Multi-link operation (MLO) is a key feature introduced in the IEEE 802.11be (Wi-Fi 7) standard to support the quality of service (QoS) demands of emerging Internet of Things (IoT) wireless applications. To fully exploit the advantage of MLO, assigning traffic identifications (TIDs) to an appropriate link is essential. In this paper, a novel TID-to-link mapping (TTLM) scheme named federated learning-empowered correlation estimation transformer-based TTLM (FLCET3LM) is proposed to achieve high throughput and low latency while reducing the energy consumption in IEEE 802.11bn (Wi-Fi 8) networks. FLCET3LM is based on a novel federated learning empowered correlation aware framework that can output the link concordance correlation coefficient (CCC) between stations (STAs) based on the average received signal strength indicator (RSSI) of each STA. In addition, FLCET3LM includes a TTLM optimization framework that minimizes the weighted CCC sum while maintaining a link utilization to a certain extent. The performance of FLCET3LM was evaluated based on Python simulation, where the results show that FLCET3LM can provide a significant performance gain in terms of throughput, latency, frame error rate, and energy consumption.

6G non-terrestrial networks (NTNs) will use satellites for global internet access, where low earth orbit (LEO) satellites will be used frequently due to the lower latency and higher link budget that can be provided compared to higher altitude satellites. However, LEO satellites require many satellites due to their fast movement and limited coverage, leading to frequent handovers (HOs). This paper presents an intelligent LEO satellite conditional handover (ILCHO) scheme that improves the NTN performance by addressing the frequent HO issues while reducing the signal strength variation between cells and outdated measurement values. The ILCHO scheme derives the LEO satellite's state information (position and orbital path), which is used to obtain the conditions for a LEO satellite to be accessible to enable optimized target LEO satellite selection that will maximize the overall network service performance while minimizing the number of HOs. The ILCHO scheme uses multi-agent reinforcement learning (MARL) to maintain an optimized performance through early preparation by incorporating a distance-based event in the execution phase. The simulation results show that the proposed ILCHO scheme can increase the throughput and signal to noise ratio (SNR) performance compared to other HO algorithms, thereby providng improved and more stable communication conditions.

As wireless local area network (WLAN) devices become more prevalent in various environments, the IEEE 802.11ax standard (Wi-Fi 6), released in 2021, aims to improve the spectral efficiency. The overlapping basic service set (OBSS) packet detection (PD)-based spatial reuse (SR) scheme is a main technology for this purpose which can increase channel access opportunities by adjusting parameters. However, the standard only provides boundary values for parameter adjustment and does not provide details on how to optimize the performance, which can lead to a performance degradation. The IEEE 802.11be standard (Wi-Fi 7), established to satisfy extremely high-throughput (EHT) demands of next-generation applications, includes the OBSS PD-based SR scheme and the coordinated SR (CSR) scheme to maximize area throughput in dense networks. The CSR scheme optimizes the transmission performance through coordination between access points (APs), which results in additional signaling overhead and protocol complexity. In this article, an enhanced SR scheme named optimized transmission power-based OBSS PD (OTOP) SR is proposed to achieve high throughput in dense networks. The OTOP scheme enhances the parameter control operation of OBSS PD-based SR to take advantage of its distributed nature which keeps the signaling overhead and protocol complexity low. The OTOP scheme derives the optimal transmission power that can maximize the transmission success probability of WLAN devices based on stochastic geometry analysis. The simulation results show that the proposed OTOP scheme performs better than the existing SR gain maximizing and OBSS PD-based SR schemes in terms of throughput, frame error rate, and fairness.

5G networks were designed to provide sufficient throughput and reliable data communication services. To achieve the targeted QoS requirements of the 5G specifications, the use of mmWaves are essential. However, mmWavs are very vulnerable to signal blockages due their high frequency. Multi-path transmission control protocol (MPTCP) can deal with this problem by transmitting data through multiple subflows using TCP. The current congestion control scheme of MPTCP was designed for wired networks and is not suitable for use in wireless networks. In this paper, a new MPTCP congestion control algorithm named round trip time (RTT) estimation based Veno (REVeno) is proposed to better support wireless networks by distinguishing between loss due to congestion and wireless channel errors. The proposed REVeno scheme uses a novel backlog estimation formula that considers the total buffer size and parameters that distinguish loss due to congestion and loss due to the wireless channel. The REVeno scheme will use these estimations to minimizes the reordering delay by equalizing the equilibrium RTT of all subflows. The simulation results show that the proposed REVeno scheme performs better than the existing schemes in terms of goodput and latency without adversely affecting other concurrent TCP connections in the same network.

