In this paper, we propose an energy-efficient resource allocation scheme for the multi-user multi-channel adaptive semantic-bit communication in emergency networks with the text transmission emergency task. Specifically, we first analyze the semantic energy efficiency for both semantic and bit communication modes. Then, we formulate a network-level semantic energy efficiency maximization problem by jointly optimizing the communication mode decision, channel selection, semantic coding length, power control, and computation resource allocation. To solve the formulated problem, we propose a fractional programming based hierarchical coalitional game (FP-HCG) algorithm. To be specific, we first use the Dinkelbach method to transform the fractional objective function into a parametric form such that the optimal solution of the original problem can be obtained by updating the parameter through iterations. In each iteration, we solve the transformed problem by formulating a two-layer coalitional game, where the coalitional game is adopted to determine the channel selection in the upper layer, and the genetic algorithm is adopted to determine the communication mode decision and resource allocation of each coalition in the lower layer. Simulation results demonstrate that the proposed scheme can efficiently improve the network energy efficiency for emergency communications.

The multi-access edge computing (MEC)-enabled low Earth orbit (LEO) satellite network is a promising approach to meet the growing ubiquitous diverse computation demands around the world. In this paper, a joint task offloading and resource allocation strategy is proposed for hybrid MEC-enabled LEO satellite networks, where two types of MEC tasks, namely delay-sensitive edgy-cloud task and data-and computation-intensive cloudy-edge task, are considered simultaneously. Specifically, we first design the cost functions for the two types of tasks, which take the delay-sensitive feature of edgy-cloud task and data-and computation-intensive characteristics of cloudy-edge task into consideration. Then, an overall terminal cost minimization problem is formulated for task offloading and resource allocation under the communication and computation capability constraints and the service delay requirements. In practice, terminals usually only care about their own costs, but satellites pursue the overall cost minimization of all the served terminals. Thus, considering the individual and collective rationality simultaneously, a two-level hierarchical game is constructed to solve the formulated problem. In the upper level, a hedonic coalition formation game is established, which enables each terminal to make the coalition selection and task offloading decision based on the designed coalition switch rule. In the lower level, the joint channel and power allocation in each coalition is first formulated as a noncooperative game to represent the individual rationality of each terminal. Then, each satellite performs the optimal computation resource allocation to maximize the coalition value with collective rationality. We prove that the Nash equilibrium (NE) for the noncooperative game exists and the coalition partition converges to a Nash stable state. Simulation results are provided to demonstrate the superiority of the proposed strategy.

Millimeter-wave (mmWave) grant-free (GF) access is a promising approach for massive machine-type communication (mMTC) networks to improve the access efficiency and alleviate the shortage of spectrum resources. Due to the lack of authentication, mmWave GF-mMTC networks are vulnerable to the pilot contamination attack (PCA), which can cause severe performance degradation of the channel estimation (CE) and user activity identification (UAI). However, the existing PCA resistance schemes for mmWave GF-mMTC networks perform the CE, UAI, and PCA detection through two separated phases, which will limit the system performance. To solve the problem, we establish a three-dimensional (3-D) transmission model with time-correlated two-dimensional sparsity for mmWave GF-mMTC networks under PCA, where the user activity sparsity, the virtual angular channel sparsity, and the temporal correlation of legitimate user (LU) status are jointly considered. Based on the established transmission model, we develop a 3-D compressive sensing based joint CE, UAI, and PCA detection (3D-CS-JCUPD) scheme. In this scheme, a parallel expectation-maximization vector approximate message passing with multiple measurement vector (Parallel EM-VAMP-MMV) algorithm is proposed to estimate the channel virtual representation (CVR) and the LU status is identified with the aid of different temporal correlation features between LUs and attackers. Moreover, we also develop a location information aided joint CE, UAI, and PCA detection (LIA-JCUPD) scheme to address the situation when attackers and LUs exhibit similar temporal correlations, where the BS utilizes the recorded LU location information to distinguish the LU status. Simulation results show that the developed schemes can achieve substantial performance gains over several reference schemes.

In the upcoming sixth generation (6G) era, dynamic routing relying on link state information update is crucial for global data service in large-scale low-earth orbit (LEO) satellite networks. However, the existing dynamic routing methods use a static link state update scheme where all satellites distribute their link state information with the same fixed period, while the link state of the satellites are different and vary dynamically. This makes it difficult to achieve the balance among various network performance metrics such as link state update accuracy, signaling overhead, network throughput, and energy efficiency. To solve this issue, we propose an adaptive link state update scheme for the LEO satellite network, where each satellite can dynamically adjust its own link state distribution interval according to the observation on the inter satellite links (ISLs). Based on the proposed scheme, we define the information deviation to characterize the accuracy of the link state update and derive the signaling overhead of link state distribution. To improve further the network performance, a multi-objective optimization problem (MOP) is formulated to minimize the information deviation and the signaling overhead simultaneously. By applying the weighted sum method, we convert the formulated MOP into a single-objective optimization problem (SOP). Then, we adopt the distributed reinforcement learning approach and develop the deep Q-network (DQN) algorithm for each satellite to learn its optimal link state distribution decision strategy based on local information. Simulation results demonstrate the superiority of the proposed scheme.

