The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type for the next-generation mobile wireless networks that support a massive number of mobile users (MUs) demanding the diverse and stringent quality-of-services (QoS) on both short delay and low error-rate. Clearly, supporting mURLLC while guaranteeing the security QoS is crucial for implementing the cell-free massive multiple-input-multiple-output (cell-free massive MIMO) mobile network using finite blocklength coding (FBC). Towards these ends, in this paper we propose to develop a new statistical security-QoS provisioning scheme using FBC while simultaneously guaranteeing statistical delay-bounded QoS and error-rate bounded QoS for mURLLC. First, we establish a cellfree massive MIMO channel model for supporting mURLLC between distributed WiFi access points (APs) and multiple mobile users with the existence of an eavesdropper. Second, we maximize the achievable secrecy rate for cell-free massive MIMO while upper-bounding the secrecy-information leak-probability. Third, we define and develop the new metric of secrecy effective capacity for three-dimensional (3D) statistical QoS provisioning over cell-free massive MIMO networks. Finally, we use numerical analyses to validate and evaluate our developed statistical security-QoS guaranteed schemes.

Guaranteeing massive ultra-reliable and low-latency communications (mURLLC) for 6 G wireless networks is of capital importance in both academia and industry. However, how to satisfy the security requirement while ensuring both the delayand error-rate-bounded quality-of-service (QoS) for mURLLC in 6G has not been thoroughly studied. In order to support security, ultra-reliable, and low-latency QoS demands for massive mobile users (MU) in 6 G wireless networks, in this paper we propose and develop the new concept of secrecy$\epsilon$-effective capacity as the performance metric to guarantee the statistical delay-bounded QoS, error-rate bounded QoS, and secrecy-information leakprobability bounded QoS. First, we establish the massive multiple-input-multiple-output (MIMO) channel model for supporting communications between the base station and multiple MUs with the existence of an eavesdropper. Second, we develop the secrecyinformation leak-probability bounded data rate to measure the statistical security performance and derive its closed-form expression under the Rician-fading channel. Third, we define the new concept of secrecy$\epsilon$-effective capacity as the performance metric for secure mURLLC by integrating our developed statistical security QoS provisioning with statistical delay and error-ratebounded QoS provisioning. Finally, we use numerical analyses to validate and evaluate our developed statistical security, statistical delay-bounded, and error-rate-bounded schemes.

Integrated sensing and communications (ISAC) has been proposed to significantly improve the performance of applications through highly-efficient spectrum/hardware sharing between channel-sensing and data-communications. However, how to apply the ISAC technique to accurately sense and estimate the wireless channel state while transmitting the information to mobile users to support massive ultra-reliable and low-latency communications (mURLLC) has imposed many new challenges not encountered before. To address these challenges, in this paper we investigate the channel capacity-distortion tradeoff for ISAC-enabled mURLLC over massive multiple-input multiple-output (MIMO) mobile networks using finite blocklength coding (FBC). First, we establish system models for ISAC-based architectures using massive-MIMO. Second, we define the capacity-distortion function under the distortion constraint for the estimated channel state. Third, we develop a new statistical quality of service (QoS) metric, termed ISAC-based$\epsilon$-effective capacity, to simultaneously guarantee statistical-delay and error-rate bounded QoS by optimizing the sensing-communication power splitting ratio of ISAC. Finally, we use numerical analyses to validate and evaluate our developed ISAC schemes in supporting mURLLC.

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support communications among massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements in 6G. Correspondingly, the integrated sensing and communications (ISAC) technique can improve various QoS performances through spectrum/hardware sharing between target-sensing and data-communications. In addition, the massive multiple-input multiple-output (massive MIMO) and finite blocklength coding (FBC) have been also widely recognized as the powerful 6G techniques to efficiently support mURLLC. However, how to apply the ISAC technique to accurately sense and estimate the wireless channel state while transmitting the information from the base station to MUs to support mURLLC using massive MIMO and FBC has imposed many new difficulties not encountered before. To conquer these challenges, in this paper we propose the new ISAC performance metrics for statistical-QoS guaranteed mURLLC over 6G massive MIMO mobile networks using FBC. First, we establish system models for ISAC-based architectures. Second, we detect targeted MUs and estimate the channel state using the radar sensing echo. Third, we develop a new ISAC performance metric as a function of the data-communication rate and channel state estimation distortion. Finally, we use the numerical analyses to validate and evaluate our developed ISAC performances in supporting mURLLC-oriented QoS requirements.