Due to enhancements in Internet of Things (IoT) technology, users can now control numerous IoT devices that are integrated for a system (e.g., smart building management system, smart factory, etc.) through mobile user equipment (UE). As the number of controllable IoT devices increases, the amount of data that needs to be processed has increased along with the energy consumption. Since many IoT devices and most UEs are battery operated, minimizing the energy consumption is very important. One solution is to have a multiaccess edge computing (MEC) system conduct the computation instead of the IoT devices and UEs to help save their energy. In this article, an energy-optimized offloading approach that uses MEC computing support to process cooperative tasks is investigated. An optimization problem model is developed to minimize the energy consumption of IoT devices and UEs that have service delay limits. The numerical results demonstrate that the proposed IoT and UE popularity-based energy optimization (IPEO) scheme provides a better performance compared to the conventional MEC offloading methods.

In this article, a deep reinforcement learning (DRL) control scheme is proposed to satisfy the strict Quality-of-Service (QoS) requirements of ultrareliability low-latency communication (URLLC) and enhanced mobile broadband (eMBB) using 5G multiple radio access technology (RAT)-based partial offloading and multiaccess edge-computing (MEC) resource allocation. In the proposed scheme, the user equipment (UE) makes optimal offloading decisions while the MEC server dynamically adjusts the server resources based on offloading requests from multiple UEs using DRL technology. The aim of the proposed scheme is to minimize the energy consumption of the UEs while maximizing the system utility (SU) performance, which is composed of the spectral efficiency (SE) and offloading success rate (OSR) of the MEC server. In addition, multiagent distributed learning technology and best experience push (BEP) techniques are used to enhance the learning efficiency of the DRL framework. The simulation result shows that the proposed scheme provides an improved SU and energy consumption performance compared to the benchmark offloading schemes.

Due to the rapidly growing number of autonomous vehicles, establishing an intelligent transportation system (ITS) supported by the Internet-of-Vehicles (IoV) technology is important. The need for IoV technology is emphasized even more because recent accidents relating to autonomous vehicles show that there are limitations in the reliability of autonomous vehicles only using vehicle onboard sensors. In addition, a highly reliable support system for many vehicular applications is in strong demand, which is why there is a need for advanced vehicle-to-everything (V2X) technology. The two major types of V2X technology are dedicated short-range communication (DSRC) and cellular-V2X (C-V2X). Many vehicles are already equipped with DSRC; however, the use of C-V2X is highly supported by many mobile communication companies and the 5GAA, which is why C-V2X is expected to be very widely used. In this article, a hybrid V2X (H-V2X) scheme is proposed, which adaptively controls C-V2X to satisfy the key-performance indicator (KPI) requirements as much as possible, and uses DSRC transmission when the C-V2X required performance cannot be reliably supported. The performance analysis graphs show that the gain in performance of the proposed H-V2X scheme can provide an improved performance compared to other V2X schemes, which makes it more suitable to support reliable autonomous vehicles.

High levels of scalability and reliability are needed to support the massive Internet-of-Things (IoT) services. In particular, blockchains can be effectively used to safely manage data from large-scale IoT networks. However, current blockchain systems have low transactions per second (TPS) rates and scalability limitations that make them unsuitable. To solve the above issues, this article proposes a deep Q network shard-based blockchain (DQNSB) scheme that dynamically finds the optimal throughput configuration. In this article, a novel analysis of sharded blockchain latency and security-level characterization is provided. Using the analysis equations, the DQNSB scheme estimates the level of maliciousness and adapts the blockchain parameters to enhance the security level considering the amount of malicious attacks on the consensus process. To achieve this purpose, deep reinforcement learning (DRL) agents are trained to find the optimal system parameters in response to the network status, and adaptively optimizes the system throughput and security level. The simulation results show that the proposed DQNSB scheme provides a much higher TPS than the existing DRL-enabled blockchain technology while maintaining a high security level.

