Average Throughput Improvement Research Articles

Designing a Scalable and Area-Efficient Hardware Accelerator Supporting Multiple PQC Schemes

This study introduces a hardware accelerator to support various Post-Quantum Cryptosystem (PQC) schemes, addressing the quantum computing threat to cryptographic security. PQCs, while more secure, also bring significant computational demands, which are especially problematic for lightweight devices. Previous hardware accelerators are typically scheme-specific, which is inefficient given the National Institute of Standards and Technology (NIST)’s multiple finalists. Our approach focuses on the shared operations among these schemes, allowing a single design to accelerate multiple candidate PQCs at the same time. This is further enhanced by allocating resources according to performance profiling results. Our compact, scalable hardware accelerator supports four of NIST PQC finalists, achieving an area efficiency of up to 81.85% compared to the current state-of-the-art multi-scheme accelerator while supporting twice as many schemes. The design demonstrates average throughput improvements ranging from 0.97× to 35.97× across the four schemes and their main operations, offering an efficient solution for implementing multiple PQC schemes within constrained hardware environments.

A SDN improvement scheme for multi‐path QUIC transmission in satellite networks

AbstractIn recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS.

Динамическое использование спектра в LTE и NR для развертывания 5G

Both Static allocation of the frequency spectrum as deployed in 4G-LTE as well as dynamically configured 5G NR physical layer channels does not satisfy the demand of current exponential growth of wireless users. As 5G NR will perform better in Line of Sight (LOS) scenarios while 4G-LTE provides remarkable performance in non-Line of sight (NLOS) scenarios. Dynamic Spectrum Sharing (DSS) allows operators to run LTE and 5G NR on the same band simultaneously. This paper illustrates the relationship and working theory of Dynamic Spectrum Sharing to comprehend how the latest 5G standard will result in a substantial improvement in average user throughput and improved network efficiencies. This is an effort to propose a generic framework to initial Deployment of 5G network.

Mobility aware blockage management of a multi-user multi-cell hybrid Li-Fi Wi-Fi system with freeform diversity receiver

In order to accommodate high data traffic, the co-deployment of heterogeneous networks like light fidelity (Li-Fi) and wireless fidelity (Wi-Fi) can offer supplementary small-cell layers of support and bring about a paradigm shift in achievable system throughput and quality of service. However, dense deployment of Li-Fi access points(APs) in an indoor arena often leads to co-channel-interference (CCI) that can be fruitfully diminished by adopting a customized optical front-end called freeform diversity receiver (FDR). With regard to multi-user association, this work judiciously imparts the motivation behind the adoption of FDR in a hybrid Li-Fi Wi-Fi network (HLWNet). Based on different mobility scenarios and blockage conditions the performance of the proposed HLWNet has been evaluated. A Li-Fi channel model with FDR and a rule-based resource allocation algorithm (RBRA) has been proposed for the purpose. Nevertheless, the network data quality of the multi-user system has been estimated in terms of packet loss, latency, and fairness index. Unlike the existing optimal resource allocation (ORA), the RBRA demonstrates superior network performance. Additionally, the execution time in the RBRA is reduced by a factor of 89 compared to the optimal resource allocation algorithm. Simulation results show a satisfactory fairness index of more than 0.85 and latency within 2.1 ms for a 10-user association inside an indoor environment of 25m2 floor area. In the absence of LOS blockage, the system throughput exhibits minimal variation, staying consistent for both methods with less than a 2% difference. However, significant improvement in average user throughput and effective system throughput has been observed compared to the existing studies. Despite line-of-sight (LOS) blockage, the proposed system with RBRA consistently maintains throughput within the range of 2.01 Gbps to 2.55 Gbps. The average user throughput, varying from 182 Mbps to 480 Mbps, is contingent upon the number of associated users, which ranges from 4 to 14.

An Interference-Aware Resource-Allocation Scheme for Non-Cooperative Multi-Cell Environment

Inter-cell interference cancellation has been investigated for several decades and has become an elementary technique for modern wireless networks. However, the existing interference cancellation mechanism rarely considers the historical channel variations and interference characteristics. In this paper, we propose an interference-aware prediction-based resource-allocation strategy to deal with multi-cell interference, where the historical noisy channel state and the acknowledgment feedback are fully utilized. Together with the predicted interference patterns, our proposed joint sub-channel allocation and rate selection mechanism can achieve better average throughput performance. Through the numerical as well as the prototyping results, we show that our proposed scheme is able to provide more than 9.7% and 8% average throughput improvement compared with many existing baselines.

