The fluid antenna system (FAS) is a reconfigurable antenna technology that enhances wireless communications by adapting to time-varying channel conditions. However, existing FAS research focuses on physical-layer performance, neglecting link-layer quality-of-service (QoS) guarantees like latency/delay. This paper develops a cross-layer model to investigate the low-latency performance of a slow fluid antenna multiple access (s-FAMA) system in a multi-user downlink scenario, employing the effective capacity (EC) framework. We first derive approximate expressions for the total EC in a multi-users-FAMA system under both complex and simplified channel models. To validate them, we propose a quasi-Monte Carlo (QMC) method to compute multidimensional integrals, overcoming the limitations of conventional numerical methods and solving a class of multidimensional integral numerical simulation problems for large-scale FAMA systems. Then, we formulate an optimization problem to maximize the total EC while satisfying the total power constraint and each user’s minimum EC requirement. To jointly optimize port selection and power allocation, an iterative algorithm based on alternating optimization (AO) and quadratic transform (QT) is proposed to solve the non-convex problem. Simulation results validate our approximations and show that our joint port selection and power allocation scheme outperforms the conventional baseline algorithms, confirming our proposed algorithm’s effectiveness and FAS’s superiority in ensuring the QoS in wireless communications.

Simultaneous wireless information and power transfer (SWIPT) has been regarded as a highly promising technology for delivering reliable energy supply for low-powered Internet-of-Thing devices (IoTDs). In this paper, we originally investigate the performance of SWIPT systems in multi-Reconfigurable Intelligent Surface (RIS) aided cell-free IoT networks with fluid antenna (FA).Our objective is to simultaneously maximize the sum transmission rate and harvested energy by optimizing the selection of FA ports, transmit beamforming vectors at access points (APs), and reflect beamforming at RISs, which is a notorious trade-off and is impossible to simultaneously maximize at the same IoTD under the traditional Power splitting (PS) or Time switching (TS) based SWIPT techniques. By converting the harvested energy into throughput, we formulate a novel equivalent-sum-rate (ESR) maximization problem. To handle this sum-of-logarithmic function and nested multiple-ratio fractional nonconvex optimization problem, we develop a Lagrangian dual transform (LDT) and quadratic transform (QT)-based alternating optimization (AO) framework to tackle it. Simulation results demonstrate that the proposed method outperforms benchmarks, highlighting the enhanced system performance in both information and energy reception facilitated by RISs and FA. Moreover, our research also indicates that the deployment of FA at IoTDs can achieve better performance than the traditional fixed-position antennas in such a network, as long as it can provide an adequate number of ports and FA size.

Fluid antenna system (FAS) is regarded as one of the key enabling technologies for supporting massive communications in next-generation networks, owing to its advantages of compact design, low power consumption, and significant performance gains. This paper investigates a FAS-assisted downlink non-orthogonal multiple access (NOMA) satellite network, where terminal users are equipped with FAS to receive superimposed signals from the satellite. The satellite-to-FAS channels are modeled as spatially correlated shadowed-Rician fading distributions, where the spatial correlation matrix is obtained from the von Mises–Fisher model. By utilizing block-correlated approximation method, the cumulative distribution function of the maximum spatially correlated shadowed-Rician fading channel gain is derived. On this basis, closed-form expressions for outage probability and ergodic data rate of the FAS-NOMA satellite network are derived under both imperfect and perfect successive interference cancellation scenarios. Moreover, asymptotic expressions for outage probability and ergodic data rate of the FAS-NOMA satellite network are derived in the high signal-to-noise ratio region to unveil the achievable diversity gain and multiplexing gain. Simulation results reveal that: 1) Compared to conventional antenna system with selection combining scheme, FAS can effectively exploit the fluctuations of the channel response and provide additional selection diversity, thereby achieving superior performance in NOMA satellite networks; and 2) By adjusting the power allocation factor under different antenna apertures, the performance of FAS-NOMA satellite networks can be further enhanced.

The fluid antenna system (FAS) enhances wireless communication through dynamic adaptation to channel conditions. However, most existing studies focus on physical-layer metrics without considering link-layer low-latency quality-of-service (QoS) guarantees. This paper develops a cross-layer model to analyze the QoS performance in a slow fluid antenna multiple access ($s$-FAMA) system using the effective capacity (EC) framework. We formulate and solve a joint port selection and power allocation problem to maximize total EC under total power and each user's minimum EC constraint. Simulation results demonstrate that our proposed algorithm significantly outperforms conventional uniform power allocation, highlighting the effectiveness of FAS in ensuring QoS.

Virtual Reality (VR) applications have attracted great attention from both industry and academia in recent years. With an increasing demand for these applications, there is high pressure on resource and performance optimization using software-defined networking. In this paper, we consider one type of QoE models in the literature; and then use the Monte Carlo Tree Search and develop an SDN-based routing scheme to guarantee users' QoE for VR applications. Our routing scheme is tested using computer simulation in a$5\mathrm{x}5$square grid network. Simulation results indicate that our routing scheme consistently outperforms the shortest path first (SPF) algorithm in terms of the QoE scores.

