Unmanned aerial vehicles (UAVs) have emerged as pivotal components in next-generation communication systems due to their broad coverage and flexible deployment capabilities, enabling efficient connectivity with multiple ground users. Although the integration of nonorthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) technologies in UAV communications has attracted growing research interest, existing studies remain insufficient for jointly optimizing beamforming and power allocation, particularly in terms of fully capturing the complex coupling among decision variables. This study investigates the joint optimization problem of beamforming, power allocation, and UAV position optimization in UAV systems, where the UAV communicates with multiple ground users using NOMA and MIMO technologies. The core objective is to maximize the achievable transmission rate of the system while complying with a total power budget constraint. Owing to the inherent nonconvexity of the formulated problem and the intricate coupling among decision variables, the original problem is decomposed into three subproblems: beamforming, power control, and UAV placement optimization. To address these subproblems efficiently, we propose an angle-sector-based iterative optimization framework by invoking the alternating optimization technique under the NOMA-MIMO system. This strategy not only enhances overall spectral efficiency but also ensures reliable connectivity for users at greater distances while maintaining high communication quality for those in proximity. The simulation results demonstrate that the adopted user grouping strategy, which incorporates a group matching mechanism, yields notable improvements in resource utilization. Compared with other solution strategies, the proposed alternating optimization algorithm exhibits superior performance in terms of achievable rate enhancement, thereby validating its effectiveness and practical value in complex UAV-enabled NOMA-MIMO systems.

Artificial intelligence-agent communication networks (ACNs) in the sixth-generation (6G) enable collaborative task execution among agents and butler. However, compared with traditional mobile edge computing (MEC), in ACNs, a large number of agents possess comparable computing capabilities and task proportions need to be jointly determined rather than being predefined, causing high optimization complexity in large-scale deployment scenarios. In this paper, we propose an effective framework to solve the large-scale coupled optimization problem under acceptable complexity. Specifically, we model the joint task allocation, resource allocation, task offloading and computation frequency adjustment problem as an NP-hard nonconvex mixed-integer nonlinear programming (MINLP) problem, and derive its lower bound through Lagrangian relaxation. We decompose the problem into resource allocation and task offloading subproblems, which are solved via proximal policy optimization (PPO) and minimum-cost models within a block coordinate descent (BCD) framework. Simulations demonstrate the tightness of the lower bound, achieving stable convergence for 30 agents while reducing latency from 150 ms to 50 ms and the energy consumption by 28.18%. Our algorithm has great potential for ACNs with many agents deployed in future 6G scenarios, such as autonomous vehicles, robotic swarms and precision telemedicine.

Nowadays, Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has been recognized as a promising technique for flexibly handling computation tasks in 5G advanced and 6G networks. This paper investigates the joint optimization of service caching and computation offloading within a dual time-scale framework. We maximize the caching utility and minimize the task processing delay by jointly optimizing service caching policies, UAV flight trajectory, and computation offloading decisions. Specifically, for the long-term problem, we use the Latent Dirichlet Allocation (LDA) model to predict user preferences, and propose a Lagrangian dual decomposition based algorithm. For the short-term problem, a self-attention based Multi-Agent Proximal Policy Optimization (MAPPO) algorithm is designed. Under the Centralized Training with Decentralized Execution (CTDE) framework, this algorithm integrates a multi-head self-attention mechanism with curriculum learning. Each UAV is regarded as an agent, and a self-attention encoder is integrated at the front-end of each Actor network. This enables the agent to dynamically capture the relative importance between itself and all users, and context-aware features are extracted to make more intelligent and trajectory designs. Through extensive simulation experiments, the long-term algorithm yields the performance improvements of 76.5% in cache hit rate and 66% in caching utility, as compared to the second best baseline. In dynamic scenarios, the short-term algorithm achieves a 16% reduction in total processing latency with respect to the proximal policy optimization policy.

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

This paper investigates the challenges of user pairing and power allocation in unmanned aerial vehicle (UAV) systems that use nonorthogonal multiple access (NOMA) to communicate with multiple ground users. The main goal is to maximize the achievable transmission rate of the system while ensuring the quality of service (QoS) requirements of users, under a constrained total power budget. Considering the non-convexity of the original problem, a stepwise optimization approach is adopted. To improve resource utilization and solve the user pairing problem, a deep Q-network method assisted by group matching is proposed. Specifically, the group matching method is applied to allocate users, and an optimized deep Q-network (OP-DQN) is used to optimize power allocation strategies. The simulation results show that compared with other user pairing strategies, this method significantly improves resource utilization and fairness. In addition, the proposed scheme effectively enhances the system transmission rate and resource efficiency.

