High-performance Communication Research Articles

Efficient MIMO Antenna Design Using Metamaterials for Compact and High-Performance Communication Applications

Efficient MIMO Antenna Design Using Metamaterials for Compact and High-Performance Communication Applications

Challenges and Solutions in High-Speed SerDes Data Path Design

Modern high-performance communication systems require high-speed Serializer/Deserializer (SerDes) data path design to meet the growing demand for data throughput and reliability due to technological advancements and data-intensive applications. This research article examines the complex problems of designing high-speed SerDes data routes and suggests novel solutions. Data fidelity across high-speed channels requires signal integrity, the first key difficulty. Attenuation, crosstalk, and EMI may degrade SerDes signal performance. Advanced methods like equalization, impedance matching, and effective shielding mitigate these impacts. Equalization, including feedforward and feedback methods, reduces channel losses and distortions, enhancing signal quality. Power consumption is another major issue as data rates and system complexity rise. Power-hungry high-speed SerDes circuits cause thermal management and energy efficiency difficulties. Adaptive power management, low-power design, and new materials with higher thermal performance and lower power dissipation are discussed in the study. Design complexity is another issue in high-speed SerDes data route design. High-precision timing and synchronization and many component integration in a small area need advanced design tools and methods. The study emphasizes simulation and modeling for complexity management and design for testability (DFT) for full verification and debugging.

Adaptive Artificial Intelligence Techniques for QoS and Congestion Management in 5G Wireless Networks: A Literature

In the rapidly evolving landscape of 5G wireless networks, ensuring Quality of Service (QoS) and managing congestion effectively are critical challenges that must be addressed to meet the stringent performance requirements of modern applications. This paper presents an in-depth study on the application of adaptive artificial intelligence (AI) techniques for QoS and congestion management in 5G wireless networks. By leveraging machine learning (ML) and deep learning (DL) algorithms, we propose an intelligent framework that dynamically adjusts network parameters in real-time to optimize performance and mitigate congestion. The proposed approach integrates reinforcement learning for adaptive decision-making, convolutional neural networks (CNNs) for traffic pattern recognition, and long short-term memory (LSTM) networks for predictive analysis of network congestion trends. Through extensive simulations and real-world testing, our framework demonstrates significant improvements in QoS metrics such as latency, throughput, and packet loss, while efficiently managing congestion across diverse network scenarios. The results indicate that adaptive AI techniques hold immense potential in enhancing the robustness and efficiency of 5G wireless networks, paving the way for more reliable and high-performance communication systems. KEYWORDS: artificial intelligence (AI), machine learning (ML), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks

Flexible Deployment of Machine Learning Inference Pipelines in the Cloud–Edge–IoT Continuum

Currently, deploying machine learning workloads in the Cloud–Edge–IoT continuum is challenging due to the wide variety of available hardware platforms, stringent performance requirements, and the heterogeneity of the workloads themselves. To alleviate this, a novel, flexible approach for machine learning inference is introduced, which is suitable for deployment in diverse environments—including edge devices. The proposed solution has a modular design and is compatible with a wide range of user-defined machine learning pipelines. To improve energy efficiency and scalability, a high-performance communication protocol for inference is propounded, along with a scale-out mechanism based on a load balancer. The inference service plugs into the ASSIST-IoT reference architecture, thus taking advantage of its other components. The solution was evaluated in two scenarios closely emulating real-life use cases, with demanding workloads and requirements constituting several different deployment scenarios. The results from the evaluation show that the proposed software meets the high throughput and low latency of inference requirements of the use cases while effectively adapting to the available hardware. The code and documentation, in addition to the data used in the evaluation, were open-sourced to foster adoption of the solution.