Incorporating software-defined networking (SDN) technology into Internet of Things (IoT) can improve network utilization. In this paper, we consider SDN-IoT networks, in which IoT devices use Zigbee protocol. A machine-learning- assisted minimum end-to-end delay (MaMED) routing scheme for IoT monitoring services is proposed; and this scheme requires no prior knowledge of input traffic. Under different SDN- IoT simulation scenarios, results show that our routing scheme outperforms the shortest path first (SPF) algorithm in terms of end-to-end delay (E2E) performances (e.g., average E2E delay and jitter), and gives more consistent delay performance when the input IoT traffic increases.

Abstract Using reconfigurable fluid antennas, it is possible to have a software‐controlled position‐tuneable antenna to realize spatial diversity and multiplexing gains that are previously only possible using multiple antennas. Recent results illustrated that fast fluid antenna multiple access ( f ‐FAMA) which always tunes the antenna to the position for maximum signal‐to‐interference ratio (SIR) on a symbol‐by‐symbol basis, could support hundreds of users on the same radio channel, all by a single fluid antenna at each user without complex coordination and optimization. The network outage rate, nevertheless, depends on the SIR threshold chosen for each user. Motivated by this, this paper adopts a first‐order approximation to obtain the outage probability expression from which a closed‐form solution is derived for optimizing the SIR threshold in maximizing the network outage rate. Moreover, a closed‐form expression is provided to estimate the number of users in the f ‐FAMA network in which the outage rate begins to plateau. Numerical results show that the proposed SIR threshold achieves near‐maximal outage rate performance.

Multiple access can be realized by utilizing the spatial moments of deep fades, using fluid antennas. The interference immunity for fluid antenna multiple access (FAMA), nevertheless, comes with the requirement of a large number of ports at each user. To alleviate this, we study the synergy between opportunistic scheduling and FAMA. A large pool of users permits selection of favourable users for FAMA and decreases the outage probability at each selected user. Our objective is to characterize the benefits of opportunistic scheduling in FAMA. In particular, we derive the multiplexing gain of the opportunistic FAMA network in closed form and upper bound the required number of users in the pool to achieve a given multiplexing gain. Also, we find a lower bound on the required outage probability at each user for achieving a given network multiplexing gain, from which the advantage of opportunistic scheduling is illustrated. In addition, we investigate the rate of increase of the multiplexing gain with respect to the number of users in the pool, and derive a tight approximation to the multiplexing gain, expressed in closed form. As a key result of our analysis, we obtain an operating condition on the product of the number of users in the pool and the number of ports at each fluid antenna that ensures a high multiplexing gain. Numerical results demonstrate clear benefits of opportunistic scheduling in FAMA networks, and corroborate our analytical results.

Open Shortest Path First (OSPF) is a widely used routing protocol, which uses cost to find the shortest path. In this paper, we develop an extension to OSPF to support QoS guarantees by replacing the cost metric with end-to-end (E2E) delay. Particulary, we propose a new stream-based Dijkstra's algorithm to find the minimum E2E delay path for any traffic stream and implement this algorithm in Quagga OSPF, a network routing software suite. Experimental results show that our proposed protocol reduces E2E delays compared with unmodified OSPF routing protocol.

Cell discontinuous transmission (Cell DTx) improves network performance because it help mitigate inter-cell interference. However, the relationship between this technology and network performance has been little studied. The aim of this work is to understand the impact of Cell DTx on users’ quality of service (QoS) performance of multiuser ultra-dense networks (UDNs). We extend the traditional one-dimensional effective capacity model and develop a new multidimensional framework for UDNs with Cell DTx, which can be applied to different scheduling policies. Under the round-robin and the max-C/I scheduling examples, computer simulation shows that the analytical and simulation results are in good agreement and thus validate the accuracy of our proposed framework.

5G networks require 100x energy efficiency enhancement. Much work on energy efficiency was done with a single-source assumption, however, information received at UEs usually comes from multiple sources. Therefore, their work for energy efficiency analysis shall be adjusted to the multiple sources scenario. In this paper, based on the effective capacity and the effective bandwidth models, we propose a new method to approximate systems' effective energy efficiency for multiple data sources. It is shown that the approximation results based on our method overlap with simulation results and are more accurate than existing methods.

The buffer-aided wireless relay technology is widely used in the 4G and the 5G wireless systems. This technology has advantages of throughput enhancement and coverage extension, but it will introduce additional delays because of buffering. Therefore, it is important to understand the end-to-end delay behavior when this technology is in use. In this paper, we consider a three-node relay system with block-fading channels and a random arrival process, and then derive a new upper bound of the two-hop end-to-end delay distribution function. Simulation results show that our bound provides the closest approximation of delay outage probability values.