To satisfy the stringent quality-of-service (QoS) requirements of 6G mobile wireless networks on both channel state estimations and information transmissions, the integrated sensing and communication (ISAC) has been an enabling technique to sense and communicate by sharing the same frequency band and hardware. Simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. However, how to integrate ISAC with SWIPT to support the 6G traffic has imposed many new challenges not encountered before. To conquer these difficulties, in this paper we propose an integrated sensing, communications, and powering (ISACP) scheme for supporting statistical-QoS provisioning over 6G wireless networks using massive multiple-input and multiple-output (massive MIMO) communications. First, we establish system models for our ISACP scheme and define the Cramér-Rao bound and channel estimation distortion under our proposed ISACP scheme. Second, we develop the performance metrics of Cramér-Rao bound and channel estimation distortion to derive the capacity-distortion function to jointly measure the performance of integrated sensing, communication, and powering. Third, we integrate the capacity-distortion function with the finite blocklength coding (FBC) through deriving the ISACP-based ϵ-effective capacity to support the statistical delay and error-rate bounded QoS provisioning in 6G. Fourth, we maximize the energy-efficiency by controlling the optimal wireless power transfer for our ISACP scheme. Finally, we use numerical analyses to validate and evaluate our proposed ISACP scheme.

Since the 6G mobile wireless networks require the high-performances on both channel state estimations and information transmissions, the technique of integrated sensing and communication (ISAC) has attracted considerable research attention due to its ability to sense and communicate by sharing the same frequency band and hardware. However, how to jointly optimize the sensing and communication functions of the ISAC to support the 6G traffic transmissions over a time-varying wireless fading channel has imposed many new challenges not encountered before. To conquer these difficulties, in this paper we propose the ISAC scheme to jointly sense the channel state and transmit the wireless-streaming data using massive multiple-input and multiple-output (massive MIMO) communications over the Rician fading channel. First, we establish the system models for the ISAC scheme under the Rician fading wireless channel and the channel state estimation scheme using the radar sensing feedback. Second, we define the channel state estimation distortion and the capacity-distortion function of a massive MIMO channel to jointly measure the performances of sensing and communication in our ISAC scheme. Third, we integrate the capacity-distortion function with the finite blocklength coding (FBC) scheme by developing the concept of the ISAC-based E-effective capacity to implement the statistical delay and error-rate bounded provisioning for supporting the 6G traffic under our ISAC scheme. Finally, we use numerical analyses to validate and evaluate our proposed ISAC scheme with massive MIMO in the non-asymptotic regime.

The upcoming 6G mobile wireless networks are expected to support massive ultra-reliable and low-latency communications (mURLLC), which is an emerging service that demands more stringent requirements than the fifth generation (5G) wireless networks on delay and error-rate bounded quality-of-services (QoS) with massive connectivity. Finite blocklength coding (FBC) techniques based short packets communication techniques have been shown to be able to support both the statistical delay and error-rate bounded QoS provisioning. Rate splitting (RS) multiple access schemes have been proposed to address the sum degree-of-freedom loss problem over massive multiple-input and multiple-output channels when massive mobile users request to access the network. However, how to support the statistical delay and error-rate bounded QoS provisioning using FBC among massive mobile users to enable the massive access has not been sufficiently studied. In this paper, we propose to integrate FBC with RS techniques to achieve mURLLC transmissions. First, we define the ∊-effective capacity to measure the performance of statistical delay and error-rate bounded provisioning, and obtain a closed-form ∊-effective capacity under the RS scheme. Then, we maximize the aggregate ∊-effective capacity over all mobile users by deriving an optimal transmit power allocation strategy for the RS scheme. Finally, using numerical analyses, we validate and evaluate our proposed RS schemes to support statistical delay and error-rate bounded QoS provisioning over 6G communication networks in the non-asymptotic regime.

Metaverse streaming, as one of the key wireless services over 6G mobile networks, generates the delay/error-sensitive and bandwidth-intensive wireless traffics with stringent quality-of-service (QoS) requirements. Consequently, metaverse streaming can be modeled as a new type of massive ultra-reliable low-latency communications (mURLLC) traffic over 6G mobile networks. However, how to efficiently support metaverse streaming with constrained wireless resources and dynamic network conditions has imposed many new challenges not encountered before. To conquer these difficulties, in this paper we propose the Neyman-Pearson criterion driven network functions virtualization (NFV) and software-defined network (SDN) architectures and optimal resource-allocations for statistical-QoS theory based mURLLC streaming over 6G metaverse mobile networks using finite blocklength coding (FBC). First, we use Neyman-Pearson hypothesis tests for characterizing metaverse streaming requests’ distribution profiles to predict their future accessing frequencies/patterns. Second, our formulated NFV/SDN architectures and virtual-network slices are assigned to the designated metaverse mobile users with the same predicted data request distributions, categories, and statistical-QoS requirements. Third, integrating the statistical QoS theory with FBC, we develop metaverse-streaming schemes by maximizing aggregate$\epsilon $-effective capacity and deriving optimal transmit power allocations. Finally, we use numerical analyses to validate and evaluate our proposed schemes over 6G mobile networks.