CoopeRIS: A framework for the simulation of reconfigurable intelligent surfaces in cooperative driving environments

Future connected vehicles will require high-performance communication technologies for advanced cooperative driving applications such as maneuvering and cooperative perception. mmWave communications can meet the bandwidth requirements of such applications, but the typically harsh propagation conditions of vehicular environments hinder the broad adoption of mmWave devices on cars. Reconfigurable intelligent surfaces (RISs) can help mitigate this problem by enabling the reflection of signals in a configurable direction. In turn, this can result in more stable non-line of sight (NLoS) links whenever a LoS path is not available. RISs have recently gained attention in the vehicular domain but, while providing benefits, they also introduce a lot of research challenges. To measure their effectiveness at scale, it is necessary to develop simulation tools that can reproduce their characteristics with high fidelity and federate them with existing cooperative driving simulation frameworks. In this work we present CoopeRIS, an open-source simulation framework federated within the Plexe/Veins/SUMO ecosystem, capable of modeling and simulating RIS-based mmWave communications in a vehicular environment. We exploit CoopeRIS to perform an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communication. In addition we propose a method to combine multiple RIS configurations into a single one to enable multi-user service delivery, showing its performance via CoopeRIS. The insights we presents within this work show the potential of such simulation framework and thus how the community can build further research work on top if it.

Bulk glass-ceramic composites and ULTCC substrates for microwave and millimetre-wave applications

In this study, a new ultra-low-temperature co-fired ceramic (ULTCC) glass-ZnO-SiO2-B2O3CeO2−MgO-Al2O3 composite was developed and its applicability as a dielectric substrate in emerging high-performance communication technologies in the microwave and millimetre-wave frequency ranges was investigated. The novel material composition enables achieving good sinterability at a low fabrication temperature of 470 °C. Extensive studies have been conducted on the microstructure, porosity, water absorption, surface roughness, and thermal expansion coefficient. Following this analysis, the dielectric properties of the bulk glass-ceramic composites and the final ULTCC substrates were characterised. These measurements were conducted within a broad frequency range of 5 GHz–1.1 THz using split-post dielectric resonators, a Fabry-Perot open resonator, and a time-domain spectrometer, resulting in quasi-continuous frequency characteristics of the complex permittivity. Furthermore, the fabricated ULTCC substrates were subjected to surface-wise measurements, which provided 2D maps of the dielectric properties as qualitative and quantitative measures of the uniformity and quality of the material.

An Adaptive State Consistency Architecture for Distributed Software-Defined Network Controllers: An Evaluation and Design Consideration

The Physically Distributed Logically Centralized (PDLC) software-defined network (SDN) control plane is physically dispersed across several controllers with a global network view for performance, scalability, and fault tolerance. This design, providing control applications with a global network view, necessitates network state synchronization among controllers. The amount of inter-controller synchronization can affect the performance and scalability of the system. The absence of standardized communication protocols for East-bound SDN interfaces underscores the need for high-performance communication among diverse SDN controllers to maintain consistent state exchange. An inconsistent controller’s network view can significantly impact network effectiveness and application performance, particularly in dynamic networks. This survey paper offers an overview of noteworthy AI and non-AI solutions for PDLC SDN architecture in industry and academia, specifically focusing on their approaches to consistency and synchronization challenges. The suggested PDLC framework achieves an adaptive controller-to-controller synchronization rate in a dynamic network environment.

FineMon: An Innovative Adaptive Network Telemetry Scheme for Fine-Grained, Multi-Metric Data Monitoring with Dynamic Frequency Adjustment and Enhanced Data Recovery

Network telemetry, characterized by its efficient push model and high-performance communication protocol (gRPC), offers a new avenue for collecting fine-grained real-time data. Despite its advantages, existing network telemetry systems lack a theoretical basis for setting measurement frequency, struggle to capture informative samples, and face challenges in setting a uniform frequency for multi-metric monitoring. We introduce FineMon, an innovative adaptive network telemetry scheme for precise, fine-grained, multi-metric data monitoring. FineMon leverages a novel Two-sided Frequency Adjustment (TFA) to dynamically adjust the measurement frequency on the Network Management System (NMS) and infrastructure sides. On the NMS side, we provide a theoretical basis for frequency determination, drawing on changes in the rank of multi-metric data to minimize monitoring overhead. On the infrastructure side, we adjust the frequency in real-time to capture significant data fluctuations. We propose a robust Enhanced-Subspace-based Tensor Completion (ESTC) to ensure accurate recovery of fine-grained data, even with noise or outliers. Through extensive experimentation with three real datasets, we demonstrate FineMon's superiority over existing schemes in reduced measurement overhead, enhanced accuracy, and effective capture of intricate temporal features.