Beam Hopping (BH) is a promising approach to support the dynamically varied and non-uniformly distributed ground traffic demands with limited satellite beam resources. However, almost all the existing BH schemes only focus on the overall cell-level traffic demands without considering the transmission demand of each user, which may degrade the system performance. To address this issue, in this paper, a user-level dynamic BH scheme for low Earth orbit satellite networks is proposed, where the user-level real-time traffic demands are integrated into the BH pattern design. Specifically, by considering the user-level transmission demands in the multi-satellite and multi-cell scenario, we formulate an optimization problem that aims to maximize the overall long-term throughput of the network by jointly optimizing the BH pattern and access control (AC) strategy. To solve the formulated problem, we first establish a user-oriented Markov decision process framework, based on which the original long-term optimization problem can be converted to a short-term sum value maximization problem. Then, a deep reinforcement learning assisted enhanced genetic algorithm is proposed to solve the converted short-term optimization problem, where the deep reinforcement learning is adopted to estimate the long-term state-action values and the enhanced genetic algorithm is used to determine the BH pattern and AC strategy with a low complexity according to the estimated state-action values. Simulation results show that the proposed scheme can achieve better performance over existing methods.

To integrate efficiently the emerging massive machine-type communications (mMTC) into the fifth generation (5G) and beyond 5G (B5G) cellular networks, we design a two-hop data aggregation and forwarding mechanism for the machine-type communications (MTC) to coexist with the traditional human-to-human (H2H) communications. Specifically, by sharing the channel resources originally allocated to H2H user equipments (HUEs), the activated MTC devices (MTCDs) first send small-sized data packets to the associated MTC gateways (MTCGs). Then, the MTCGs aggregate the received data packets and forward them to the base station (BS). The data transmission of MTCGs and HUEs are scheduled by the BS such that there is a competition relationship between the forwarding MTCGs and HUEs. To limit the influence of mMTC on the H2H service, we set different scheduling weights for the forwarding MTCGs and HUEs to control the transmission opportunities of the two types of services. Based on the stochastic geometry (SG) theory, we develop an analytical framework to characterize signal-to-interference ratio (SIR) during the intra-group aggregation and MTCG forwarding phase. Using this SIR analytical framework, we derive the data aggregation success probability of each MTCD and the average throughput of each forwarding MTCG. We also conduct simulations to evaluate the performance of the mMTC and H2H service under varying parameter settings, which indicates the trade-off between the system settings of the data aggregation phase and MTCG forwarding phase.

Due to the lack of authentication, millimeter-wave (mmWave) grant-free massive machine-type communication (GFmMTC) networks are vulnerable to the pilot contamination attack (PCA), which will cause serious performance degradation of channel estimation (CE) and active user detection (AUD). However, the existing works towards the PCA detection in the mmWave GFmMTC networks perform the CE, AUD, and PCA detection through two separated phases, which will limit the system performance. To solve the problem, in this paper, we establish a three-dimensional transmission model with time-correlated two-dimensional sparsity for both legitimate users (LUs) and attackers in mmWave GFmMTC networks, where the LU and attacker activity sparsity, the virtual angular channel sparsity, and the temporal correlation of L U activity are jointly considered. Based on the established transmission model, a three-dimensional multiple measurement vector-compressive sensing (MMV-CS) based joint CE and AUD scheme against PCA is proposed. Specifically, we first formulate the joint CE and AUD under PCA as a three-dimensional MMV-CS problem. Then, by utilizing the sparsity of user activity and angular virtual channel, we develop a parallel expectation-maximization vector approximate message passing with MMV (Parallel EM- VAMP-MMV) algorithm to efficiently solve the formulated problem. Simulation results show that the proposed scheme can achieve a substantial performance gain over comparison methods.