The sixth generation (6G) mobile wireless networks are expected to provide a wide range of massive ultra-reliable and low-latency communications (mURLLC) for multimedia transmissions, which require extremely stringent delay and error-rate bounded quality of services (QoS). Massive multiple-input and multiple-output (MIMO) communication has been recognized as one of the promising techniques to support mURLLC thanks to its advantages in the beamforming gain and spatial multiplexing, etc. On the other hand, finite blocklength coding (FBC) based small packets communication technique has recently shown to be able to support the statistical delay and error-rate bounded QoS provisioning. However, how to achieve the statistical delay and error-rate bounded QoS provisioning through massive MIMO techniques has not been sufficiently studied. In this paper, we propose to apply the ∊-effective capacity into massive MIMO communications to achieve the statistical delay and error-rate bounded provisioning for mURLLC traffic transmissions. First, we develop the FBC based scheme over a Nakagami-m fading wireless channel. Then, using the developed FBC based system model, we derive a closed-form expression for ∊-effective capacity of massive MIMO communications, representing the maximum packet's arrival rate that a wireless channel can support under a given stringency of delay requirement and a constrained decoding error-rate. Finally, we use numerical analyses to validate and evaluate our proposed statistical delay and error-rate bounded QoS provisioning scheme over massive MIMO communication networks.

While the fifth generation (5G) of wireless networks is currently being rolled out, wireless networks still need further development to meet the requirements of dramatically increasing numbers of users and new applications and the resulting traffic expected in the coming decade and beyond. The sixth generation (6G) of wireless networks is envisioned to respond to this by providing services with massive access, ultra-reliability, low latency, intelligence, and security while maximizing the spectral/energy/cost efficiency. Unmanned aerial vehicles (UAVs) have attracted considerable research attention due to their mobility and ability to line-of-sight (LoS) coverage in areas that suffer from low channel quality. However, how to characterize a UAV’s coverage area is a challenging problem and has not been thoroughly studied. To address this issue, in this paper we investigate the coverage performance of base station (BS) to UAV communications with a number of interfering mobile users. We first establish a Nakagami-m fading channel model for BS-toUAV wireless communications. Then, we derive a closed-form expression for the UAV’s average coverage probability under the scenario of interfering mobile users. Finally, numerical results confirm our derived analytical results and evaluate the UAV’s performance under different scenarios that anticipate 6G wireless networking models.

The sixth generation (6G) wireless networks are required to provide the massive ultra-reliable low-latency communication (mURLLC) services for massive subscribers, and thus, need to be supported by new techniques. Since the massive multiple-input multiple-output (massive MIMO) technique with massive antennas is able to substantially improve the channel performance, it has been widely applied to achieve the goal of mURLLC networks. Moreover, based on the inherent advantages of high mobility and dynamically deployment, the emerging unmanned aerial vehicle (UAV) technique has also been considered as one of the promising candidate techniques in the 6G wireless networks. However, how to integrate the massive MIMO and UAV techniques has never been thoroughly studied. In this paper, we first establish the massive MIMO channel model between a set of UAVs and a ground station, equipped with uniform rectangular antenna array. Then, we derive the expression of channel capacity for this channel model, which is a function of the distance between each UAV and each antenna. To support the mURLLC traffics in the 6G wireless networks, we employ the effective capacity theory to measure the maximum packet arrival rate, and we also derive the upper-bound on the effective capacity, which is a function of our obtained channel capacity. Finally, we validate and evaluate our derived results of the UAV communication with massive MIMO channel over 6G wireless networks through numerical analyses.

The multiple access technique has been proposed to accommodate a number of heterogeneous communication devices to support a wide variety of applications and services in the sixth generation (6G) mobile wireless networks. Due to the inherent merits in programmability, mobility, and dynamic configuration, unmanned aerial vehicle (UAV) is admitted as the candidate technique for the 6G wireless communication networks. Moreover, UAVs are becoming the important enablers of various applications in military, surveillance, monitoring, supplies delivery, and connection recovery as a temporary hotspot, etc. However, how to efficiently integrate UAV wireless communication system with their trajectory control for 6G networks has neither been well understood nor thoroughly studied. To overcome this challenge, in this paper we propose and develop a control scheme for jointly optimizing UAV coverage probability and trajectory tracking control to efficiently support UAV communications over 6G mobile wireless networks. First, we develop a base station (BS) to UAV communication channel model, and derive the UAV’s coverage probability under the Nakagami-m fading channel. Since the UAV’s coverage probability depends on its relative posture (i.e., position and angle) to the BS and interfering mobile users, we then derive the UAV flying trajectory control scheme to minimize its trajectory tracking error. We also show that our proposed control schemes can attain the Lyapunov stability of trajectory error. Finally, we validate and evaluate our derived results of the UAV trajectory control scheme over 6G networks through numerical analyses.