Digital Signal Processing Oriented Electronic Communication Engineering Applications and Practices

This paper first describes the applications of digital signal processors in software radio, speech compression coding, modems and GPS systems, demonstrating the versatility of digital signal processors in practical communication systems. Next, digital signal processing oriented electronic communication practices are presented in the system design section to design high performance communication systems for specific communication needs and environmental conditions. The digital signal processing platform used in the platform architecture is optimized for signal processing and data transmission. The BER is 0.008% when the signal-to-noise ratio is -10 dB. The time complexity of digital signal processing is 7.2 ms when the number of communication nodes is 1000. It shows that digital signal processing oriented electronic communication engineering is important for improving the performance, reliability and efficiency of communication systems.

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a “place-and-forget” IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems.

A Rail-To-Rail and Low Offset Novel Strongarm Comparator Circuit for Low Power Data Converter Architectures

With the rapid improvements in the design of advanced high performance communication receivers, hand-held electronic devices are in widespread usage for some time. These devices facilitate high speed and secured access to internet data. The demand for realizing a smart and better usage experience puts forth strict requirements on the design aspects of next-generation high speed low power complementary metal oxide semiconductor (CMOS) receiver design. One of the major modules in the implementation of high speed low power CMOS receiver device is the analog to digital converter (ADC) architecture. In the process of conversion from analog to digital signals, Quantization and sampling operations are vital and are realized using comparator circuits. The comparator design has a significant role in the design of data converter architecture. Several comparator architectures exist, but StrongARM topology is discussed and implemented in this work due to its negligible static power dissipation and rail to rail output voltages. The proposed novel comparator architecture is designed and simulated in 90nm CMOS process using Cadence Virtuoso tool and operated at supply voltage of VDD=1.5V, a clock frequency of 250MHz. Compared to the previous designs, the energy delay product was reduced by 8% which is an important observation to be observed for use in wireless sensor node applications.

MiddleNet: A Unified, High-Performance NFV and Middlebox Framework With eBPF and DPDK

Traditional network resident functions (e.g., firewalls, network address translation) and middleboxes (caches, load balancers) have moved from purpose-built appliances to softwarebased components. However, L2/L3 network functions (NFs) are being implemented on Network Function Virtualization (NFV) platforms that extensively exploit kernel-bypass technology. They often use DPDK for zero-copy delivery and high performance. On the other hand, L4/L7 middleboxes, which have a greater emphasis on functionality, take advantage of a full-fledged kernelbased system. L2/L3 NFs and L4/L7 middleboxes continue to be handled by distinct platforms on different nodes. This paper proposes MiddleNet that develops a unified network resident function framework that supports L2/L3 NFs and L4/L7 middleboxes. MiddleNet supports function chains that are essential in both NFV and middlebox environments. MiddleNet uses the Data Plane Development Kit (DPDK) library for zero-copy packet delivery without interrupt-based processing, to enable the ’bumpin-the-wire’ L2/L3 processing performance required of NFV. To support L4/L7 middlebox functionality, MiddleNet utilizes a consolidated, kernel-based protocol stack for processing, avoiding a dedicated protocol stack for each function. MiddleNet fully exploits the event-driven capabilities of the extended Berkeley Packet Filter (eBPF) and seamlessly integrates it with shared memory for high-performance communication in L4/L7 middlebox function chains. The overheads for MiddleNet in L4/L7 are strictly load-proportional, without needing the dedicated CPU cores of DPDK-based approaches. MiddleNet supports flow-dependent packet processing by leveraging Single Root I/O Virtualization (SR-IOV) to dynamically select the packet processing needed (Layers 2 -7). Our experimental results show that MiddleNet achieves high performance in such a unified environment.

CASSR based metamaterial tunable low pass filter

With the need for compact, discrete, and tunable passive devices in the upcoming wireless communication techniques, metamaterials have been proven to their incredible properties and have raised a new paradigm. The article discusses the possibility to design an electrically small metamaterial low pass filter (LPF) from a Complementary Asymmetric Single Split Resonator (CASSR) with a tunable pass-band for high-performance communication systems. The proposed CASSR filter on low-cost FR4 substrate of relative permittivity 4.4 and thickness 1.6 mm for a cut-off frequency of 1.12 GHz shows a roll-off rate of 159 dB/GHz. It is electrically small with a footprint area of 0.112λ × 0.112λ mm2 with ka = 0.70.

