then, we propose one virtual resource allocation algorithm, labeled as UAV-5G-VNE. Our UAV-5G-VNE consists of initial allocation part (UAV-5G-VNE-Ini) and re-allocation part (UAV-5G-VNE-Re). Our UAV-5G-VNE enables to predict all possible connecting access nodes of virtual UAV and makes implemented network services continued. In order to validate our UAV-5G-VNE efficiency, we conduct the experiments. Experiment results demonstrate that UAV-5G-VNE outperforms two benchmark algorithms, in terms of TSP profit and virtual service acceptance.

In the foreseeable future, data transmission and information exchange play important roles in intelligent transportation (ITS) systems. Heterogeneous networks (HetNets) currently emerge in order to provide large amount of data flow and customized network services. Virtualization technology is considered as one most promising approach towards HetNets implementation, aiming at managing virtualized HetNets resources in a convenient manner. In the virtualization research, the virtual resource allocation is another core technical issue. Though the issue has been well addressed in recent years, the secure virtual resource allocation has not been fully investigated yet. Hence, we research the secure virtual resource allocation for HetNets in this paper. Following the introduction of the security model for HetNets, we propose one effective heuristic strategy, incorporating the greedy and isolation methods. Experiment work is conducted so as to validate the efficiency of the heuristic strategy. Experiment results vividly reveal that our heuristic strategy, incorporating the greedy and isolation methods, performs better than the counterpart without using the isolation method, in terms of average virtual network service acceptance.

In this paper, we consider a downlink cell-free massive multi-input multi-output (mMIMO) system, assuming few-bit analog-digital converters (ADCs) and digital-analog converters (DACs) are implemented at the access points (APs). Leveraging on the linear additive quantization noise model (AQNM), we derive a tight approximate rate expression, which provides insights into the impacts of the imperfect quantization error and channel estimation error. Thanks to the trackable result, we quantitatively compare the performance differences between the two quantization models, namely the AQNM and the Bussgang theorem. In particular, the AQNM can offer analytical tractability for few-bit quantization while the Bussgang theorem only characterizes 1-bit quantization since the multi-bit quantization under the Bussgang theorem is difficult to deal with. Simulation results show that under the same 1-bit quantization, the rate performance with the Bussgang theorem is roughly identical to the case of the AQNM.

The penetration of electric vehicles (EVs) embedded with information and communication technology (ICT) devices and tools form a huge connected network that can be viewed as Internet-of-EVs(IoEV). The huge data gathered in IoEV network needs to be processed at cloud-based infrastructure which has abundant resources. However, due to the high mobility of the EVs, resource management from the remote cloud service providers has become one of the most difficult tasks to be performed in this environment. In this regard, data analytics fused with fog or edge computing can be leveraged to increase the resource availability in V2G environment where resources are provided to the EVs on the edge of the network. Keeping these points in mind, this paper presents a new framework for integration of cloud computing and IoEV on the edge of the network which provides flexibility to the end users for smooth execution of various applications. In addition, a resource allocation and job scheduling strategy for EVs at the edge of the network is presented in the paper. The results obtained with respect to various performance metrics confirm the applicability of the proposed scheme for future applications in V2G scenario.

Recently, network function virtualization (NFV) technology is strongly emphasized by the telecommunication industry, aiming at virtualizing physical resources, providing more agile and high-quality virtual network services and reducing telecommunication provider costs. In NFV environment, each network service (NS) is usually represented by a sequences of service function chains (SFCs). The issue of SFC composition, embedding and scheduling is regarded as the key issue in NFV research. Most of previous researchers focus on solving SFC embedding (SFC-E) problem and proposing effective embedding algorithms. Correspondingly, there exist dozens of embedding publications in the literature. However, few researchers studied the secure SFC-E problem. Hence, we research this topic and discuss the typical security risks in NFV-enabled networks. Then, we propose one novel and secure framework, labeled as No-Sec-SFC-E, in this paper. The goal of No-Sec-SFC-E is to ensure secure network function deployment and resource allocation in NFV-enabled networks. In No-Sec-SFC-E framework, virtual network functions (VNFs), having high security probabilities, are usually preferred. With the aiming of validating the proposed No-Sec-SFC-E framework, we conduct the experiment. Recorded experiment results vividly reveal the feasibility and effectiveness of NoSec-SFC-E.