The large-scale interactive services distribute clients' requests across a large number of physical machine in data center architectures to enhance the quality-of-service (QoS) performance. In parallel and distributed data center architecture, even a temporary spike in latency of any service component can significantly impact the end-to-end delay. Besides the average latency, tail-latency (i.e., worst case latency) of a service has also attracted a lot of research attentions. The tail-latency is a critical performance metric in data centers, where long tail latencies refer to the higher percentiles (such as 98th, 99th) of latency in comparison to the average latency time. While the statistical delay-bounded QoS provisioning theory has been shown to be a powerful technique and useful performance metric for supporting time-sensitive multimedia transmissions over mobile computing networks, how to efficiently extend and implement this technique/performance-metric for statistically bounding the tail-latency for data center networks has neither been well understood nor thoroughly studied. In this paper, we model and characterize the tail-latency distribution in a three-layer parallel and distributed data center architecture, where clients request different types of services and ten download their requested data packets from data center through a first-come-first-serve M/M/1 queueing system. We first define the statistical tail-latency bounded QoS, and investigate the tail-latency problem through generalized extreme value (GEV) theory and generalized Pareto distribution (GPD) theory. Then, we propose a scheme to identify the dominant sources of latency variance in a semantic context, so that we are able to optimize the instructions of those sources to reduce the latency tail. Finally, using numerical analyses we validate and evaluate our developed modeling techniques and schemes for characterizing the tail-latency QoS provisioning theories in supporting data center networks.

Among the promising 6G candidate techniques to support big-data mobile wireless networks, the cell-free massive multi-input-multi-output (CF-M-MIMO) technique has received a great deal of research attentions, where a large number of distributed single-antenna access points (APs), whose ensemble forms a distributed massive MIMO array, simultaneously and jointly serve the single-antenna mobile users. However, CF-MMIMO results in the low efficiencies for big-data mobile networks when the size of the served area increases. To overcome this challenge, the user-centric approach has been integrated with CF-M-MIMO such that APs only serve a selected subset of mobile users rather than all of them. However, the performance of this approach cannot outperform the traditional CF-M-MIMO in overloaded cases, where the number of mobile users is larger than that of APs. To efficiently implement the big-data aware usercentric cell-free massive MIMO system and enhance the spectral and energy efficiencies while reducing the interference, in this paper we propose an efficient non-orthogonal multiple access (NOMA) and user-centric based CF-M-MIMO scheme over 6G mobile wireless networks. Our proposed scheme develops a more efficient central-processing-unit based bipartite graph matching algorithm to select the optimal mobile users served by each AP. Then, we propose the NOMA-aided power allocation and pilot cluster assignment schemes. The numerical results show that our proposed schemes outperform the existing schemes without applying the NOMA technique in terms of mitigating the interference and enhancing the energy efficiency.

Time-sensitive multimedia services and applications aim at satisfying users' stringent requirements on the delay-bounded quality-of-service (QoS). The statistical delay-bounded QoS provisioning theory has been developed to guarantee a given delay-bound with a small violation probability in multimedia wireless networks. The age of information (AoI) theory characterizes the freshness of information, which is the time-difference between the current time and the time-stamp of the latest observation, and thus, is another important theory to analyze the information latency in multimedia wireless networks. This paper proposes to integrate the statistical delay-bounded QoS provisioning theory with the age of information theory to investigate information latency and to improve the delay-bounded QoS performance over a stationary and ergodic first-come-first-serve M/M/1 channel in multimedia wireless networks. First, we define an AoI-exponent to measure the decaying rate of the violation probability for the maximum allowed AoI and characterize the relationship between the key parameter QoS-exponent in statistical delay-bounded QoS provisioning theory and the AoI-exponent in AoI theory. Second, we also derive a closed form expression of the AoI-based effective capacity, as a function of both the QoS-exponent and the AoI-exponent, to measure the optimal constant information updating rate for a given service process to support the statistical-bounded AoI guarantee. Third, we jointly optimize the statistical delay-bounded QoS and statistical upper-bounded AoI schemes. Finally, we validate and evaluate AoI based statistical delay-bounded QoS provisioning schemes over multimedia mobile wireless networks through numerical analyses.

Data center networks are expected to enable massive connectivity for a wide range of applications. The traditional way of providing security services for data center is difficult to support these applications flexibly and effectively, because supporting security services will inevitably result in the additional networking operations and complexities, imposing the extra network end-to-end delay and latency. Consequently, the emergent-secure applications in data center networks have extremely stringent delay-bounded quality of service (QoS) requirements for data processing. To support the latency critical service, current secure data center systems employ cache partitioning techniques to share the last level cache (LLC) and to guarantee the low-tail-latency QoS. However, the conventional way-partitioning scheme can only provide a constrained number of partitions, which cannot satisfy the latency critical applications' requirement. To overcome this difficulty, we propose the HiCa scheme, a hierarchical cache partitioning technique, to significantly mitigate the tail-latency and improve performances of commodity multicore with waypartitioned caches without operating system changes. Our proposed HiCa scheme groups applications into clusters according to their cache behavior compatibility, and then, partitions the entire cache into several layers and assigns each application cluster into one layer. We also derive the ratio of the number of instructions per cycle (IPC) of using our proposed HiCa scheme to evenly partitioning the cache, in the scenarios of all instructions being cached with the same probability and instructions being cached according to accessing probability. The performance evaluation results show that our proposed HiCa scheme can outperform the conventional cache partitioning technique and can efficiently mitigate the tail-latency in multicore.