Throughput Enhancement of Dynamic Full-Duplex Cellular System by Distributing Base Station Reception Function

In-band full-duplex (IBFD) is a promising technology for improving spectral efficiency beyond 5G and 6G. The dynamic duplex cellular (DDC) system has been proposed as a method for the phased introduction of IBFD into cellular systems while maintaining backward compatibility. Although previous studies have shown that DDC systems can improve the average user throughput, twice the spectral efficiency compared to half-duplex (HD), ideally expected for IBFD, has not been achieved. The main component that disturbs throughput enhancement is the residual component of self-interference (SI) at the base station (BS), which SI cancellers cannot entirely suppress. In this study, we propose a DDC system with a dedicated transmitter (DT) and multiple distributed receivers called remote receivers (RRs) for the BS to reduce the effective SI and enhance uplink reception quality. We implement user equipment scheduling and power control for the DDC system in the topology with DT and multiple RRs and improvement by IBFD. evaluate its performance using computer simulations in a single-cell environment. The proposed DDC system with receiver-distributed topology achieves up to 96% improvement in average user throughput of uplink and downlink from the HD system with the same topology, which is close to the ideal performance.

Active lane management and control using connected and automated vehicles in a mixed traffic environment

Traditionally, active traffic management systems (ATM) have included lane management systems (LMS) mounted on overhead gantries to provide merge advisories to human drivers when lanes are closed due to incidents or work zones. Advancements in connected and automated vehicles (CAVs) provide opportunities for developing applications to send advisory messages directly to CAVs, which provides an efficient way of managing traffic. This research develops an enhanced CAV based lane management and control application to overcome the limitations of infrastructure-based systems and improve operations, fuel efficiency, and safety in a mixed autonomy operation of CAVs and human-driven vehicles. Impacts of this approach on operations, fuel efficiency, and safety are compared to traditional LMS using a calibrated simulation model of a site where traditional LMS is present. High fidelity simulation runs of drive cycles were conducted using the Autonomie® model to estimate fuel efficiency effects. The simulation results showed that starting at a lower penetration of 20% CAVs, an average throughput improvement of 3.5% was observed, increasing to a maximum of 14.2% for full penetration of CAVs. The drive cycle simulations revealed a reduction of 47% to 60% in fuel consumption with the CAV based application. Both lane change and rear-end conflicts were also observed to reduce. Likewise, volatility represented by acceleration-deceleration variation, was reduced by an average of 18.2% and 17.6% under varying penetrations of CAVs. Volatility represents variation in driving regimes over an instantaneous period of driving, so this reduction in volatility serves as a positive surrogate indicator of safety and fuel efficiency for this new application as well.

An efficient multi-level cache system for geometrically interconnected many-core chip multiprocessor

<p><span lang="EN-US">Many-core chip multiprocessor offers high parallel processing power for big data analytics; however, they require efficient multi-level cache and interconnection to achieve high system throughput. Using on-chip first level L1 and second level L2 per core fast private caches is expensive for large number of cores. In this paper, for moderate number of cores from 16 to 64, we present a cost and performance efficient multi-level cache system with per core L1 and last level shared bus cache on each bus line of a cost-efficient geometrically bus-based interconnection. In our approach, we extracted cache hit and miss concurrencies and applied concurrent average memory access time to more accurately determine the cache system performance. We conducted least recently used cache policy-based simulation for cache system with L1, with L1/L2, and with L1/shared bus cache. Our simulation results show that an average system throughput improvement of 2.5x can be achieved by using system with L1/shared bus cache system compared to using only first level L1 or L1/L2. Further, we show that the throughput degradation for the proposed cache system is only within 5% for a single bus fault, suggesting a good bus fault tolerance.</span></p>

CIDAN-XE: Computing in DRAM with Artificial Neurons

This paper presents a DRAM-based processing-in-memory (PIM) architecture, called CIDAN-XE. It contains a novel computing unit called the neuron processing element (NPE). Each NPE can perform a variety of operations that include logical, arithmetic, relational, and predicate operations on multi-bit operands. Furthermore, they can be reconfigured to switch operations during run-time without increasing the overall latency or power of the operation. Since NPEs consume a small area and can operate at very high frequencies, they can be integrated inside the DRAM without disrupting its organization or timing constraints. Simulation results on a set of operations such as AND, OR, XOR, addition, multiplication, etc., show that CIDAN-XE achieves an average throughput improvement of 72X/5.4X and energy efficiency improvement of 244X/29X over CPU/GPU. To further demonstrate the benefits of using CIDAN-XE, we implement several convolutional neural networks and show that CIDAN-XE can improve upon the throughput and energy efficiency over the latest PIM architectures.