In this study, the uplink performance of cell‐free massive multi‐input multi‐output (mMIMO) system with multi‐antenna access points (APs) and users is investigated, assuming low‐resolution analogue–digital converters (ADCs) are employed at the APs. By exploiting the additive quantisation noise model, a tight closed‐form rate expression is derived. This tractable finding characterises the impacts of the multi‐antenna APs and users, the imperfect quantisation error and the channel estimation error. In order to maximise the uplink sum‐rate, a joint quantisation bit and power control problem is formulated, subjecting to the backhaul capacity and each user power constraints. The original resource optimisation problem is non‐convex and it is decomposed into two sub‐problems, namely quantisation bit design and power allocation problem, to alleviate the difficulties. In particular, the resultant two sub‐problems can be efficiently determined by utilising the Lagrange Multiplier and sequential convex approximation methods, respectively. Finally, numerical simulations are presented to examine the analytical findings and evaluate the effectiveness of the proposed algorithm.

Future network based on software-defined networking (SDN) and network function virtualization (NFV) technologies is the main evolution tendency of current Internet, enabling telecommunication service providers (TSPs) to share their virtualized network resources with their contracted users in a flexible and economical manner. One key technical issue is virtualized resources allocation. In order to solve this issue, multiple mapping algorithms have been proposed. However, prior mapping algorithms focus on solving the allocation problem in one centralized underlying substrate network (SN), having the only goal of maximizing the TSPs' mapping revenue. As energy cost accounts for more than half of the total underlying network cost, it is crucial to minimize the total energy cost, while keeping high mapping revenue. In addition, in the real networking environment, multiple geographically distributed SNs, called interdomain networks, coexist. Hence, it is essential to embed each virtual network (VN) service among the interdomain SNs. Based on this, we first propose the formal problem model and the energy cost model. Then, we propose a novel and efficient mapping strategy, labeled EERID. Our EERID is able to map each VN service among interdomain SNs within polynomial time. The experimental results vividly reveal that EERID significantly reduces energy cost by approximately 18% over the existing energy-aware algorithms. At the same time, our EERID achieves higher embedding revenues than the existing energy-aware mapping algorithms, up to 23%.

Environmental monitoring is one of the fundamental applications of the Internet of Things (IoT), and caching in energy harvesting-aided IoT is a promising solution to handle the energy charging of the IoT nodes in the vast monitoring area. However, the growth of the requirements for monitoring area and accuracy brings huge infrastructure costs to the network operators (OP), especially for the multiple OPs scenario. In this article, we utilize wireless virtualization to enable the IoT node sharing between multiple OPs in cache-enabled energy harvesting-aided IoT, so as to improve the utility of the OPs. A Stackelberg game is formulated to jointly handle the IoT node sharing and energy transmission incentives between OPs and energy transmitters. Then, the knapsack problem, convex and linear programming are utilized to approximate the game through problem transformation and derivations. On the basis of that, an alternative direction algorithm is proposed to solve the equilibrium efficiently. The simulation results verify the advantages of the proposed algorithm in utility improvement and fairness maintenance between multiple OPs.

Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Virtualization is the fundamental attribute of next generation network. Embedding customized virtual networks (VNs) onto the underlying physical network (PN) is the core issue in virtualization research. This issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the re-embedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be re-embedded. The dynamic embedding algorithm ensures flexible VN assignment and improves the physical resource utilization. Finally, simulation results are illustrated in order to validate the strength of our proposed dynamic algorithm. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

Virtual network embedding (VNE) refers to the resource allocation problem for network virtualization. Since its inception, multiple mapping algorithms have been proposed for embedding virtual networks (VNs) effectively and efficiently. However, prior mapping algorithms mostly complete the VN embedding in two separated stages: first node embedding and subsequent link embedding. Certain mapping algorithms embed the VN in one stage by using mixed integer linear programming method or subgraph isomorphism approach, involving high embedding completion time. Meanwhile, prior researchers conduct the VN embedding, on the basis of one underlying substrate network (SN). While in future VNE application, each VN must be mapped among multiple geographically distributed SNs. On above backgrounds, we propose a location aware and node ranking value driven embedding algorithm, labeled as LANRVD. The LANRVD enables to conduct the embedding in two coordinated embedding stages within polynomial time. In addition, the LANRVD embeds the VN among multiple geographically distributed SNs. Numerical results reveal that the LANRVD significantly improves VN acceptance ratio by 10% over existing typical two-separated-stages algorithms.