A device-to-device (D2D) wireless ad hoc network architecture enables dynamic self-organizing communications among mobile users who can directly exchange information with their peers without a pre-determined network infrastructure. Moreover, finite blocklength coding (FBC) is the promising candidate technique to support time sensitive multimedia wireless networks services, where mobile users transmit short packets to upper-bound the transmission delay of video/audio traffic. The scaling law technique models the maximum D2D channel capacity as a function of the density of mobile users. Recent studies have integrated D2D wireless ad hoc networks with FBC theory to further improve the performance of 5G wireless ad hoc networks. However, how to model and analyze the capacity of D2D wireless ad hoc networks under the finite blocklength regime is not well understood and has not been thoroughly studied. To overcome these challenges, applying the scaling law technique, we derive upper-bounds on the coding rate of each D2D channel and the number of time slots needed to complete all D2D transmissions. Combining the D2D channel's coding rate with the number of time slots needed for all D2D transmissions, we derive the maximum aggregate throughput for wireless ad hoc networks with all mobile users using D2D communications while mitigating interference. We also develop a model where each D2D channel follows the Nakagami-m distribution, under which we derive the average aggregate throughput and its upper-bound. Finally, we evaluate our derived results in the D2D wireless ad hoc networks over finite blocklength regime through numerical analyses.

Wireless cooperative communications and relay networks can significantly improve the performance of fifth generation (5G) wireless networks, which aim at providing a wide range of time-sensitive multimedia services and applications by satisfying users' stringent requirements on the delay-bounded quality of service (QoS). Finite blocklength coding (FBC) is a promising candidate technique to support time-sensitive services in 5G wireless networks, where mobile users transmit short packets to upperbound the transmission delay of multimedia traffic. In this paper, we derive closed-form expressions for the coding rates of direct and relay transmissions, respectively, under the Nakagami-m fading channel in the finite blocklength regime. We compare decoding error probabilities, coding rates, and outage probabilities between direct and relay transmissions. Our comparison results show that while relay transmission yields a larger coding rate than that of direct transmissions under the Nakagami-m fading channel, the outage probability and error rate of relay transmissions are higher than those of direct transmissions. We also derive a closedform expression for the outage probability for relay transmissions with multiple parallel relays. Finally, we evaluate and validate our derived coding rates and outage probabilities for both direct and relay transmissions in the finite blocklength regime through numerical analyses.

Caching in mobile devices is a new paradigm to revolutionize the traditional data contents (i.e., files) sharing methods in wireless cellular networks. By caching the popular data contents in mobile devices and disseminating these data contents through device-to-device (D2D) communications, the wireless cellular network can improve its area spectral efficiency, save the base station bandwidth consumptions, and reduce the transmission delay of downloading. The caching scheme is closely related to the data popularity prediction, but how to accurately estimate the future popularity profile has not been well understood. Moreover, although the hierarchical caching architecture has been shown to yield more benefits than flat caching strategies, the challenge of designing an optimal hierarchical caching scheme has not been thoroughly addressed. In this paper, we use Neyman- Pearson hypothesis testing mechanism to predict the future data popularity, and also propose and optimal hierarchical caching schemes over D2D wireless ad-hoc networks. The key of Neyman- Pearson hypothesis testing in our proposed scheme is to derive the closed form of the decision threshold, which in a function of costs if choosing the incorrect hypotheses. We formulate these costs as the sum of time durations for content placement phase and content delivery phase. We derive the closed form expressions of these two phases respectively and obtain the closed form of optimal decision rule, which maximizes the cache hitting probability and upperbounds the prediction error probability. Finally, we evaluate and validate our proposed Neyman-Pearson hypothesis testing based hierarchical caching schemes through numerical analyses.

The fifth generation (5G) wireless networks aim at providing a wide range of time-sensitive multimedia services and applications to satisfy users’ stringent requirements on delay-bounded quality of service (QoS). One of the important requirements for 5G multimedia wireless networks is to efficiently support the ultra-reliable and low- latency communications (URLLC). Finite blocklength coding (FBC) is a promising candidate technique to support URLLC services, where mobile users transmit short packets to upper-bound the transmission delay of multimedia traffic. In this paper, we model and analyze the packet-block error rate for URLLC under the Nakagamim fading channel in the finite blocklength regime. Then, we derive the minimum energy per bit for data transmissions in the Nakagami-m fading channel under the constraint of block error rate. To derive closed-form expressions for the minimum energy per bit, we obtain achievability and converse bounds on the minimum energy per bit, and show that these two bounds are equal to each other. Finally, we evaluate our derived minimum energy per bit in the finite blocklength regime through numerical analyses, and compare it with those under the Rayleigh fading channel and the wideband approximation, validating our derived analytical results.