In this paper, a trajectory optimization algorithm for multiple unmanned aerial vehicles (UAVs) is investigated for free space optic (FSO) based wireless communication networks, which is composed of multiple UAVs and multiple ground terminals (GTs). Considering the FSO channel model and line of sight (LoS) probability, the altitude of the UAVs for FSO connection between each GT and UAV is decided by the predetermined radius of the communication area of the GT. A multi-UAV trajectory optimization (MUTO) scheme is proposed to maximize the service time. In the MUTO scheme, first, the network is divided into multiple sectors using graph partitioning, and assigned to each UAV, and then the traveling salesman problem (TSP) is used to determine the order of the GTs that the UAV passes through. The number of UAVs that maximizes the time for the UAV to stay within the communication area of the GT is derived. The UAV trajectories are determined by sequentially minimizing the energy consumed when moving between GTs and maximizing the service time by applying successive convex approximation. In addition, to assist operations that use multiple UAVs, two collision avoidance schemes are applied to the MUTO scheme. First is the additional constraints (AC) method, which uses a geographic formula to control the distance between the UAVs, and second is the initial delay (ID) method, which deliberately adds a delay to the UAV trajectories to provide sufficient UAV spacing. The simulation results show that under low density operation conditions, MUTO-ID and MUTO-AC are sufficient to avoid all UAV collisions, and in overly dense UAV operations with very close UAV trajectories, MUTO-ID can reduce collisions by approximately 80%, MUTO-AC can reduce collisions by approximately 85%, and MUTO-ACID can reduce collisions by approximately 95%, when compared to the MUTO scheme with no collision avoidance support applied.

Multipath TCP (MPTCP) is a promising solution that can provide high end-to-end throughput in fifth generation (5G) mobile networks. Many next-generation applications will require high throughput and low latency simultaneously, but the current MPTCP congestion control algorithms cannot reliably satisfy this requirement. In this paper, a novel MPTCP congestion control scheme named delay-equalized FAST (DEFT) is proposed to achieve high throughput and low end-to-end (E2E) delay in 5G networks. First, in order to achieve high throughput, DEFT includes a novel window control algorithm that shows fast responsiveness when the state of the millimeter-wave (mmWave) link changes from line-of-sight (LOS) to non-LOS (NLOS) and vice versa. Second, in order to achieve low E2E delay, DEFT includes a delay-equalizing algorithm which minimizes additional reordering delay in the receive buffer. The performance of DEFT was evaluated based on ns-3 simulation and was compared with wVegas, Balia, and delay-adapted LIA. Simulation results show that DEFT can provide a significant goodput gain and application-level E2E delay reduction for the range of interest.

With the wide spread of various smart devices and the proliferation of internet of things (IoT) sensors, the amount of traffic on mobile networks is rapidly increasing, and applications with extreme requirements are increasing. Network function virtualization (NFV) and mobile edge computing (MEC) are emerging as core technologies to satisfy users' real-time service demands. Adapting NFV technology to MECs allows the ability to assign cloud-computing capabilities near the base stations (BSs) of radio access networks (RANs), resulting in extremely fast service access to user equipment (UE). However, placement of virtualized network functions (VNF) within the edge network need to consider the location and the requirements of the user which change in real-time. There has been almost no consideration in the existing research on VNF resource allocation (VNF-RA) based on these aspects. Therefore, in this paper, a VNF resource allocation scheme based on context-aware grouping (VNF-RACAG) technology is proposed that enables groups (based on the geographic context of users, such as location and velocity) to compute the optimal number of clusters to minimize the end-to-end delay of network services. Then, a graph partitioning algorithm is used to minimize user movement between clusters, optimizing the data rate that users lose due to VNF migration.

Energy transfer (ET) and energy harvesting (EH) through radio frequency (RF) signals are a promising technology that can reduce the dependency on batteries in wireless sensor networks. However, there is a tradeoff between the RF-based ET and data communication when they operate in the same frequency band. Therefore, a proper medium access control (MAC) protocol is needed in wireless powered sensor networks (WPSNs). However, a utilization degradation problem occurs when the distributed coordination function (DCF) MAC protocol of the IEEE 802.11 is applied to WPSNs. In order to overcome this problem, this paper extends the IEEE 802.11e enhanced DCF (EDCF) into a harvest-then-transmit-based modified EDCF MAC (HE-MAC) protocol. In addition, the HE-MAC's Markov chain model and steady-state probabilities are derived and used in the performance analysis. Next, based on the steady-state conditions, optimization is conducted to maximize the EH rate, which satisfies the frame generation rate and transfers additional energy to achieve a self-sustained energy consumption profile. Finally, the simulation performance of EH protocols HE-MAC, RF-MAC, and DOS are compared, where the results show that HE-MAC provides in a superior performance for the range of interest.