We investigate the downlink rate performance of cell-free massive multiple-input multiple-output (mMIMO) network with conjugate beamforming precoder when the access points (APs) are equipped with one-bit analog-digital converters (ADCs) and digital-analog converters (DACs). Based on Buss-gang decomposition theory, we derive a rigorous closed-form rate expression, which covers the impact of multi-antenna APs, the imperfect quantization error, and channel estimation error. Then, by exploring this closed-form result, we show that the quantization interferences resulted from one-bit quantization can significantly decrease the downlink rate performance. In addition, we also analyze the performance gain provided by adding the total number of antennas or increasing the total transmitted power. We observe that, adding the total number of antenna arrays is a promising way to compensate for the quantization losses. However, these losses cannot be compensated by infinitely increasing the total transmitted power.

This paper explores the potential of physical layer security for K- tier heterogeneous networks (HetNets) with nonbest user association (UA) scheme. By modeling the spatial positions of network elements in each tier as homogeneous Poisson point processes (PPPs), we first present the non-best UA probability of a typical user equipment associating with the m-th average biased received power (ABRP) with passive eavesdroppers, then the expression of total secrecy probability for the K-tier HetNets is derived with stochastic geometry. Both the analytical and numerical results show that the implementation of the non-best UA scheme can significantly improve the secrecy probability, which indicates that non-best UA could be a promising solution for safeguarding K-tier HetNets. Moreover, we show that the secrecy probability with non-best UA scheme is not always decreasing over the transmission power of the base stations, which is quite different from the best ones.

In this paper, a multigroup multicast cell-free massive MIMO (CF-mMIMO) system with conjugate beamforming (CB) precoding is considered. In this system, M N-an-tennas access points (APs) distribute in the serving area and coherently serve J×K single-antenna users. All users are randomly divided into J multicast groups. A novel closed-form downlink rate's expression for any M, N, J, and K is derived, which motivates us to propose a weighted max-min power optimization algorithm. In designing this algorithm, the high rate requirements of the high priority groups and the egalitarianism principle are considered. Numerical results demonstrate that the performance improvement achieved by increasing N is visibly better than increasing M. Furthermore, the proposed weighted maxmin fairness algorithm performs well in many respects.

In the area of network virtualisation, virtual network embedding (VNE) refers to the resource allocation problem. In the literature, researchers have proposed multiple VNE algorithms. These algorithms have the goal of accommodating as many requested virtual networks (VNs) as possible. However, most of prior embedding algorithms belong to the two‐stage (separated node and link embeddings) mapping algorithm category. Certain embedding algorithms embed each VN in one mapping stage by using mixed integer linear programming approach or graph theory, having very high computation time. There is a lack of heuristic algorithms, enabling to embed nodes and links per VN in one mapping stage. In addition, each requested VN embedding needs to be completed in polynomial time so as to be promoted to future dynamic VN service application and real‐time VNs embedding. Based on these backgrounds, the authors propose a novel real‐time and one‐stage heuristic mapping algorithm (VNE‐RTOS). Numerical evaluations are conducted to strengthen that VNE‐RTOS earns more embedding revenues by 8% over typical two‐stage heuristic embedding algorithms (e.g. VNE‐TAGRD) while achieving the same substrate resource utilisation.

In order to enhance simultaneously the association robustness of mobile users and spectrum efficiency (SE) of systems, by integrating the decoupled uplink (UL)/downlink (DL) association (DUDA) and cross‐tier dual connectivity (DC), the orthogonal multiple access (OMA)‐mode and non‐OMA (NOMA)‐mode design schemes are investigated over a three‐tier heterogeneous network (HetNet) accompanying with the proposition of a conditional association analysis method. The scheme exploits effectively the DC requirement of NOMA. Additionally, in the proposed conditional association design and analysis method, the decoupling UL association designs are conducted only under a given DL association condition. Owing to low complexity, this method shows that it is very easy to achieve the possible decoupling UL DC designs and the corresponding condition association probabilities. Then, the conditional coverage probabilities and SEs of the OMA‐mode and NOMA‐mode HetNets are derived by specially considering DUDA and coupled UL/DL association strategies. The proposed conditional design framework can be easily extended to multi‐tier HetNets and is helpful to select feasible DL candidate from all possible DL associations so that the resulting UL associations are optimal. Besides this, the cross‐tier DC design ensures the effective exploitation of the whole network resources because different tiers have different serving levels.