The fifth generation (5G) wireless networks are expected to provide a wide range of time-sensitive multimedia services and applications to satisfy users' stringent requirements on delay-bounded quality of service (QoS). Finite blocklength coding theory can efficiently address the issue of delay-bounded QoS constraint guarantees, where mobile users send messages using packets with small numbers of bits to achieve low latency transmissions. In this paper, we employ the Nakagami-m fading model to analyze channel coding performance in the finite blocklength regime, in terms of average block error rate, capacity outage probability, and symbol error probability. We first derive closed-form expressions for upper and lower bounds on the average block error rate. Then, we compare these bounds with the capacity outage probability and show that the average block error rate is larger than the capacity outage probability. We also obtain a lower-bound in closed-form for the symbol error probability under M-ary phase shift keying (MPSK). Finally, we validate and evaluate our derived average block error rate bound, capacity outage probability, and symbol error probability in the finite blocklength regime through numerical analyses.

The device-to-device (D2D) communication is an efficient mobile-data offloading technique to significantly improve the cellular base station (BS) spectrum efficiency in the fifth generation (5G) wireless networks by enabling mobile users to directly receive the multimedia data from a nearby mobile users through D2D communication. On the other hand, D2D multimedia-data offloading imposes the new modeling challenges in characterizing the statistical delay-bounded quality of service (QoS) provisioning over two-hop tandem wireless links when a mobile user receives its requested data from the cellular BS and relays it to the next mobile user which is requesting the same data via D2D communications. Thus, one of the most important challenges of the D2D multimedia-data offloading architecture lies in how to satisfy the statistical delay-bounded QoS requirements over the two-hop wireless links which consists of two tandem single-hop links. To tackle these challenges, we propose the overall two-hop wireless link QoS provisioning schemes based on its two individual single-hop statistical QoS requirements. We derive the overall effective capacity's expression of the two-hop tandem wireless links as a function of the single-hop's effective capacities and prove that the overall equivalent effective capacity of the two-hop wireless link is upper bounded by the single-hop effective capacity with the smaller QoS exponent. Moreover, we derive the upper-bound and lower-bound of the equivalent QoS exponent for the overall two-hop wireless links and also show its monotonicity with respect to single-hop QoS exponents. To show the network performance's improvement after integrating the cellular networks and D2D offloading, we compare the scalability of the BS-based cellular wireless networks with and without D2D offloading. We also evaluate and validate our proposed two-hop wireless link statistical delay bounded QoS provisioning schemes over multimedia D2D offloading architecture through numerical analyses.

The multimedia transmission represents a typical big data application in the fifth-generation (5G) wireless networks. However, supporting multimedia big data transmission over 5G wireless networks imposes many new and open challenges because multimedia big data services are both time-sensitive and bandwidth-intensive over time-varying wireless channels with constrained wireless resources. To overcome these difficulties, in this paper we propose the information-centric virtualization architectures for software-defined statistical delay-bounded quality of service (QoS) provisioning over 5G multimedia big data wireless networks. In particular, our proposed schemes integrate the three 5G-promising candidate techniques to guarantee the statistical delay-bounded QoS for multimedia big data transmissions: 1) information-centric network (ICN), to derive the optimal in-network caching locations for multimedia big data; 2) network functions virtualization (NFV), to abstract the PHY-layer infrastructures into several virtualized networks to derive the optimal multimedia data contents delivery paths; and 3) software-defined networks (SDNs), to dynamically reconfigure wireless resources allocation architectures through the SDN-control plane. Under our proposed architectures, to jointly optimize the implementations of NFV and SDN techniques under ICN architectures, we develop the three virtual network selection and transmit-power allocation schemes to: 1) maximize single user's effective capacity; 2) jointly optimize the aggregate effective capacity and allocation fairness over all users; and 3) coordinate non-cooperative gaming among all users, respectively. By simulations and numerical analyses, we show that our proposed architectures and schemes significantly outperform the other existing schemes in supporting the statistical delay-bounded QoS provisioning over the 5G multimedia big data wireless networks.

Mobile cloud-computing is a wireless network environment that focuses on sharing the publicly available wireless resources. Wireless network virtualization provides an efficient technique to implement the mobile cloud-computing by enabling multiple virtual wireless networks to be mapped onto one physical substrate wireless network. One of the most important challenges of this technique lies in how to efficiently allocate the wireless resources of physical wireless networks to the multiple virtual wireless network users. To overcome these difficulties, in this paper we propose a set of novel game-theory based schemes to resolve the wireless resources allocation problem in terms of transmit power and wireless spectrum. We formulate this wireless resources allocation problem as the gaming process where each mobile user bids for the limited wireless resources from physical substrate wireless networks, and competes with the other mobile-user players bidding for the same resources. Under our proposed game-theory framework, we develop three types of wireless resources request strategies: price-based strategy, correlation-based strategy, and water-filling-based strategy to allocate wireless resources under three different gaming mechanisms. The extensive simulation results obtained validate and evaluate our proposed schemes.