Due to the increasing demand for the accuracy of spectrum maps, fusing spectrum maps has gained attention as an effective method to improve the exactitude of spectrum map construction. However, most of the existing spectrum map fusion methods overlook the over-fitting problem and the correlation of spectral data in the fusion process, so the performance can hardly meet expectations. In this paper, a spectrum map fusion method based on differential ridge regression is proposed, which can construct accurate spectrum maps in the electromagnetic environment with strong-correlation data with high accuracy. First, we construct a spectrum map fusion model by exploiting the propagation characteristics of the spectrum signal. According to the path loss model, the differential ridge regression regularization term is designed to handle the correlation of spectral data and suppress anomalies from spectrum receivers. Finally, we construct a convex optimization problem for spectrum map fusion and obtain the lower bound of the problem by developing Lagrange duality. This method can ensure the convergence of the spectrum map fusion problem and accelerate the convergence speed under low complexity. Simulation results show that the proposed fusion method can effectively improve the accuracy of spectrum map construction compared with the state-of-the-art.

Although resource sharing and mixed numerology among slices are promising for improving wireless resource utilization, these techniques can compromise isolation performance and cause serious inter numerology interference (INI). Therefore, this paper studies wireless resource management in a mixed numerology-based sliced 6G space–terrestrial integrated radio access network (STI-RAN) with the aim of reducing INI and guaranteeing isolation performance while decreasing interference from Doppler frequency shifts caused by the high-speed movement of low-orbit satellites. First, an isolation performance indicator is defined to evaluate different isolation performances, and a universal spectral distance model is formulated to rewrite the INI power model. Next, the dynamic wireless resource management problem is formulated in a discrete form, yielding a scheme called Flex-$\mu$, which is designed to reduce the INI and Doppler frequency shifts, guarantee isolation performance, and enhance the SINR. Finally, an intelligent multi-characteristic matrix coding-based social group optimization (MultiMatrix-SGO) algorithm is designed to solve the proposed NP-hard discrete optimization problem. Compared with existing schemes, the system utility is efficiently increased by up to 58.32%, the SINR can converge to 38.44 dB, and the isolation performance is guaranteed while the INI and Doppler frequency shifts are reduced.

The sixth-generation mobile communication systems are faced with the challenge of supporting massive user access while satisfying massive ultra-reliable and low latency communications (mURLLC). Although the cell-free massive multiple input multiple output (CF mMIMO) has significant advantages, seamless coverage is still challenging. Moreover, mURLLC is limited by the mutual constraints of latency, reliability and connection density for a scalable CF mMIMO system. In this paper, we first develop an analytical model of mURLLC based on a scalable CF mMIMO architecture. By introducing the access point planning matrix and combining it with the maximum-ratio combining method, we derive the user’s post-processing signal-to-noise ratio. Second, we employ the finite blocklength theoretical analysis tools to derive the latency and error probability, which can quantify system reliability, and use the connection density metric to portray scalability. Furthermore, we analyze the interplay mechanism between latency, reliability and connection density. Through simulation experiments, we verify the constraints among latency, reliability and connection density, and find that the scalable CF mMIMO can effectively meet mURLLC requirements for massive user access with appropriate parameters.

The emergence of generative pre-trained transform-ers (GPTs) will thoroughly change the application of sixth generation mobile communications (6G) networks. Therefore, it is necessary to design new network architectures to support ubiq-uitous deployment and real-time applications of GPTs. Aiming to integrate GPTs and the 6G network, this paper investigates the typical application scenarios of 6G+GPTs and summarizes the requirements of network key performance indicators (KPIs). Then, to address the complex and dynamically changing commu-nication environment, a self-adaptive 6G network architecture is proposed based on autonomous learning and self-optimization. Additionally, a novel mechanism based on attack samples is studied to improve the security of applying GPTs in 6G networks. Finally, we demonstrate that the proposed network architecture and security mechanism can satisfy the KPIs and improve robustness effectively. Overall, this paper provides a theoretical basis for the support of native GPTs with a novel 6G network architecture.

Achieving energy-efficient massive ultrareliable and low-latency communications (E2-mURLLC) is a promising application prospect for sixth-generation (6G) mobile communication networks. However, there are some insurmountable obstacles, such as a large number of potential users, complex and diverse small-scale and shadow fading, and stringent energy efficiency (EE), reliability, and latency requirements. Considering the above obstacles, we propose a cell-free massive multiple-input–multiple-output (MIMO) architecture based on the$\kappa $-$\mu $shadowed fading model, and maximum-ratio combining (MRC) multiuser detection with simple path-loss decoding (S-PLD) to achieve the simultaneous optimization of EE, latency, and reliability. Furthermore, the finite blocklength information theory is used to uncover the relationship among EE, reliability, latency, and achievable data rate when the packet size is small. Simulation results show that compared with the massive MIMO architecture, using our architecture with MRC multiuser detection and S-PLD can support a threefold increase in the number of access users, reduce transmit power by 90%, achieve a nearly 100 times reliability enhancement, and shorten transmission latency by 23.3%. Consequently, a cell-free massive MIMO system with MRC multiuser detection and S-PLD, as a considerable significant potential to facilitate the advancement from URLLC to E2-mURLLC, is promising to support some time-sensitive applications with massive access, such as unmanned aerial vehicles, the Industrial Internet of Things and vehicle-to-vehicle communications.