A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal redundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy. Moreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application environments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink can recover measurements with reasonable accuracy by using the proposed method.

Virtual network embedding (VNE) refers to the resource allocation problem in network virtualization (NV). In the literature, researchers have proposed multiple VNE algorithms. These algorithms aim at embedding more and more requested virtual networks (VNs) onto the underlying networks and maximizing embedding revenues. Prior VNE algorithms mostly belong to the two-stage (separated node and link embedding) mapping algorithm category. Some other VNE algorithms embed each VN in one stage by using mixed integer linear programming (MILP) approach. There is a lack of one-stage heuristic algorithm, enabling to embed nodes and links in one mapping stage. In addition, each requested VN needs to be mapped in polynomial time so as to be promoted to future dynamic VN service application and real-time VNs embedding. Therefore, we propose a real-time and one-stage heuristic mapping algorithm (VNE-RTOS). Numerical simulations are conducted to validate that our VNE-RTOS earns more embedding revenues by approximately 3.4% over typical two-stage heuristic embedding algorithms (e.g. GRD-VNE) while achieving the same substrate resource utilization.

A novel m th best average biased received power (ABRP) user association (UA) scheme is proposed for K ‐tier heterogeneous networks, where a user is associated with the closest base station (BS) of the k th tier having the m th strongest ABRP, . By using stochastic geometry and Poisson point processes, the authors perform the analysis of the UA probability that a typical user is associated with a BS that has the m th strongest ABRP as well as the downlink signal‐to‐interference‐noise ratio (SINR) coverage performance. The obtained results show that, for a given tier k , when the layer's position in the ordered set of ABRPs is not equal to m and the power or density of BSs is relatively small, i.e., , the UA probability increases monotonically with or . Contrarily, it decreases. For the other UA probabilities, the opposite results are achieved. Additionally, the results also show that the coverage probability of the downlink SINR of the tier k always increases with power , which is consistent with the one in the conventionally best ABRP scheme. However, for other tiers, the corresponding downlink SINR coverage probabilities do not behave monotonically with the power .

Network virtualization (NV) is a promising approach to remove the ossification of current Internet. Virtual network embedding (VNE) is the key issue in NV which efficiently and effectively maps various of virtual networks (VNs), with different node and link resource requests, onto the shared substrate network(s) with finite underlying resources. Previous VNE algorithms in the literature are mostly heuristic. Single network topology attribute and each node's local resources are assisted to rank nodes in most heuristic algorithms, leading to inefficient resource utilization of substrate network in the long run. To deal with this issue, we propose the network topology attribute and network resource-considered algorithm (VNE-NTANRC). The VNE-NTANRC algorithm adopts a novel node-ranking approach to rank all substrate and virtual nodes before embedding each given VN. The novel node-ranking approach has two subapproaches and considers five important network topology attributes and global network resources altogether. One subapproach is able to calculate all node values (NoV) directly. The other subapproach, stimulating from the Google PageRank website algorithm, enables to calculate NoVs in a stable state. Simulation results reveal that VNE-NTANRC algorithm outperforms typical and latest heuristic algorithms, only considering single network topology attribute and local resources.