Mars: Near-Optimal Throughput with Shallow Buffers in Reconfigurable Datacenter Networks

The performance of large-scale computing systems often critically depends on high-performance communication networks. Dynamically reconfigurable topologies, e.g., based on optical circuit switches, are emerging as an innovative new technology to deal with the explosive growth of datacenter traffic. Specifically, periodic reconfigurable datacenter networks (RDCNs) such as RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have been shown to provide high throughput, by emulating a complete graph through fast periodic circuit switch scheduling. However, to achieve such a high throughput, existing reconfigurable network designs pay a high price: in terms of potentially high delays, but also, as we show as a first contribution in this paper, in terms of the high buffer requirements. In particular, we show that under buffer constraints, emulating the high-throughput complete graph is infeasible at scale, and we uncover a spectrum of unvisited and attractive alternative RDCNs, which emulate regular graphs, but with lower node degree than the complete graph. We present Mars, a periodic reconfigurable topology which emulates a d-regular graph with near-optimal throughput. In particular, we systematically analyze how the degree~d can be optimized for throughput given the available buffer and delay tolerance of the datacenter. We further show empirically that Mars achieves higher throughput compared to existing systems when buffer sizes are bounded.

Enhanced Electromagnetic Wave Absorption Properties of FeCo-C Alloy by Exploiting Metamaterial Structure

This study presents a tri-layer broadband metamaterial absorber that operates in the GHz range. The absorber was composed of a polyhedral iron-cobalt alloy/graphite nanosheet material arranged in a flat sheet with two punched-in rings for the top layer, a continuous FR-4 layer at the middle, and a continuous copper layer at the bottom. For the normal incidence of the electromagnetic wave, the proposed absorber demonstrated an exceptional broadband absorption in a frequency range of 7.9–14.6 GHz, revealing an absorption exceeding 90%. The absorption magnitude remains to be above 90% in a frequency range of 8–11.1 GHz for transverse-electric-polarized waves at incident angles up to 55°. For both the transverse-magnetic- and electric-polarized waves, the absorption exceeds 90% in a frequency range of 9.5–14.6 GHz. The physical mechanism behind the absorption properties is analyzed thoroughly through the electric and magnetic field distributions. The obtained results could contribute potentially to the development of microwave applications based on metamaterial absorbers, such as radar-stealth technology, electromagnetic shielding for health safety, and reduced electromagnetic interferences for high-performance communications and electronic devices.

Securing UAV Flying Base Station for Mobile Networking: A Review

A flying base station based on an unmanned aerial vehicle (UAV) uses its mobility to extend its connectivity coverage and improve its communication channel quality to achieve a greater communication rate and latency performances. While UAV flying base stations have been used in emergency events in 5G networking (sporadic and temporary), their use will significantly increase in 6G networking, as 6G expects reliable connectivity even in rural regions and requires high-performance communication channels and line-of-sight channels for millimeter wave (mmWave) communications. Securing the integrity and availability of the base station operations is critical because of the users’ increasing reliance on the connectivity provided by the base stations, e.g., the mobile user loses connectivity if the base station operation gets disrupted. This paper identifies the security issues and research gaps of flying base stations, focusing on their unique properties, while building on the existing research in wireless communications for stationary ground base stations and embedded control for UAV drones. More specifically, the flying base station’s user-dependent positioning, its battery-constrained power, and the dynamic and distributed operations cause vulnerabilities that are distinct from those in 5G and previous-generation mobile networking with stationary ground base stations. This paper reviews the relevant security research from the perspectives of communications (mobile computing, 5G networking, and distributed computing) and embedded/control systems (UAV vehicular positioning and battery control) and then identifies the security gaps and new issues emerging for flying base stations. Through this review paper, we inform readers of flying base station research, development, and standardization for future mobile and 6G networking.