The fifth-generation (5G) and beyond networks are expected to accommodate both the original human-to-human (H2H) communication and the emerging massive machine-type communication (mMTC). To enable a harmonious coexistence between the two different types of services, we propose a resource-efficient mMTC/H2H coexistence scheme by jointly considering the random access (RA) and data transmission, where the entire uplink resources are divided for the proposed RA and data transmission procedures. Based on the proposed scheme, we derive the average achievable throughput of the bursty mMTC service and develop a time-nonhomogeneous Markov chain model to characterize the joint state transition of H2H user equipments (HUEs). To tackle the cumbersome Markov model, we approximately decompose the constructed time-nonhomogeneous Markov model into multiple independent Markov chains, where each decomposed Markov chain characterizes one single HUE’s state transition. Then, the decomposed Markov model is transformed into a semi-Markov process and the corresponding steady-state condition is obtained based on the queueing network analysis for H2H service. By approximating the evolution of number of HUEs in different states as M/M/1 queues, we derive the stationary probabilities for the embedded Markov chain of the semi-Markov process and obtain the data transmission success probability of each HUE. Based on the abovementioned analytical framework, we formulate a constrained nonlinear integer programming (NLIP) problem to maximize the mMTC throughput under the constraints of H2H quality-of-service (QoS) stabilization and resource allocation. By adopting the modified particle swarm optimization (PSO) algorithm, we solve the formulated problem and obtain the efficient resource allocation strategy for the mMTC/H2H coexistence. Simulation results demonstrate that the developed analytical framework and modified PSO algorithm achieve close to the optimal mMTC/H2H coexisting performance and can be adapted to various network settings.

To satisfy the highly heterogeneous requirements of Internet of Things applications for the sixth-generation (6G) networks, the machine-type communication (MTC) aims to support multiple types of services having diverse traffic demands, which will cause significant challenges in the grant-free based channel estimation (CE) and multi-user detection (MUD) scheme design for massive MTC (mMTC) networks. To address this challenge, we develop a multi-state Markov chain based transmission model to characterize the diverse time-varying traffic demands for MTC users, where the temporal correlation of user activity and the data length diversity are jointly exploited. Based on the developed transmission model, a multi-service oriented joint CE-MUD scheme is proposed to realize the efficient CE, user activity identification and data detection. Specifically, we first construct the joint block sparse structure for the transmitted pilot and data signals to fully explore the structured sparsity of the pilot and data symbols. Then, we convert the joint CE-MUD into a maximum a posteriori probability (MAP) problem such that the block sparsity of the transmitted signals and the diverse traffic demands provided by the established transmission model can be efficiently exploited. Moreover, we further develop an adjustable prior probability aided Bayesian sparsity adaptive matching pursuit (APP-BSAMP) algorithm to efficiently solve the formulated MAP problem. In the proposed algorithm, we first adjust the prior user activation probabilities through the approximate message passing (AMP) based detector to reduce the impact of the misestimation of user transmission status. Then, we jointly reconstruct the transmitted pilot and data signals under the Bayesian pursuit framework, where the active user set is obtained by maximizing the posterior probabilities. Simulation results show that the proposed scheme can achieve a substantial performance gain over existing methods.

The diverse time-varying transmission demands cause significant challenges in the grant-free based multi-user detection (MUD) scheme design for massive machine-type communications (mMTC) networks. In this paper, we develop a multistate Markov model to characterize the diverse time-varying traffic demands, where the temporal correlation of the user activity and the data length diversity are considered simultaneously. Based on the developed Markov model, a diverse traffic demands oriented MUD scheme is proposed to realize the efficient joint user activity and data detection. Specifically, we first construct the block sparse structure for the transmitted signal to fully exploit the structured sparsity of the data matrix. Then, we convert the MUD into a maximum a posteriori probability (MAP) problem such that the block sparsity of the transmitted signal and the temporal correlation and data length diversity provided by the established Markov model can be efficiently exploited. Moreover, we further develop an intra-block pruning aided Bayesian block orthogonal matching pursuit (IBPA-BBOMP) algorithm such that the formulated MAP problem is efficiently solved. Simulation results show that the proposed scheme can achieve a substantial performance gain over existing methods.

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes.

As device-to-device (D2D) caching technology allows a number of devices to cache some particular contents, requesters can obtain these contents directly from these neighbor devices rather than the base station (BS) and thus the burden can be efficiently reduced. However, the required time consumptions for caching contents, which may significantly affect the network delay performance, are ignored in the existing schemes. Consequently, a delay-driven caching and sharing strategy is proposed in this paper. Specifically, in the proposed strategy, each D2D device can obtain contents from BS and play as the cache device (CD). Moreover, the consumed time for CDs is integrated into the strategy design. Then, three kinds of delay, which are the delay for CDs to cache contents and the delay for requesters to get the required files from BS and CDs, respectively, are considered simultaneously. We formulate an optimization problem, which aims at minimizing the overall average network delay subject to the successful transmission probability as well as the content cache and request constraints. To solve the formulated complex non-convex problem, the original problem is divided into three subproblems and efficiently solved in an iterative manner. Moreover, as the convergence for solving the three subproblems are proved, the convergence of the developed iterative algorithm can be guaranteed. Simulation results demonstrate that the proposed strategy can efficiently reduce the overall average network delay as compared to the existing schemes.