Edge computing techniques have been developed to support the exponentially increasing service demands in the fifth generation (5G) networks by bringing the data contents and their corresponding computations/communications to the edge of the wireless networks, which is the area near to mobile users. As one of the promising and enabling techniques in edge computing wireless networks, in- network caching stores the data contents close to mobile users to efficiently reduce the transmission delay for time-sensitive multimedia data contents. However, one of the main challenges for implementing in-network caching techniques lies in how to develop the collaborative caching mechanisms among all caches in the edge of wireless networks to upper-bound the data transmission delay while maximizing the cache hitting rate. In this paper, we propose the inter-tier and intra-tier collaborative hierarchical caching mechanisms over 5G edge computing multimedia mobile wireless networks, where the popular multimedia data contents are selectively cached at different wireless network caching tiers (e.g., at routers, cellular- base stations/WiFi-access-point, and mobile devices, respectively). The inter-tier collaborative hierarchical caching minimizes the average number of hops (including wireless hops and wireline hops) through the collaborative caching across three wireless network caching tiers, and the intra-tier collaborative hierarchical caching maximizes the overall cache hitting rate within the same wireless network caching tier. To optimize the intra-tier collaborative hierarchical caching mechanism, we derive the upper-bound and lower- bound of maximum numbers of device-to-device (D2D) pairs at the bottom tier of wireless network caching. Finally, we use numerical analyses to evaluate and validate our proposed collaborative hierarchical caching mechanisms over edge computing mobile wireless networks.

As a crucial step moving towards the next generation of super-fast wireless networks, recently the fifth-generation (5G) mobile wireless networks have received a plethora of research attention and efforts from both the academia and industry. The 5G mobile wireless networks are expected to provision distinct delay-bounded quality of service (QoS) guarantees for a wide range of multimedia services, applications, and users with extremely diverse requirements. However, how to efficiently support multimedia services over 5G wireless networks has imposed many new challenging issues not encountered before in the fourth-generation wireless networks. To overcome these new challenges, we propose a novel network-function virtualization and mobile-traffic offloading based software-defined network (SDN) architecture for heterogeneous statistical QoS provisioning over 5G multimedia mobile wireless networks. Specifically, we develop the novel SDN architecture to scalably virtualize wireless resources and physical infrastructures, based on user's locations and requests, into three types of virtual wireless networks: virtual networks without offloading, virtual networks with WiFi offloading, and virtual networks with device-to-device offloading. We derive the optimal transmit power allocation schemes to maximize the aggregate effective capacity, overall spectrum efficiency, and other related performances for these three types of virtual wireless networks. We also derive the scalability improvements of our proposed three integrated virtual networks. Finally, we validate and evaluate our developed schemes through numerical analyses, showing significant performance improvements as compared with other existing schemes.

Peer-to-peer (P2P) cache techniques for information-centric networks (ICN) provide the local data resources services for mobile users by storing the popular data contents in their peer mobile users' cache stations, so that a mobile user can obtain these data contents directly from its peer mobile users instead of the remote service provider. Retrieving the requested data content from the nearby storage locations (i.e., cache stations), the P2P caching technique improves the network performance by reducing the duplicate data transmission traffic. To overcome the difficulties of minimizing the data transmission delay for the P2P caching system, we divide all mobile users in a wireless cell as several P2P caching groups, where a mobile user can obtain its requested data content from a peer mobile user in the same group, or from a peer mobile user in another group. We propose the Markov decision process (MDP) based stochastic optimization scheme in this cache system with dynamic number of peers to derive the optimal lifespan for cached data contents in cache stations. The objective of our proposed stochastic optimization framework is to efficiently minimize the cost function, which jointly measures the cache size/cost and the data content transmission delay, over all P2P caching groups in the wireless cell. To achieve this goal, we apply the finite-state Markov chain to characterize the number of mobile users in each caching group and dynamically adapt the cached data lifespan in cache stations. We also develop an efficient iteration algorithm to implement our proposed MDP scheme. Finally, we validate and evaluate the performance of our proposed schemes through the numerical analyses, which show that our proposed schemes converge to a unit optimal solution.