Vehicular ad hoc networks, wireless sensor networks, Internet of Things, and mobile device-to-device communications can be modeled as different homogeneous fog networks, wherein similar terminals/things/devices/nodes are sharing their computation, communication, and storage resources in the neighborhood for achieving better system performance through effective collaborations. It is very desirable, but quite challenging, to simultaneously reduce service delay and energy consumption in such networks for delay-sensitive and energyconstraint applications, e.g., virtual reality and online 3-D gaming on mobile devices. In this paper, a cross-layer analytical framework is developed to formulate and study the balance between service delay and energy consumption. An effective control parameter V is derived to characterize their tradeoff relationship during dynamic task scheduling processes in fog networks. Combining this analysis with Lyapunov optimization techniques, a novel delay energy balanced tasking scheduling (DEBTS) algorithm is proposed to minimize the overall energy consumption while reducing average service delay and delay jitter. It is proved that DEBTS can achieve the theoretical [O(1/V), O(V)] tradeoff between these two performance metrics. Further, extensive simulation results show that DEBTS can offer much better delay-energy performance in task scheduling challenges. Specifically, for a typical V value of 4 × 104, DEBTS can save 26% and 29% more energy, and at the same time, reduce average service delay by 29% and 32%, than traditional random scheduling and least busy scheduling algorithms, respectively.

An accurate energy efficiency analytical model based on a two-mode circuitry was recently proposed; and the model showed that the use of this circuitry can significantly improve a system's energy efficiency. In this paper, we use this analytical model to develop a new power control scheme, a scheme that is capable of allocating a minimum transmission power precisely within the delay-outage probability constraint. Precision brings substantial benefits as numerical results show that the energy efficiency using our scheme is much higher than other schemes. Results further suggest that data rate values affect energy efficiency non-uniformly, i.e., there exists a specific data rate value that achieves maximum energy efficiency.

Capacity-coverage tradeoff balancing is a classical but critical problem for practical wireless multi-cell network optimization. Increasing the average capacity is often at the expense of decreasing the cell coverage and vice versa. It becomes more complex in massive multiple-input/multiple-output (MIMO) networks to balance the above two performance indicators since capacity is much highly improved while the inter-cell interference is also increased greatly under massive antenna scenarios. In this work, a novel system-level optimization parameter is proposed, namely minimum user signal to interference plus noise ratio (user-SINR) threshold, to solve the above balancing problem. By utilizing this new parameter, the corresponding user scheduling and inter-cell interference coordination scheme are further provided for cooperative massive MIMO networks. Performance analysis and numerical results show that the proposed minimum user-SINR threshold is a very effective optimization parameter to achieve the capacity-coverage tradeoff performance with lower optimization complexity, combined with the proposed scheduling and interference coordination schemes.

Device to Device (D2D) communication has been proved to be an effective way to enhance cellular network capacity, which enables direct data exchange of localized traffic of users in proximity. Current D2D links are mainly based on WiFi Direct technology. In this paper, we propose a 4G LTE-assisted distributed D2D communication network. Information of user devices will be uploaded to a D2D server periodically via commercial 4G LTE network. The D2D server then will initialize a D2D network after collecting the device information. A Token sharing strategy is proposed to control the process of D2D networking, based on the information of SINR, location, battery power, as well as service QoS demand. Finally, our proposed system is demonstrated by Android smartphones.

The complementary cumulative distribution function (CCDF) of delay in a wireless communication system can be approximated by the effective capacity model. However, it is still unknown if such a distribution function can be approximated analytically and accurately. In this paper, we consider a system model with a Nakagami-m fading channel and an exponential arrival process, and derive a simple approximation formula for the CCDF of delay. Simulation results show that our formula is accurate in approximating the CCDF of delay in the system model considered above.

End-to-end delay is an important consideration of the design of wireless tele-ultrasonography systems because such systems support delay-sensitive applications and require critical delay constraints. This paper addresses the characterisation and estimation of end-to-end delay distributions by using the Effective Capacity (EC) technique. A cross-layer simulation platform is built to 1) represent a scenario of remote medical ultrasound video streaming over fixed WiMAX networks, 2) transmit a real ultrasound medical video and 3) verify the accuracy of the EC-based delay distribution estimation for video frames. The results show that in most cases, the simulation results and estimation results are in good agreement.

It is suggested that packet-based wireless digital communication systems should be represented by discrete-time queueing models. On the basis of the Effective Capacity model, we characterise the probability distribution of a delay process in a digital system by two parameters, namely, the probability of non-zero delay and the success probability of a connection. An estimator that estimates these two parameters is mathematically derived. We consider non-ARQ traffic over the IEEE 802.16-2004 WiMAX systems as an example and establish a cross-layer simulation platform for such an example. The estimator is tested through simulation and results show that the empirical results from the simulation platform are close to estimation results.

Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types.

Wireless multi-hop architectures are increasingly used in many wireless networks. However, it is very difficult to analyse and guarantee end-to-end delay performance over multi-hop wireless paths. In this paper, we develop a cross-layer analytical model to characterise the multi-hop delay performance and derive new mathematical formulae of (DBVP), delay mean and jitter of end-to-end communications. The analytical model is verified by computer simulations, and the results show that the mathematical formulae and simulations are in good agreement.