Network slicing is one of the key technologies for 5G mobile communications. Depending on the requirements of network services, the traffic flow to the core cloud may be a problem. Therefore, network slicing in mobile edge computing (MEC) which cloud computing functionalities can be allocated on the base station near the users becomes important. By adapting network function virtualization (NFV) technology to the MEC network, this paper provides a service function chaining (SFC) slicing scheme that uses the popularity of network services to decide the number of replicas of network services to minimize service time.

In this paper, cooperative caching based content delivery management is analyzed in wireless networks. The wireless network consists of mobile devices which can download mobile contents (e.g., e-book, multimedia data, Apps etc.) and share these contents with other devices. Each device can download contents from the content provider (CP) via cellular network by paying network infrastructure cost, or receive support from another device via complementary WLAN or WPAN. In this paper, a cluster-based cooperative content delivery structure in wireless networks is proposed, and the cooperative caching scheme is analyzed to reduce the content delivery cost of each wireless devices.

Exposed terminals are critical performance degrading factors in wireless networks adopting carrier sense multiple access with collision avoidance (CSMA/CA). The objective of this paper is to minimize the exposed terminals and enhance spatial reuse. Therefore, an analytical model is provided to show how much gain can be obtained by resolving the exposed sender (EXS) and exposed receiver (EXR) problems in terms of capacity and delay. It is shown that resolving EXS and EXR both outperform the conventional CSMA/CA significantly. In addition, a transmission power control scheme that can jointly address the hidden and exposed terminal problem is introduced. Results demonstrate that the proposed scheme outperforms the other schemes in terms of spatial reuse, power consumption, and thus, the overall throughput and energy efficiency improves.

The performance of a time division multiple access (TDMA) based wireless mesh network (WMN) depends highly on the broadcast scheduling algorithm (BSA) applied. In this paper, it is demonstrated that the required throughput of a node is based on the amount of packets generated at that node and also the packets that need to be relayed through the node. Therefore, depending on the node's position within the WMN, nodes will have different throughput requirements. In order to quantify the required throughput of a node, the metric transmission demand (TD) is defined and a TD based BSA is proposed. Second, a TD based channel utilization metric named transmission efficiency is introduced and a modified way to use the Pollaczek-Khintchine (P-K) formula is also proposed to include the influence of TD in average delay computations. Results demonstrate that the proposed BSA is scalable and provides higher throughput and less average delay compared to other BSAs.

In this paper, a performance analysis on multihop wireless networks based on CSMA/CA for non-saturated environments is presented. Specifically, the model provides an accurate per-flow throughput performance for various types of heterogeneous non-saturated traffic. Simulation results confirm the accuracy of the model which provides deeper insights into the behavior of random access protocols based on CSMA/CA.

In this paper, a random synthetic stochastic vector mobility model (VMM) to simulate vehicle traffic movement for mobile and vehicle ad hoc networks is proposed. The objective of the VMM is to be able to easily regenerate random mobility patterns that accurately match the stochastic characteristics of vehicles (e.g., cars and buses) better than other random synthetic stochastic mobility simulators, which include random walk, Gauss-Markov mobility, fluid flow, and random way point. An essential characteristic of vehicle mobility is that it has directionality and is targeted toward a destination while showing a certain degree of randomness in speed and angle changes as it moves toward its destination. To model these characteristics, a destination targeted VMM (DTVMM) is proposed, which can be made to randomly move toward the exact location or the neighborhood of the targeted destination based on the setting of the DTVMM stochastic parameters. When the stochastic parameters of actual traffic traces are used, the simulation results show that the DTVMM movement patterns are very similar to actual traces and have the same stochastic characteristics.

Abstract ZigBee uses the network security and application profile layers of the IEEE 802.15.4 and ZigBee Alliance standards for reliable, low‐powered, wireless data communications. However, the ZigBee protocol has some security weaknesses for newly joining devices. In this paper, these weaknesses are analyzed and a novel elliptical curve identity‐based cryptography (ECIC) protocol is presented to improve the security level of ZigBee networks. The proposed method provides perfect forward secrecy and enables network key transmission between nodes. Therefore, the traffic concentrated on the trustcenter is diminished, and the network efficiency is improved by reducing the key establishment time and processing burden on each sensor node, thereby reducing the overall energy consumption of the sensor network. Copyright © 2010 John Wiley & Sons, Ltd.