We investigate the performance of massive ultra-reliable and low-latency communications (mURLLC) under massive active users, and non-uniform small-scale and shadow fading in the uplink (UL) of a next-generation multiple access (NGMA) system that integrates massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques. We first derive new closed-form expressions to accurately approximate the probability density function (PDF) and cumulative distribution function (CDF) of the channel gains in MIMO systems under the$\kappa $-$\mu $shadowed fading. Then, we derive the post-processing signal-to-noise ratio (SNR) and its closed-form PDFs and CDFs in the NGMA system, under both perfect and imperfect channel state information of the$\kappa $-$\mu $shadowed fading channel. Given the post-processing SNRs and their PDFs, the general expressions are established for the error probability (EP) to analyze the mURLLC of NGMA by applying finite blocklength information theory. Corroborated by extensive simulations, our analysis reveals that with the increasing reliability requirements of the users, the relative gaps in EPs enlarge between users experiencing different fading channels, and the feasible system configurations (i.e., the transmit powers of the users, and the numbers of antennas, active users, and subcarriers) also increasingly differ between the users. The impact of different fading on mURLLC implementations cannot be overlooked, and the research of mURLLC under the$\kappa $-$\mu $shadowed fading model is indispensable. The NGMA system considered in this paper is capable of achieving mURLLC under non-uniform small-scale and shadow fading.

The proliferation of delay-sensitive Internet-of-Things (IoT) applications has ushered in a need for the statistical delay quality-of-service (QoS) guarantee for the applications. In this paper, we first derive an upper bound for the queuing delay violation probability (UB-QDVP) in uplink non-orthogonal multiple access (NOMA) by applying stochastic network calculus (SNC) to the Mellin transforms of service processes. A closed-form asymptotic approximation of the UB-QDVP is developed by proving the asymptotic convergence of the Mellin transform and its finite-length truncations. Given the closed-form asymptotic UB-QDVP, we propose two power allocation schemes. The first scheme minimizes the transmit power of a NOMA user pair while guaranteeing the statistical delay QoS of the pair. The second maximizes the$\alpha $-utility function of the effective capacity of the user pair, striking a balance between the energy efficiency and user fairness of uplink NOMA systems. Simulations validate the UB-QDVP and show the superiority of the proposed schemes to conventional power allocation schemes in terms of energy efficiency and fairness.

Accurate and efficient foliage penetration (FOPEN) target recognition plays a vital role in many mission-critical applications, ranging from civilian to surveillance and military. Recently, device-free sensing (DFS), as an emerging technique, has gained great popularity because it requires no dedicated equipment other than wireless transceivers. Although some DFS-based approaches have been successfully applied in foliage environments, they are vulnerable to climate dynamics and heavily rely on re-labeling large amounts of new data when the weather is altered. To address this issue, a CNN-based weather adaptive target recognition network (WATRNet) is proposed in this paper. Specifically, a lightweight weather conditional normalization (WCN) module is embedded atop each convolutional block to encode inputs under different weather conditions into a shared latent feature space. Under an end-to-end learning manner, the proposed WATRNet first learns knowledge from sufficient labeled data under a certain weather condition to achieve a precise classifier. When applying this model under another weather condition, only the WCN module needs to be retrained using limited new labeled samples to learn weather-invariant features, while the rest convolutional parameters in WATRNet are frozen. Consequently, the domain discrepancy caused by climate variations can be adaptively mitigated with as few relabeled data as possible. Comprehensive evaluations are carried out on a real FOPEN dataset collected under four different weather conditions. Experimental results verify that the presented method can achieve over 90% accuracy, even when it implements from a normal weather condition to another severe weather condition with only small amounts of training samples.

This paper investigates a new downlink nonorthogonal multiple access (NOMA) system, where a multiantenna unmanned aerial vehicle (UAV) is powered by wireless power transfer (WPT) and serves as the base station for multiple pairs of ground users (GUs) running NOMA in each pair. An energy efficiency (EE) maximization problem is formulated to jointly optimize the WPT time and the placement for the UAV, and the allocation of the UAV’s transmit power between different NOMA user pairs and within each pair. To efficiently solve this nonconvex problem, we decompose the problem into three subproblems using block coordinate descent. For the subproblem of intra-pair power allocation within each NOMA user pair, we construct a supermodular game with confirmed convergence to a Nash equilibrium. Given the intra-pair power allocation, successive convex approximation is applied to convexify and solve the subproblem of WPT time allocation and inter-pair power allocation between the user pairs. Finally, we solve the subproblem of UAV placement by using the Lagrange multiplier method. Simulations show that our approach can substantially outperform its alternatives that do not use NOMA and WPT techniques or that do not optimize the UAV location.