Forward(ing) from the moon - lunar pathfinder data relay satellite, paving the way for communication and navigation constellation

Lunar Pathfinder is the first commercial data-relay satellite, providing cost-effective, high-performance communication services to the next generation of lunar missions. Since the unveiling of the Lunar Pathfinder vision, progress has been made steadily, and the spacecraft is on track to begin operations as early as 2025. Within the last year, SSTL Lunar have been actively working with ESA and NASA to secure these communications services in order to support their upcoming lunar missions. As well as this, a few hosted payloads have been added to the spacecraft that will increase the scientific value of the mission. Lunar Pathfinder will enable both institutional and commercial missions to carry out data intensive activities around the moon, without the need for complex and costly on-board communication equipment and access to global ground networks. An ESA Moonlight Phase A/B1 study is also ongoing, investigating a full lunar constellation that will provide enhanced communication and navigation services. SSTL Lunar is a brand of SSTL created to promote, offer and deliver the future lunar communication and navigation services. An integral part of SSTL, it symbolizes the start of an off-planet service offering for lunar assets. Lunar Pathfinder falls under the small satellite class, with a mass of 300kg, and will operate in an Elliptical Lunar Frozen Orbit (ELFO), offering communication services to lunar missions of all types (orbiters, rovers, landers, stationary instruments). Due to launch and enter operation in 2025, the spacecraft will be in service for 8 years. This programme is supported by ESA under a commercial partnership programme. The Lunar Pathfinder services relieve some of the burdens faced by lunar missions using Direct to Earth (DTE) communications for transmitting data back to their ground stations. DTE requires a direct line of sight, making it impossible for communications on the far side of the Moon, and exposing the risk of communications interruption due to obstructing terrain - especially relevant for mobile assets. Lunar Pathfinder's orbit provides high availability communications for those missions on the far-side and polar regions, unlocking a plethora of exploration opportunities. The reliance of DTE on the Deep Space Network (DSN) presents further challenges. Subscription to the network is routinely over capacity, leading to limitations on the data-rate and contact times achievable, which subsequently affects the volume of data that can be transferred. As a consequence, the optimal scientific and commercial potential of these missions cannot be realised. With a store and forward capability, a proximity link, two simultaneous channels in S-band and UHF for lunar asset links, Lunar Pathfinder is designed to lift these constraints. It allows longer upload durations and improved data-rates with each of the lunar assets, enabling the bulk transmission of data back to Earth Ground Station, ready for distribution to the end users. An ESA GNSS receiver capable of detecting weak signals coming from the Earth GNSS infrastructure (GPS and Galileo), will be on-board Lunar Pathfinder, investigating the potential of GNSS in the future of Lunar navigation. A NASA retro-retroreflector is also hosted by Lunar Pathfinder, along with an ESA radiation monitor, contributing to the knowledge generation that will further support humanity's return to the Moon. To achieve a thriving ecosystem, the supporting infrastructure must be in place to enable sustainable evolution of nascent lunar markets into a mature industry. A critical pillar of this infrastructure is currently being investigated in a PhaseA/B1 study under ESA's Moonlight initiative, in the form of a lunar communications and navigation service, provided by a constellation of satellites. Building on the foundations of Lunar Pathfinder, an SSTL-led consortium, along with established telecoms operators and ground segment operators, are characterising the end-to-end architecture of the service. Collaboration and interoperability is essential for the sustainable future of the lunar economy, therefore this communication and navigation service is designed in harmony with other infrastructure elements such as Gateway and LunaNet. This allows the service to be easily leveraged by institutional, commercial and governmental users alike, enabling flexible communication and navigation capabilities, energising disruptive applications and business models.

Causal Language Model Aided Sequential Decoding With Natural Redundancy

A high-performance communication receiver desires a sufficient and accurate recognition of transmitted sources. In this paper, we propose a sequential decoding algorithm for the robust reception of sources with natural redundancy (NR) over the AWGN channel. To fully exploit the abundant NR in the exemplified English text sources, a causal language modeling (CLM) with a powerful Transformer decoder neural network (NN) structure is adopted for modeling the joint probability distribution. For versatility of byte-level tokenization, the UTF-8 encoding scheme is considered for the Wikipedia corpus, namely the English edition of the Wiki-40B dataset. The tree search-based M-algorithm (MA) is integrated with CLM, denoted as CLM-MA algorithm, to synthesize the a priori probability of information sequence and likelihood values of polluted symbols. Both simulation experiments and hardware platform evaluations are conducted to analyze the performance of the CLM-MA algorithm. With an adequately large M value, tremendous performance profit is achievable, especially for high-efficiency modulation levels and extremely noisy conditions. This mechanism of adopting the pre-trained NN model merely desires self-supervised learning architecture and eliminates the requirement of high-cost and accurate labels, which paves a new way for communication receiver design.