Network virtualization (NV) is widely accepted as one enabling technology for future network, which enables multiple virtual networks (VNs) with different paradigms and protocols to coexist on the shared substrate network (SN). One key challenge in NV is VN embedding (VNE), which maps a VN onto the shared SN. Since VNE is NP-hard, existing efforts mainly focus on proposing heuristic algorithms that try to achieve feasible VNE in reasonable time, consequently the resulted embedding is not optimal. To tackle this difficulty, we propose a candidate assisted (CAN-A) optimal VNE algorithm with lower computational complexity. The key idea of the CAN-A algorithm lies in constructing the candidate substrate node subset and the candidate substrate path subset before embedding. This reduces the mapping execution time substantially without performance loss. In the following embedding, four types of node and link constraints are considered in the CAN-A algorithm, making it more applicable to realistic networks. Simulation results show that the execution time of CAN-A is hugely cut down compared with pure VNE-MIP algorithm. CAN-A also outperforms the typical heuristic algorithms in terms of other performance indices, such as the average VN request acceptance ratio and the average virtual link propagation delay.

This paper focuses on a multi‐user full‐duplex massive multiple‐input multiple‐output amplify‐and‐forward relaying system with low‐resolution analogue‐to‐digital convertors (ADCs). By modelling the loop‐interference and quantisation noise as additive noise, we perform the analyses of the achievable spectral efficiency (SE) by considering the two canonical processing schemes, maximum ratio combining/maximal ratio transmission (MRC/MRT) and zero‐forcing receive/zero‐forcing transmission (ZFR/ZFT), respectively. Specially, we first obtain the tractable closed‐form expressions of the achievable SE. Then, we present the asymptotic analyses under three different power‐scaling scenarios. Comparison analysis shows that the low‐resolution quantisers impose more performance loss on ZFR/ZFT schemes than MRC/MRT ones. At the same time, it is found that with different power‐scaling schemes, the systems have different capabilities to restrict the effect of loop interference and low‐resolution ADCs. Specially, the impact of both the loop‐interference and quantisers on achievable SE can be restricted effectively only under the power‐scaling scenario where the relay's transmission power is scaled down with the number of transmit antennas and the sources’ transmission power is fixed. For the power‐scaling case where the transmission powers of both relay and sources are scaled down simultaneously with the number of antennas, only the effect of the loop interference can be restricted, but the one of low‐resolution quantisers remains.

This paper considers a downlink nonorthogonal multiple access (NOMA) system where the source intends to transmit independent information to the users at target data rates under statistical channel state information. The outage balancing problem is studied with the issues of power allocation, decoding order selection, and user grouping being taken into account. Specifically, with regard to the max-min fairness criterion, we derive the optimal power allocation in closed form and prove the corresponding optimal decoding order for the elementary downlink NOMA system. By assigning a weighting factor for each user, the analytical results can be used to evaluate the outage performance of the downlink NOMA system under various fairness constraints. Furthermore, we investigate the case with user grouping, in which each user group can be treated as an elementary downlink NOMA system. The associated problems of intergroup power and resource allocation are solved. The implementation complexity issue of NOMA is also considered with focus on that caused by successive interference cancellation and user grouping. The complexity and performance tradeoff is analyzed by simulations, which provides fruitful insights for the practical application of NOMA. The simulation results substantiate our analysis and show considerable performance gain of NOMA when compared with orthogonal multiple access.

To obtain an insight about the effect of channel parameters on buffer‐aided relay selection systems, the max‐link selection (MLS) schemes are investigated over independent and non‐identically distributed (i.ni.d) Rayleigh fading channels. Specially, by employing Markov chain, the authors first model the state transition matrix and outage probability. Secondly, they obtain the closed‐form expressions of the corresponding statistic properties. The presented results show: (i) when the relaying channels are asymmetric (but the source–relay links are independent and identically distributed fading, so does the relay–destination links), the MLS scheme outperforms the traditional best relay selection (T‐BRS) and max–max best relay selection (MM‐BRS) schemes. However, when the relaying links are unbalanced severely, the MLS scheme does not provide diversity gain over the T‐BRS and MM‐BRS schemes; and (ii) For the more general i.ni.d fading case, it is observed that the MLS schemes can be inferior to the MM‐BRS schemes. The derivations of this work have values for reference. For example, by using the achieved statistic properties they can further perform the investigation on the delay for the buffer‐aided MLS relaying systems over i.ni.d fading channels, which is a critical issue of buffer‐aided relay selection systems.