A wide variety of information technology (IT) services will become available for vehicles through the IEEE 802.11p, IEEE 1609, and wireless access in vehicular environment (WAVE) standards, and naturally the need for service handover will become a necessity. For WAVE networks, a time coordinated medium access control (MAC) protocol named WAVE point coordination function (WPCF) is proposed in this paper. The mathematical derivations and simulation analysis demonstrate that the proposed WPCF protocol can significantly enhance the utilization efficiency and can support more users based on an equal-length access period when compared to point coordination function (PCF), enhanced distributed channel access (EDCA), or hybrid coordination function (HCF) controlled channel access (HCCA) for vehicle to infrastructure (V2I) communications. The paper also demonstrates the effectiveness of the proposed WAVE handover controller (WHC) and WPCF in minimizing service disconnection time when used for V2I handover. In addition, technical enhancements and messages are added to enable seamless communication services over soft-handover (SHO), and enable minimum handover delay in hard-handover (HHO) situations. The mathematical performance evaluation and the simulation results show that the proposed scheme can significantly improve the handover latency compared to the performance that can be delivered by the current protocols.

This paper proposes a novel method to perform a quantitative analysis of message complexity and applies this method in comparing the message complexity among the mobile ad hoc network (MANET) address autoconfiguration protocols (AAPs). The original publications on the AAPs had incomplete parts, making them insufficient to use on practical MANETs. Therefore, the first objective of the research was to complete the AAPs by filling in the missing gaps to make them operational. The missing procedures that were filled in have been developed based on the most logical procedures being accurate to the original protocol publications. The research in this paper finds applications in wireless networks that apply reduced addresses to achieve less memory usage, smaller overhead, and higher throughput (for example, the IPv6 low-power wireless personal address network (6LoWPAN)), but, as a result, possess a high address duplication probability. This research consists of two cases, where the first case deals with the message complexity analysis of the single-node joining case (SJC) and the second case deals with the complexity analysis of the MANET group merging case (GMC).

In this letter, space time (ST) trellis codes are developed for systems with an arbitrary number of receiver antennas in quasi-static fading channels applying M-ary continuous phase frequency shift keying (CPFSK) modulation with M = 4 and 8 for h = 1/M. The maximizing minimum squared Euclidean distance criterion (MMSEDC) is applied in the code search algorithm when obtaining the ST codes. The results show that a significant performance gain can be obtained by using the ST codes from the MMSEDC code search algorithm compared to the ST codes obtained in X. Zhang and M.P. Fits (2003) for CPFSK modulation systems

We propose a communication system and base station network topology. They apply a constraint-based optimal adaptive modulation algorithm and macroscopic diversity reception combining for enhanced robust wireless network QoS support of reliable broadband mobile data communication services. We extend the adaptive modulation research of S.T. Chung and A.J. Goldsmith (see IEEE Trans. Commun., vol.49, no.9, p.1561-71, 2001) in two ways. First, the lognormal shadowing channel characteristic is included in addition to Rayleigh fading. Second, a macroscopic selection diversity scheme is combined with an adaptive modulation topology to enhance the instantaneous BER and spectral efficiency performance. The results show that optimal adaptive modulation control combined with macroscopic diversity enables QoS constraint-based services to be supported more reliably over a wider range of SNR conditions. Also, the macroscopic diversity selection scheme results in a significant improvement in the spectral efficiency. Wireless data link utilization can be increased while the required BER can be supported. Macroscopic diversity combining enables the adaptive modulation to provide more robust/reliable network QoS support under time varying channel conditions (i.e., as the SNR may fluctuate). As the number of combining diversity links increase, the minimum SNR limit that supports the instantaneous BER requirements is significantly lowered, thus making the wireless networking channel QoS and supportive data rate more robust/reliable.

Earliest deadline first (EDF) has become one of the most promising scheduling schemes for providing quality-of-service (QoS) differentiation over high speed networks. We study the deadline violation (loss) probability at an EDF scheduling switch. An analytical framework based on the dominant time scale (DTS) has been developed for estimating the deadline violation probabilities of the aggregated traffic and the individual flows. This enables us to determine whether a given flow can meet its deadline with the required loss probability. As shown by simulations using real network traffic, the developed asymptotic approximations are accurate enough to predict the real metrics. The framework can serve as the basis for the design of call admission control (CAC) mechanisms which are targeted to provide statistical guarantees on transmission delays and/or loss.