Network function virtualization (NFV) implements mobile edge computing (MEC) services as software appliances, and allows resources to be adaptively allocated to accommodate demand variations. Scalability and network cost (including operational cost and response latency) are key challenges. This paper presents a new game-theoretic approach to minimizing the network cost, where access points (APs) select MEC servers and routes in a decentralized manner, and unloaded routers and links are deactivated for cost saving. The key idea is that we interpret the minimization of network cost as a mixed game with a non-monotonic cost function capturing both the operational cost and response latency. We prove that the game is conditionally an ordinary potential game and converges to$\alpha $-approximate equilibriums. A closed-form expression is derived for the convergence delay. Another important aspect is that we integrate Stackelberg routing into the proposed mixed game to avoid inefficient equilibriums (with high cost or latency). We prove that the mixed game can converge faster to better equilibriums under linear response latency models. Extensive simulations corroborate the new game-theoretic approach can significantly outperform existing techniques in terms of efficiency, convergence, and scalability.

Abstract This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems. We first propose a frequency variational Bayesian algorithm, which leverages common sparsity of different sub‐carriers in the frequency domain. The algorithm shares all the information of the support sets from the measurement matrices, significantly improving channel estimation accuracy. To enhance robustness of the frequency variational Bayesian algorithm, we develop a hierarchical Gaussian prior channel model, which employs an identify‐and‐reject strategy to deal with random outliers imposed by hardware impairments. A support selection frequency variational Bayesian channel estimation algorithm is also proposed, which adaptively selects support sets from the measurement matrices. As a result, the overall computational complexity can be reduced. Validated by the Bayesian Cramér‐Rao bound, simulation results show that, both frequency variational Bayesian and support selection‐frequency variational Bayesian algorithms can achieve higher channel estimation accuracy than existing methods. Furthermore, compared with frequency variational Bayesian, support selection‐frequency variational Bayesian requires significantly lower computational complexity, and hence, it is more practical for channel estimation applications.

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.

Most existing optimization objectives considered in non-orthogonal multiple access (NOMA) power allocation schemes are non-delay-sensitive metrics. In order to apply NOMA to various Internet of Things scenarios, the delay must be considered. The effective capacity of users, which characterizes the capacity under specific expiration probabilities, can potentially be a performance metric of statistical delay quality of service (QoS). In this paper, we propose two novel dynamic power allocation schemes with statistical delay QoS guarantee in the uplink NOMA system with paired users. One of the schemes maximizes the sum effective capacity (SEC) of the strong and weak users, which is a non-convex nonlinear optimization problem and is solved by Lagrangian dual decomposition and successive convex approximation (SCA). The other one maximizes the effective energy efficiency (EEE) of uplink NOMA, which is a fractional optimization problem and is solved by integrating the Dinkelbach method, SCA, and Lagrangian dual decomposition. Numerical results show that the SEC and EEE can be significantly improved by the proposed schemes, compared to the existing NOMA and orthogonal multiple access power allocation schemes.

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms.

Pattern division multiple access (PDMA) is a novel non-orthogonal multiple access (NOMA) solution to the problem of massive connection and higher spectral efficiency for the fifth generation (5G) wireless networks. The performance of PDMA is determined by the gain brought by the PDMA pattern and is generally related to the inner product of the pattern. This paper first sets up the system model of uplink (UL) and downlink (DL) PDMA, and formulates the pattern matrix design principles for enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable and low latency communications (URLLC). In this paper, the design criteria and examples of 5G application scenarios are given, and the performance of different PDMA pattern matrices is compared and analyzed by link-level simulation. The effectiveness of the proposed PDMA pattern matrix design principles is verified by Monte Carlo simulation.

To enable ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT), a sparse-code multiple-access (SCMA)-enhanced full-duplex (FD) scheme (FD-SCMA) is proposed in this article. FD-SCMA can support short-packet transmissions of several SCMA users in the uplink (UL) and downlink (DL) simultaneously by an FD next generation node B (gNB). First, the gNB and UL users can generate and superpose signals according to the preconfigured SCMA codebooks, and simultaneously transmit the signals via occupied subcarriers in a joint SCMA pattern. The receivers at the gNB and DL users can demodulate and decode the signals with multiuser detection (MUD). With the imperfect self-interference suppression (SIS) of FD considered, the effective signal-to-noise ratio (SNR) of FD-SCMA at the gNB and DL users is formulated. The error probability of FD-SCMA in the UL and DL is also derived under a given transmission latency constraint of short-packet transmissions. In the stationary flat-fading channel, it is proved that FD-SCMA can achieve better reliability than the existing FD and SCMA schemes. In the time-invariant frequency-selective fading channel, the upper bounds for error probability of the UL and DL users in FD-SCMA are derived, respectively. Through the theoretical calculation and Monte Carlo simulation, it is verified that the superiority of FD-SCMA in supporting ultrareliable and low-latency short-packet transmissions in IoT.