We consider the downlink relay network, for which a pairwise transmission strategy is suggested with the users being grouped into pairs and the source communicating to each pair of users using a novel pairwise relaying protocol. By utilizing superposition coding (SC), the messages of each two paired users are intentionally transmitted non-orthogonally or simultaneously in the same time-frequency channel to compensate for the bandwidth loss of relaying. We investigate the optimal decoding at the relay and the users, respectively, which are then combined to guide the selection of the power allocation factors of SC and to decide the corresponding decoding order. Then, a largely simplified outage expression of the pairwise relaying protocol is given, based on which we investigate, for the multi-user scenarios, the user pairing problem from the perspective of minimizing the total power consumption under targeted outage probabilities of the users. An easily implemented user pairing algorithm with computational complexity of O(K2) is provided, which achieves a performance that is very close to that of the optimal user pairing which can be solved in polynomial time, O(K3). Numerical results show that the pairwise transmission strategy can significantly decrease the average transmit power.

In this paper, we investigate a joint relaying and superposition coding (SupC-R) transmission strategy for downlink networks. By relaxing the orthogonality constraint between different users, the SupC-R strategy facilitates coordination and channel sharing among different data flows, by which the disparity in the channel qualities of different users, as an inherent feature of downlink network topology, is utilized as benefits for potential performance gains. The inter-user interference due to non-orthogonality can be steered by smart design of superposition coding (SupC), including both the selection of the superposition factor and the decoding order. At the same time, channel sharing saves time and leads to more efficient utilization of network resources. The analytical and numerical results show significant gains of SupC-R over its conventional relaying counterpart. The effect of the channel disparity on the design of SupC and the performance of the resultant SupC-R strategy is highlighted.

In the existing literature about cognitive radio opportunistic relaying (CR‐OR) systems, the first‐order statistics such as outage probability are investigated widely. However, for the second‐order statistics, such as average outage duration (AOD) and average outage rate (AOR), there is not open works, still. To obtain a comprehensive cognition on the behaviour of mobile communication systems, this study focuses on the second‐order statistical properties of CR‐OR systems. There are two CR‐OR schemes considered, in which the canonical amplify‐and‐forward (AF) and reactive decode‐and‐forward (RDF) are employed, respectively. Since the equivalent end‐to‐end signal‐to‐noise ratio (SNR) of AF CR‐OR is complicated such that it is very difficult to obtain the closed‐form solution to AOR of AF CR‐OR schemes, the high SNR approximation in AF CR‐OR schemes is employed. For the two schemes, first the closed‐form solutions to AORs and AODs are obtained by using appropriate mathematical proof. Based on the derivations, the comparison analyses about AORs and AODs of the two schemes are provided. The comparison results show that, under high SNR approximation, the AF CR‐OR scheme achieves the same AOR and AOD as RDF CR‐OR. Finally, the impact of system parameters on AORs and AODs is provided.

To overcome the performance loss caused by the N th single best relay selection schemes, in this study, the authors present the N–R th dual best relays selection schemes. In the proposed schemes, there are two relays selected out of the available relays to forward the received signals to the destination by using the decode‐and‐forward protocol. The statistical descriptions of instantaneous end‐to‐end received signal‐to‐noise ratios (SNR) are investigated over independent and non‐identically distributed Nakagami‐ m fading channels. By using the appropriate mathematical proof, the authors firstly obtain the closed‐form expressions to the probability density function and cumulative distribution function of instantaneous end‐to‐end SNR. Then, based on the derived results, the outage probability and average symbol error rate (SER) of the proposed N–R th dual best relays selection schemes are investigated. The comparison analyses between the N th schemes and the N–R th schemes show that the proposed N–R th dual best relays selection schemes can improve greatly the performance of opportunistic relaying systems such as outage probability and average SER. Especially, the proposed dual best relays selection scheme can obtain more outstanding improvement on SER.

Noncoherent multiuser detection for CDMA systems with M-ary nonlinear modulation was recently studied, and noncoherent optimum, decorrelative, and MMSE detection schemes for this kind of system were proposed and analyzed in previous papers. In this letter we propose a blind adaptive noncoherent decorrelative multiuser prefilter for CDMA systems with M-ary nonlinear modulation over AWGN channel. The proposed multiuser detector uses possibly known signature waveforms of users as much as possible, so it provides better performance compared with previous multiuser detectors for nonlinearly modulated CDMA systems in terms of convergence properties and symbol error rate.