A critical challenge for wireless networking vendors and cellular/mobile phone carrier companies is to be able to accurately estimate the quality of service (QoS) that can be provided based on the network architecture, router/switch topology, and protocol applied. In addition, due to the development of technologies like multiprotocol label switching (MPLS) and generalized MPLS (GMPLS), which can deploy differentiated services (DiffServ), the variation in QoS performance based on the priority assignment is of significant importance. This paper provides a call admission control (CAC) scheme and a theoretical analysis of delay jitter of homogeneous traffic based on a nonpreemptive head-of-the-line (HOL) priority scheme in wireless networks that apply automatic retransmission request (ARQ) to recover the packet errors. The CAC scheme regulates the lower class traffic to provide the required levels of delay jitter for the higher priority classes.

Future high speed packet-switching networks deploying integrated service (IntServ) or differentiated service (DiffServ) architectures are expected to provide heterogeneous quality-of-service (QoS) guarantees for a variety of applications. Call admission control (CAC) plays a critical role in achieving this goal and is an integration of the traffic models, scheduling disciplines, and QoS specifications. Its major task is to decide whether a new connection should be granted while the QoS requirements of all the connections are to be satisfied. However, it is well known that developing an effective and efficient CAC algorithm for a stochastic system such as an EDF scheduler is generally very difficult due to the intractability of per-class QoS analysis. A robust control mechanism is necessary for the long-range dependent traffic with infinite variance. In this paper, we present an admission control algorithm for probabilistic services scheduled by an EDF scheduler. In deriving the admission condition, we consider all the connections with similar QoS constraints as an aggregate traffic class. A statistical framework is also developed to analyze the per-class QoS metrics.

In this paper, the provision of quality-of-service (QoS) for real-time traffic over a wireless channel deploying automatic repeat request (ARQ) error control is investigated. By introducing the concepts of ARQ capacity and effective capacity, an analytic model has been derived to evaluate the loss probabilities in both the network layer and the physical layer. In contrast to the previous results, this model quantifies the interaction between the network and physical layers. As shown by the simulation experiments, our analysis can predict the real metrics under a wide range of conditions. This enables the call admission controller in wireless networks to control and optimize traffic QoS using instantaneous channel status information.

Differentiated services (DiffServ) networking technologies are under development with the objective to support diverse classes of traffic that require different quality of service (QoS) guarantees. Recent studies have shown that real network traffic exhibits self-similarity or long-range dependence (LRD) in high-speed communication networks, which has a deteriorating impact on the network performance. To assist the development of admission control mechanisms, which can accommodate heterogeneous traffic including short-range independence and long-range dependence, this paper proposes a measurement-based approach to estimate the buffer overflow probability for each priority queue in a multiplexer deploying the head-of-the-line (HOL) priority discipline. The accuracy and effectiveness of this model has been verified by simulations. The results of this paper will provide a practical insight into the buffer dimensioning and admission control design of a HOL multiplexer.

Wireless application of orthogonal frequency division multiplexing (OFDM) is gaining much attention as a modulation technique for high speed data transmission in various applications due to its good properties against frequency selective fading, intersymbol interference (ISI), and intercarrier interference (ICI). However there are still several issues related to synchronization and multiple access that require more research. We investigate the performance of OFDM transmission through slotted ALOHA multiple access and compare the results to unslotted ALOHA OFDM systems. The results show that the OFDM slotted ALOHA technique has advantages in SNR, throughput, and frame synchronization performance compared to unslotted ALOHA with OFDM, which suggests slotted ALOHA as a candidate for multiple access for OFDM wireless communication applications.

The average codeword success probability of the majority-logic-like vector symbol (MLLVS) code is derived for the following two cases: (1) single-pass decoding and (2) upper bound of multipass decoding, when the received word has more than (J-1) symbol errors, where J is the number of check sum equations. The MLLVS code has been simulated by Metzner (1996), and it was concluded that the average error correcting capability of MLLVS codes exceed the decoding capability of Reed-Solomon codes, but is achieved with less complexity. Additionally, for codes that have larger structures, the error correcting capability is sustained even further with a high probability of decoding success through multipass decoding procedures. The mathematical derivations of the error correction performance beyond (J-1) symbol errors serve as theoretical proof of the MLLVS code error correcting capability that was shown only through simulation results until now by Metzner. One characteristic feature of this derivation is that it does not assume any specific inner code usage, enabling the derived decoding probability equations to be easily applied to any inner code selected, of a concatenated coding structure.