Cooperative communication is an efficient technique for mobile wireless networks supporting information transmission through relay nodes, which forward the signal received from a source node to its destination. Applying the cooperative communication in wireless networks can significantly improve the network performance. One of the most important challenges is how to efficiently allocate the access opportunity of a relay node to multiple mobile-users while taking into account the delay-bounded quality of service (QoS) for the time-sensitive multimedia data transmissions. To overcome these difficulties, in this paper we propose a novel game-theory based scheme to resolve the relay node allocation problem by measuring the maximum throughput for time-sensitive multimedia data transmission under a QoS requirement as the effective capacity gain. We formulate this one relay allocation problem as a single-relay-auction such that all mobile-user players can decide whether to patriciate in this relay auction, and the mobile-user player who places the highest bid obtains the relay allocation. The mobile-user players who lose or stay out of the auction can only directly transmit information to their destinations. Then, we derive the optimal strategies for the single-relay-auction, maximizing the payoff (effective capacity gain minus costs) in each bid round.The extensive simulation results obtained validate and evaluate our proposed schemes.

Information-centric network technique is developed to reduce duplicate transmissions for popular data contents by adopting caching strategy and enabling mobile users to access popular data contents from cache stations of nearby network gateways. In current information-centric wireless networks, one popular data content can be stored in multiple cache stations and requested by multiple mobile users. One of the main challenges of this multiple-cache-and-multiple- user wireless network lies in how the mobile user obtains a data content from one of these multiple cache stations with the minimum cost while guaranteeing the data transmission quality-of-service (QoS), and which mobile-user should a cache station choose to provide the data content. To overcome these problems, we formulate a many-to-many negotiation game, where each player (a cache station or a mobile user) bargains with all of its negotiation partners (all mobile users and all cache stations, respectively) in terms of desirable prices, aiming at maximizing its individual payoff. In our proposed negotiation game scheme, the payoff for a mobile user is the obtained QoS gain minus corresponding costs, and the payoff for a cache station is the obtained payment from its negotiation partner mobile user minus the cost for providing services. We propose an adaptive concession strategy, such that players can adjust the concession speed and concession amount of their desirable prices according to their current bargain situations in each game round. Then, we mathematically prove that our proposed adaptive concession strategy maximizes players' payoffs and the probability to achieve a negotiation agreement. Finally, we validate and evaluate the performances of our proposed negotiation game scheme and adaptive concession strategy through the numerical analyses.

Big data transmission on a wireless network environment focuses on sharing the large volume information data with the effective capacity guaranteed. Wireless network virtualization provides an efficient technique to implement the big data transmission by enabling multiple virtual wireless networks (VWNs) to be mapped onto one physical substrate wireless network (SWN), downloading the information data from SWN. One of the most important challenges of this technique lies in how to efficiently allocate the wireless resources of physical wireless networks to the multiple virtual wireless network users, considering the quality of service for the data transmission. To overcome these difficulties, in this paper we propose a novel auction based scheme to resolve the wireless resources allocation problem in terms of transmit power and wireless spectrum. We formulate this wireless resources allocation problem as an auction process where each mobile user bids for the limited wireless resources from physical substrate wireless networks, and competes with the other mobile-user players bidding for the same resources. First, the mobile users derive their bids for transmit powers, which indicate their willingness to pay for the transmit powers, aiming at maximizing their payoffs (performance-gain minus cost) on each subchannel. Then, according to the derived available transmit power on each subchannel, mobile users derive their bids for the number of subchannels, aiming at maximizing their overall payoffs. The SWN assigns these two types of wireless resources to each mobile user according to the bids for all virtual users. Finally, as the mobile users' bidding processes proceed iteratively, our proposed games are guaranteed to converge to the Nash Equilibrium, where the benefits of SWN and mobile users are both optimized, thus maximizing the aggregate effective capacities for our resources-virtualized big data transmission wireless networks. The extensive simulation results obtained validate and evaluate our proposed schemes.

Wireless network virtualization provides an efficient technique to reduce the cost of overall networks by allowing multiple wireless virtual networks to be mapped onto one physical substrate wireless network. One of its important challenges of this technology is how to efficiently allocate the wireless resources of physical wireless networks to multiple virtual network users. To overcome these challenges, we develop the game- theory based schemes to resolve the resources allocation problem on both buffer/storage space and transmission-rate. Mapping the virtual networks onto the physical wireless network is formulated as the process of bidding for the limited wireless resources from a physical substrate wireless network, and competing with the other mobile-user players, which bid for the same wireless resources of a physical substrate wireless network. Our proposed game- theory schemes work as follows. First, virtual network announces its value function to the physical substrate wireless network, to claim for their required resources (buffer/storage space and transmission bandwidth). Then, the physical substrate wireless network allocates the two types of wireless resources to each virtual wireless network and charges for the provided resources according to the claimed value functions for all virtual users and its current network conditions. Finally our proposed schemes feed back conjectural prices for virtual networks' next-time bid. By accepting the conjectural prices, the game of virtual networks converges the Nash Equilibrium where their long term payoffs are optimized. The simulation results obtained validate and evaluate our proposed schemes through numerical solutions and the simulations.