To accommodate massive machine-type communication (mMTC) in the networks originally designed for human-to-human (H2H) communication, we investigate the resource allocation for the mMTC/H2H coexisting network where the conventional random access (RA) and data transmission procedures are tailored for mMTC. The resource allocation strategy jointly consider the resource allocation of physical random access channel (PRACH) and physical uplink shared channel (PUSCH), aiming to support more MTC users while protecting the quality-of-service (QoS) of traditional H2H communication. A Markov chain is utilized to explicitly model the RA and data transmissions of H2H, and H2H's success probability of data transmission is derived under the analysis of stationary distribution. Then, we formulate a nonlinear integer programming (NLIP) problem which aims to maximize MTC throughput while guaranteeing H2H's success probability of data transmission. By solving the optimization problem with a modified particle swarm optimization method, we obtain the resource allocation strategy that achieves a balance between PRACH and PUSCH in terms of resource efficiency. Simulation results demonstrate the superiority of our proposed resource allocation strategy over traditional LTE strategy in the scenario of mMTC/H2H coexistence.

Machine-type communications (MTC) technology, which enables direct communications among devices, plays an important role in realizing Internet-of-Things. However, a large number of MTC devices can cause severe collisions. As a result, the network throughput is decreased and the access delay is increased. To address this issue, a throughput-oriented non-orthogonal random access (NORA) scheme is proposed for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PAs), multiple MTC devices (MTCDs) choosing the same PA can be distinguished and regarded as a non-orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel for transmissions by multiplexing in the power domain. The Sukhatme's classic theory and the characteristic function approach are adopted to formulate an optimization problem. The aim is to maximize the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. Based on the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. The derived solution can be used to adjust the access class barring factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity suboptimal solution is also developed, which can achieve near-PSO performance under high data rate requirement. Simulation results show that the proposed scheme can efficiently improve the network performance and comparison is made with the existing schemes.

Due to the spectrum-sharing feature of cognitive radio networks (CRNs) and the broadcasting nature of wireless channels, providing quality-of-service (QoS) provisioning for primary users (PUs) and protecting information security for secondary users (SUs) are two crucial and fundamental issues for CRNs. Consequently, in this paper, we establish a unified QoS and security provisioning framework for wiretap CRNs. Specifically, different from the widely used deterministic QoS provisioning method and information-theoretical security protection approach, our established framework, which is built on the theory of statistical queueing analysis, can quantitatively characterize the PU's QoS and the SU's security requirements. By adopting the theories of effective capacity and effective bandwidth, we further convert the QoS and security requirements to the equivalent PU's effective capacity and SU's effective bandwidth constraints. Following our developed framework, we formulate the nonconvex optimization problem, which aims at maximizing the average throughput of SU subject to PU's QoS requirement, SU's security constraint, as well as SU's average and peak transmit power limitations. Then, we adopt the techniques of convex hull and probabilistic transmission to convert the original nonconvex problem to the equivalent convex problem and obtain the optimal power allocation scheme through the Lagrangian method. Moreover, we also develop a fixed power allocation scheme which is suboptimal but has low complexity. The simulation results are also provided, which demonstrate the impact of the PU's QoS and the SU's security requirements on SU's throughput as well as the advantage of our proposed optimal power allocation scheme over the fixed power allocation scheme and the conventional security-based water-filling policy.

In this paper, we propose a QoS-driven subchannel and power allocation scheme for security-aware D2D underlaying cellular networks. Specifically, we aim at maximizing the delay QoS constrained average sum throughput of D2D users while meeting cellular users' QoS requirements, D2D users' information security demands, as well as the subchannel and power allocation constraints. To solve our formulated integer-mixed nonconvex problem, we develop a two-step subchannel and power allocation scheme. In particular, by introducing power-splitting variables, the subchannel allocation and power allocation can be decoupled and independently optimized, where the subchannel allocation scheme is based on the classic water-falling algorithm and the power allocation problem is solved via the convex approximation method. Simulation results show that our proposed scheme outperforms the existing method.

In this paper, we establish a unified Quality-of-Service (QoS) and security provisioning framework for wiretap cognitive radio networks (CRN) by employing the theories of statistical queueing analysis, effective bandwidth, and effective capacity, which can quantitatively characterize the QoS and security requirements. Based on our developed framework, we formulate the nonconvex optimization problem that aims at maximizing the average throughput of secondary user (SU) subject to PU's QoS requirement, CRN's security constraint, as well as SU's average and peak transmit power limitations. By using the techniques of convex hull and probabilistic transmission, we convert the original nonconvex problem to the equivalent convex problem and then obtain the optimal power allocation via Lagrangian method. Simulation results demonstrate the impact of PU's QoS and CRN's security requirements on SU's throughput as well as the advantage of our proposed scheme over the fixed power allocation and the conventional security-based water-filling policy.