It is challenging to satisfy the critical requirements of ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT) under severe channel fading. The emerging massive multiuser multiple-input-multiple-output (MU-MIMO) concept is applied in IoT networks under shadow fading, enabling URLLC with pilot-assisted channel estimation (PACE) and zero-forcing (ZF) detection. Assuming users are uniformly and randomly deployed under log-normal shadow fading, the probability density function (pdf) of postprocessing signal-to-noise ratios (SNRs) is derived for the uplink (UL) of massive MU-MIMO with perfect channel state information (CSI) and imperfect CSI obtained by PACE. Then, finite blocklength (FBL) information theory is utilized to derive the error probability of accessing users with a given latency, thereby evaluating the reliability of massive MU-MIMO for short-packet transmissions. Further, the length of pilots to minimize the error probability can be decided by the golden section search method (GSSM), which can converge rapidly. Numerical results verify that massive MU-MIMO can support a large number of UL URLLC users even when users are randomly deployed under shadow fading.

In this paper, we study a two-way relay network (TWRN), in which eavesdroppers' channel state information is imperfect, and all the eavesdroppers collude to form joint receive beamforming for enhanced receptions. To ensure the robustness of the TWRN, we design the transmit beamforming and establish two secure beamforming design formulations, which combines the physical layer network coding with artificial noise technique at the relay. One secure beamforming design formulation is to minimize the total transmit power in the TWRN, and the other one is to guarantee a maximum secrecy sum rate (SSR) in the worst-case. With the help of approximation techniques, the former optimization problem is converted into a convex problem, whose semidefinite relaxation solution is rank-one. However, due to the maximum available power limit of the system, the power optimization problem may be infeasible. To overcome this problem, the SSR optimization formulation is presented, and the optimization problem is decoupled into two subproblems that can be transformed into the convex forms. Finally, it is verified by the numerical results that the proposed schemes are effective.

Compared with the conventional mobile edge cloud (MEC) network, the network function virtualization (NFV)-enabled MEC network provides new flexibility on the MEC service deployment. Resource wastage owing to dynamic workloads in traditional MEC networks can be overcome through adaptive resource allocation. In this paper, we investigate the resource allocation problem to minimize the operational cost (e.g., energy consumption, capital expenditure) and the average response time in the NFV-enabled MEC network. We consider the problem from the perspective of MEC service deployment, assignment, and routing among the access points (APs) and MEC servers. We propose an user-network cooperation-based algorithm with low-complexity. In the proposed algorithm, the network announces a path-switching rule (i.e., α-approximate deviation) with proportionally shared operational cost, while the APs selfishly choose their paths with the least cost accordingly. We analyze the selfish behaviors of APs with game theory. We prove existence and convergence of α-approximate equilibriums. Also, we evaluate the efficiency of the equilibriums with the price of stability (POS). Furthermore, an enhanced algorithm based on public service advertising (PSA) is proposed to improve the convergence performance and equilibriums efficiency. Through simulations, we show the superiority of the proposed algorithms over existing algorithms (e.g., BnB-SD and greedy routing) on the accuracy and convergence performance (measured by the overall path switching).

Mobile edge computing (MEC) is a promising technique which enables the user equipment (UE) to leverage the vast computation resources on the clouds (or cloudlets). The redundant design and dynamic nature of traffic raise an energy inefficiency issue in MEC network. In this paper, we aim to minimize the energy consumption and average response time in the MEC network. We jointly consider the cloud selection and routing optimization on both wired and wireless links. Based on the game theory, we propose a low-complex resource allocation algorithm, which can achieve the global optimal solution. Further, to reduce the number of re-routing (routing times), an improved algorithm is proposed, which introduces an approximate factor (i.e., β). The β represents the additional cost during the re-routing, such as session migrations, energy consumption. We demonstrate the convergence of the improved algorithm. The simulations show that the proposed algorithms outperform the other conventional algorithms.

Pattern division multiple access (PDMA) is a multi- carrier non-orthogonal multiple access (NOMA), which can meet the requirements of massive user connections and super-high data rate in the fifth generation (5G) wireless networks. In this paper, we work on the optimization of power allocation to improve the performance in the down-link PDMA system with imperfect channel state information (CSI) at transmitter. The outage throughput of the system is maximized by optimizing the power allocation under the constraints of maximum transmits power, minimum user data rate, and outage probability. Since this optimization problem is a probabilistic mixing problem, we first turn it into a non-probability problem. Then, assuming the pattern matrix is known, we propose an iterative power allocation scheme. The closed-form expression of power allocation is derived based on Karush-Kuhn-Tucker (KKT) conditions. The simulation results demonstrate that the proposed iterative power allocation scheme yields better performance over the existing schemes.