Mars: Near-Optimal Throughput with Shallow Buffers in Reconfigurable Datacenter Networks

The performance of large-scale computing systems often critically depends on high-performance communication networks. Dynamically reconfigurable topologies, e.g., based on optical circuit switches, are emerging as an innovative new technology to deal with the explosive growth of datacenter traffic. Specifically, periodic reconfigurable datacenter networks (RDCNs) such as RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have been shown to provide high throughput, by emulating a complete graph through fast periodic circuit switch scheduling. However, to achieve such a high throughput, existing reconfigurable network designs pay a high price: in terms of potentially high delays, but also, as we show as a first contribution in this paper, in terms of the high buffer requirements. In particular, we show that under buffer constraints, emulating the high-throughput complete graph is infeasible at scale, and we uncover a spectrum of unvisited and attractive alternative RDCNs, which emulate regular graphs, but with lower node degree than the complete graph. We present Mars, a periodic reconfigurable topology which emulates ad-regular graph with near-optimal throughput. In particular, we systematically analyze how the degree d can be optimized for throughput given the available buffer and delay tolerance of the datacenter. We further show empirically that Mars achieves higher throughput compared to existing systems when buffer sizes are bounded.

Enhancing security & QoS of trust-enabled wireless networks using machine learning powered transformable blockchain sharding

Trust enabled wireless networks use temporal behaviour information of nodes in order to classify them into different trust categories. This information is utilized by the router for high performance communication that is optimized in terms of end-to-end delay, energy consumption, throughput, packet delivery ratio, and other quality of service (QoS) parameters. Establishing security in trust enabled wireless networks is a difficult task, because high trust nodes might be compromised by external or internal attacks, thereby disrupting normal communication. In order to perform this task, blockchain based security models are deployed. These models provide high transparency, comprehensive traceability, distributed processing, and data immutability, which makes them highly deployable for trust enabled networks. Blockchain models enforce compulsive verification of data before communication, which makes them resilient to DDoS, MITM, denial of service, and other data-based attacks. In order to enforce these checks, each of the block is hashed, and the hash values are compared with every existing block in the chain. These checks include hash uniqueness, and hash pattern validations; the later of which is decided by the network designer(s). As the length of blockchain increases, computational complexity of adding a new block (a.k.a. blockchain mining) increases exponentially, which adds to the end-to-end delay, and energy consumption of wireless nodes, which is a drawback of these models. To avoid this, sidechains & blockchain sharding models are developed. These models work by dividing the existing blockchain into multiple parts (based on a certain pre-set criteria), and then use the parts for high speed and low power mining. But again, due to increase in number of sidechains, the computational complexity of managing these chains, and locating data blocks within them increases exponentially. Moreover, in any practical wireless network, there is a need to communicate modifiable data, which is not supported by current blockchain implementations. In order to resolve these issues, this text proposes a transformable blockchain sharding model, which is managed via a light weight meta heuristic method for high-speed data access. The proposed model aims at reducing computational complexity of sidechain maintenance with the help of directed acyclic graphs (DAGs) for storing of hash ranges. The model also incorporates a transformable blockchain solution, wherein the block structure is designed to incorporate selectively mutable as well as non-mutable information. Both the mutable and non-mutable information is encrypted using high performance elliptic curve cryptosystem, which makes it highly secure against network attacks. The proposed model showcases 15% improvement in network lifetime, 8% reduction in end-to-end delay, 22% reduction in computational complexity, and 18% improvement in network throughput when compared with various blockchain and sidechain based wireless networks, thereby assisting in development of a high QoS and highly secure wireless network.