Pattern division multiple access (PDMA), which can exploit time, frequency, spatial resources or any combination of these resources, has been a promising candidate multiple access technology for the fifth generation (5G) wireless systems. Addressing the high-reliability, low-latency and massive-connectivity requirements of 5G systems is becoming one of the most promising research trends. In this paper, we combine the PDMA with the full-duplex relay and propose a cooperative PDMA (Co-PDMA) system to improve the reliability of cell-edge users with a short delay. We analyze the outage performance of the Co-PDMA system when the instantaneous channel state information could or could not be available at the transmitter. In addition, we also compare the outage performance in the cooperative and the non-cooperative PDMA system when the selection combining (SC) or the maximum ratio combining (MRC) scheme is utilized at the receiver. The numerical results show that the proposed Co-PDMA system can obviously improve the outage performance, and the MRC scheme can further improve the system reliability. Moreover, when the instantaneous channel state information is known at the transmitter, the PDMA with successive interference cancellation (SIC) decoding has a great advantage on improving reliability compared with the conventional orthogonal multiple access (OMA).

With the emergence of the mission-critical Internet of Things applications, ultra-reliable low-latency communications are attracting a lot of attentions. Non-orthogonal multiple access (NOMA) with multiple-input multiple-output (MIMO) is one of the promising candidates to enhance connectivity, reliability, and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in the downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove that the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA are Cauchy convergent and can be asymptotically approached by a finite truncated binomial series in the closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. The extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition.

Pattern division multiple access (PDMA) is a multi- carrier non-orthogonal multiple access (NOMA), which can meet the requirements of massive user connections and super-high data rate in the fifth generation (5G) wireless networks. In this paper, we work on the optimization of power allocation to improve the performance of the downlink PDMA system significantly. Considering the perfect channel state information (CSI) is acquired at the transmitter, we propose an iterative power allocation (IPA) scheme based on the integration of the iterative subgradient method and the Mann iterative method. Moreover, Lagrange multipliers and the allocated power are updated until converged in each iteration. It is demonstrated in simulation results that the achievable sum throughput superiority of our proposed scheme over other schemes.

High reliability and low latency, increasingly demanded by mission critical IoT applications, are the two key requirements for modern wireless communication systems. In this paper, we consider a battery-limited wireless machine type communication network where non-orthogonal multiple access (NOMA) is embedded to support massive connectivity. Hence, energy efficient NOMA transmission under statistical delay constraints is required to prolong the battery lifetime of the devices. We firstly derive the probabilistic upper bounds of the queueing delays of NOMA devices via the (min,×) stochastic network calculus. Then, we propose a transmit power optimization algorithm based the probabilistic delay bounds. Simulation results verify the tightness of the derived upper bound of the delay violation probability and thus the effectiveness of the proposed power control algorithm.

The explosive growth of user traffic brings a great pressure on evolved packet core (EPC) caused by huge numbers of emerging mobile intelligent applications. An EPC with good flexibility and scalability is required. In this paper, we proposed a virtualized and programmable EPC architecture (NOS-EPC) by leveraging the framework of not only stack (NOS) framework in order to satisfy the stringent requirements in beyond 5G (B5G) network. The global controller (GC) and the global network view (GNV) are established for the NOS-EPC. The control plane (C-Plane), the user plane (U-Plane) and the management plane (M-Plane) for the NOS-EPC, which are mutual and decoupled, are realized. NS3 based simulations are performed to verify the performance of the NOS-EPC. We compare the proposed NOS-EPC with different LTE solutions, including the LTE/EPC and software- defined network based EPC (SDN- EPC). The results show that the NOS-EPC can efficiently improve the EPC performance on the aspects of procedure duration and signaling overheads.

To meet the requirements of high energy efficiency (EE) and large system capacity for the fifth-generation Internet of Things (IoT), the use of massive multiple-input multiple-output technology has been launched in the massive IoT (mIoT) network, where a large number of devices are connected and scheduled simultaneously. This paper considers the energy-efficient design of a multipair decode-and-forward relay-based IoT network, in which multiple sources simultaneously transmit their information to the corresponding destinations via a relay equipped with a large array. In order to obtain an accurate yet tractable expression of the EE, first, a closed-form expression of the EE is derived under an idealized simplifying assumption, in which the location of each device is known by the network. Then, an exact integral-based expression of the EE is derived under the assumption that the devices are randomly scattered following a uniform distribution and transmit power of the relay is equally shared among the destination devices. Furthermore, a simple yet efficient lower bound of the EE is obtained. Based on this, finally, a low-complexity energy-efficient resource allocation strategy of the mIoT network is proposed under the specific quality-of-service constraint. The proposed strategy determines the near-optimal number of relay antennas, the near-optimal transmit power at the relay, and near-optimal density of active mIoT device pairs in a given coverage area. Numerical results demonstrate the accuracy of the performance analysis and the efficiency of the proposed algorithms.

Massive connectivity and low latency are two important challenges for the Internet of Things (IoT) to achieve the quality of service provisions required by the numerous devices it is designed to service. Motivated by these challenges, in this paper we introduce a new millimeter-wave nonorthogonal multiple access (mmWave-NOMA) transmission scheme designed for cellular machine-to-machine (M2M) communication systems for IoT applications. It consists of one base station (BS) and numerous multiple machine type communication (MTC) devices operating in a cellular communication environment. We consider its down-link performance and assume that multiple MTC devices share the same communication resources offered by the proposed mmWave-NOMA transmission scheme, which can support massive connectivity. For this system, a novel MTC pairing scheme is introduced the design of which is based upon the distance between the BS and the MTC devices aiming at reducing the system overall overhead for massive connectivity and latency. In particular, we consider three different MTC device pairing schemes, namely: 1) random near and the random far MTC devices; 2) nearest near and the nearest far MTC devices (NNNF); and 3) nearest near and the farthest far MTC device. For all three pairing schemes, their performance is analyzed by deriving closed-form expressions of the outage probability and the sum rate. Furthermore, performance comparison studies of the three MTC device pairing schemes have been carried out. The validity of the analytical approach has been verified by means of extensive computer simulations. The obtained performance evaluation results have demonstrated that the proposed cellular M2M communication system employing the mmWave-NOMA transmission scheme improves outage probability as compared to equivalent systems using mmWave with orthogonal multiple access schemes.

Cloud radio access network (C-RAN) centralizes several baseband units to form a pool, which is the primary form of the wireless network virtualization. Some aspects of C-RAN are always developing, such as the processing capacity and multiple radio access technology (multi-RAT) convergence. In this paper, the Not Only Stack (NO Stack) framework, as a virtualization approach, is suggested to be employed in 5G radio access network (RAN). NO Stack is programmable, flexible, and sustainable, while adopting the mature virtualization technology to achieve a fully virtualized RAN (vRAN). With RAN slicing and network orchestration schemes, the baseband processing and storage resources could be sliced and orchestrated to realize the multi-RAT convergence and flexible reconfiguration. By reconstructing the dedicated and default bearer establishment procedure in long term evolution (LTE), NO Stack framework reduces the signaling and delaying respectively. Seen from the analysis and demonstration, the vRAN based on NO Stack can support multi-RAT convergence and flexible networking, as well as reduce the signaling and delay.

Pattern Division Multiple Access (PDMA) is a novel non-orthogonal multiple access scheme proposed to meet the demand of massive connection in the future 5G communications. PDMA is based on the joint design of transmitter and receiver. The multiuser signals are superposed on the multiple signal domains based on different characteristic patterns at the transmitter side, and the successive interference cancellation (SIC) is used to separate the multiuser signals at the receiver side. In this paper, we proposed the enhanced technology of PDMA, named as interleaver-based PDMA (IPDMA). IPDMA scheme could distinguish different user based on different bit-level interleavers, different characteristic patterns, and different combinations of bit-level interleaver and characteristic pattern. Then the iterative decoding and detection was used at the receiver to separate multi-users. Simulation results showed that the proposed IPDMA could improve the block error rate (BLER) performance, compared to the PDMA. And analysis indicated that the complexity of the IPDMA scheme is closed to the PDMA scheme.

Pattern Division Multiple Access (PDMA) is a novel non-orthogonal multiple access scheme proposed to meet the diverse demands on high capacity and large number of connections in the fifth generation (5G) wireless networks. PDMA uses the characteristic pattern to define the sparse mapping from data to a group of resources, and the sparsity of the pattern gives impacts on the capacity performance and detection complexity. In this paper, we considered the pattern assignment and power allocation in downlink PDMA system. The Joint Pattern assignment and Power Allocation (JPPA) scheme based on the optimum Iterative Water-Filling (IWF) algorithm was proposed to optimize the total throughput of all users. The simulation results demonstrated that the proposed JPPA scheme can improve the sum throughput significantly, compared to the Random Pattern assignment and IWF Power Allocation (RPPA) scheme.

As a promising radio access technology for future 5G network, novel multiple access (NMA) is aimed to improve spectrum efficiency and access capability via design of successive interference cancellation (SIC) amenable pattern in single domain (e.g. power domain, code domain, and space domain) or joint multiple domains. In this paper, a pattern in joint space and power domain is designed for NMA system. The proposed pattern jointly adopts a quasi-orthogonal space-time block code (Q-OSTBC) in space domain and power allocation (PA) scheme for users in power domain, which is in conjunction with SIC based linear receiver. The pattern utilizes Q-OSTBC with unequal diversity order to mitigate the error propagation in the SIC based detector for each user, and PA for multiple users with different channel gain to improve the spectrum efficiency. Simulation results show that the designed pattern can effectively mitigate error propagation in SIC based system. We also investigate the performance of SIC based receiver with or without channel decoding during the reconstruction of interference, simulation results indicate that the reconstruction with decoding outperform the one without decoding, especially under the condition of low signal-to-noise ratio (SNR). Due to the diversity gain, the NMA system using the designed pattern can obtain higher average sum rate compared to its counterpart, i.e. the combination of Vertical Bell Layered Space-Time code (VBLAST) and PA.

This paper considers imperfect channel state information (CSI) in the downlink precoding of cellular multi-user large-scale distributed antenna system (DAS). Specifically, the uncertainty of channel estimation is mainly caused by the pilot reuse among users in adjacent cells. The phenomenon is termed as pilot contamination in a multi antenna system. Since the large-scale fading may vary from antenna to antenna, it's not easy to jointly estimate the channels from all antennas to a certain user. Instead, this paper only jointly estimates the downlink channels of the antennas at the same site, therefore with the same large-scale fading. Consequently, the channel estimation is executed in a distributed manner while the subsequent precoding is done in a centralized way. Based on the analysis of the properties of the DAS channel estimations, a multi-cell MMSE precoding scheme is proposed which aims to minimize the sum of intra- cell interference and the interference the cell pours to other cells. An approximation of the multi-cell MMSE precoding is further derived to reduce the computational complexity. Simulation results show significant performance gains over traditional singlecell precoding schemes.

Ultra dense network (UDN) is identified as one of the key enablers for 5G since it can provide ultra high spectral reuse factor exploiting proximal transmissions. By densifying the network infrastructure equipments, it's highly possible that each user will have one or more dedicated serving base station antennas, introducing the user-centric virtual cell paradigm. However, due to irregular deployment of large amount of base station antennas, the interference environment becomes rather complex, thus introducing severe interferences among different virtual cells. This paper focuses on the downlink transmission scheme in UDN where a large number of users and base station antennas are uniformly spread over a certain area. An interference graph is first created based on the large- scale fadings. Then, base station antennas and users in the virtual cells within the same maximal connected component are grouped together and merge into one new virtual cell cluster, where users are jointly served via zero-forcing beamforming. A multi-virtual-cell minimum mean square error precoding scheme is further proposed to mitigate the inter-cluster interference. Simulation results show that the proposed interference graph based virtual cell merging approach can attain the average user rate performance of the grouping scheme based on virtual cell overlapping with smaller virtual cell size and reduced signal processing complexity.

The aim of SON (Self-Organizing Network) is to realize the autonomic function of the wireless network by self-configuration, self-optimization and self-healing, which reduces the human intervention and improves the user experience. Self-configuration is a process where newly deployed eNodeB are configured by automatic installation procedures to get the necessary basic configuration for system operation. Self-optimization is a process that continuously monitors an environment and automatically optimizes various parameters when the environment changes. Self-healing is a process that detects and localizes failures, then fixes the problems automatically. This paper gives a comprehensive introduction to the SON functionalities and outlines the framework of self-configuration, self-optimization and self-healing. Some concrete algorithms are proposed for self-optimization and self-healing, which include capacity and coverage optimization and cell outage detection and compensation respectively. Simulation results in various scenarios are provided to evaluate the performance of the proposed algorithms.

In order to satisfy the demand of future Broadband Wireless Access (BWA) with high mobility, high data rate and low cost, Tsinghua University proposes a novel BWA system named BRadio with independent intellectual property rights. This paper describes the BRadio system from the aspects of physical layer technologies, Medium Access Control (MAC), networking methods, and services. The physical layer technologies focus on Time domain and Frequency domain United Orthogonal Frequency Division Multiple Access (TFU-OFDMA), Time domain and Frequency domain United Single Carrier Modulation time division multiple Access (TFU-SCMA), Interleave Division Multiple Access (IDMA), and Multiple Input Multiple Output (MIMO). In MAC layer we describe several new functionalities in BRadio system. The networking methods of BRadio are divided into networking independently and networking with the existing networks. BRadio can support various services, among which, the Personal Media Service (PMS) is presented in detail. Relying on the technical superiority of its high data rate, high mobility and low cost, as well as its flexible networking and comprehensive service platform, BRadio has become a potential technical solution for BWA.

Energy detection has a low complexity of receiver structure and requires no information about primary signal. However, a single user detection has been proved vulnerable to the noise uncertainty, which causes the existence of a SNR wall belowwhich the detector cannot robustly a chieve the given detection requirement. Compared to single user detection, cooperative energy spectrum sensing algorithms can increase the detection probability significantly. In this paper we expound the SNR wall in a new perspective and analyze the SNR wall phenomenon under typical cooperative energy spectrum sensing algorithms. Besides, experimental tests have been made to study the influence of threshold setting methods to the detection performance. Analysis and simulation results show that cooperative sensing and suitable threshold setting are hopeful to decrease the limitation of SNR wall phenomenon.

Massive Multiple-Input Multiple-Output (MIMO) is an emerging research field due to the increasing demand of spectral efficiency in wireless broadband systems, such as Long Term Evolution (LTE) which is proposed by 3GPP (Third Generation Partnership Project). Precoding is important to exploit the performance of massive MIMO, and codebook design is crucial due to the limited feedback channel. In this paper, we propose a novel double codebook design method based on a Kronecker-type approximation of the array correlation structure for the uniform rectangular array, which is preferable for the antenna deployment of massive MIMO systems. As a case study, we run simulations based on assumptions of the LTE-Advanced system which is extended to 32 dual-polarized antennas. The results verify that the proposed codebook design has remarkable performance gain compared to the traditional double codebook under the uniform rectangular array setup.