A Framework for Mitigating DDoS and DOS Attacks in IoT Environment Using Hybrid Approach

The Internet of Things (IoT) has gained remarkable acceptance from millions of individuals. This is evident in the extensive use of intelligent devices such as smartphones, smart television, speakers, air conditioning, lighting, and high-speed networks. The general application area of IoT includes industries, hospitals, schools, homes, sports, oil and gas, automobile, and entertainment, to mention a few. However, because of the unbounded connection of IoT devices and the lack of a specific method for overseeing communication, security concerns such as distributed denial of service (DDoS), denial of service (DoS), replay, botnet, social engineering, man-in-the-middle, and brute force attacks have posed enormous challenges in the IoT environment. Regarding these enormous challenges, this study focuses on DDoS and DoS attacks. These two attacks have the most severe consequences in the IoT environment. The solution proposed in this study can also help future researchers tackle the expansion of IoT security threats. Moreover, the study conducts rigorous experiments to assess the efficiency of the proposed approach. In summary, the experimental results show that the proposed hybrid approach mitigates data exfiltration caused by DDoS and DoS attacks by 95.4%, with average network lifetime, energy consumption, and throughput improvements of 15%, 25%, and 60%, respectively.

Real-time measurements and performance analysis of closed-loop MIMO service for mobile operators

As fifth generation (5G) networks are starting to become commercial, user expectations in terms of new services become high as well. This signifies that mobile communications service providers need to build robust 5G new services as quickly and cost-efficiently as possible. Many new technologies rely on closed-loop (CL) and multiple input multiple output (MIMO) technologies due to emerging cooperation between nodes in next generation networks. In this paper, we first compare different multiantenna transmission modes namely: transmit diversity, open-loop (OL), and CL MIMO spatial multiplexing strategies to provide mobile network operator (MNO) services in terms of their characteristics, ,limitations and benefits. Later we investigate how launching a large-scale CL MIMO deployment strategy can affect the various key performance indicators (KPIs) of the existing services provided by Mobile Network Operators (MNOs) in real-operational network infrastructure in Turkey. Our practical experimental results indicate that, compared to OL MIMO system, CL MIMO can achieve large performance on a practical setup, where up to 3% improvement in cell average throughput, 9% in user throughput, 6% in spectrum efficiency, and 9% in channel quality indicator (CQI) and modulation coding scheme (MCS) are obtained, while reduction by 25% and 17% on sum delay and initial block error rates (IBLER) are observed.

An integrated-multi-RAT framework for multipath-computing in heterogeneous-wireless network

ABSTRACT The bandwidth-intensive applications on Smart-Mobile-Devices (SMDs) are increasing with SMD's colossal growth. The overlapped cellular and non-cellular networks, in hot-spot-places, and SMDs capabilities are significant reasons for this growth. SMD's interfaces-RAT (Radio-Access-Technology) can have complementary link characteristics. The end-users can avail always-best-connectivity (ABC) on their SMDs with complementary RAT characteristics. This paper proposes an Integrated-multi-RAT-utilization (Im-Ru) framework for multipath-computing support to realize ABC for the end-users. The Im-Ru framework has two approaches. The First is a hybrid-RAT-discovery model based on SMD's interfaces, current-location, and identification using ANDSF and MIIS servers. The second is the user's preference-based RAT-selection using weighted-RAT-parameters. We observe that the Im-Ru framework for multipath-computing is useful in future 5G-NR networks. We analyzed the Im-Ru's performance related to average-throughput improvement over the existing approaches for SMD's different speeds and observed a significant improvement. The experimental results show that Im-Ru is more reliable by realizing lower packet-loss and delay than existing work.

Dual-Radio-Assisted (DRA) MAC Protocols for Distributed Terahertz Networks

Terahertz (THz) communication is envisioned as one of the key technologies to satisfy the high data rate demand of intelligent 6G networks. However, directional transmissions, as well as the peculiarities of the THz band, pose challenges on medium access control (MAC) design. In this paper, a dual-radio-assisted MAC protocol supporting concurrent transmission is proposed, in which 2.4/5 GHz omnidirectional radio is utilized for control signaling, while THz beamforming is adopted for data transmissions. The request-to-send (RTS-GHz)/test-to-transmit (TTT-THz) handshake is adopted for initial link establishment. Furthermore, the network table is constructed at each node recording receiver-oriented network allocation vector (RNAV) value, angle-of-departure (AoD) of DATA, and angle-of-arrival (AoA) of RTS-GHz. The RNAV record enables concurrent transmission without collision. The record of the first two dominant AoA paths alleviates the LoS blockage problem. The AoD record further decreases the link association delay by replacing RTS-THz with RTS-GHz. Network performances on the protocol are analytically and numerically investigated. Results indicate that the proposed DRA-MAC protocol with AoD memory can achieve 88.2%, 51.6%, and 50% improvement in average link delay, throughput, and LoS blockage alleviation respectively, compared with existing THz MAC protocols.

A Deflection-Based Deadlock Recovery Framework to Achieve High Throughput for Faulty NoCs

Deadlock is a critical issue in faulty Networks-on-Chips (NoCs). Existing deadlock-free approaches on faulty NoCs suffer from low throughput and poor fairness when the network becomes oversaturated. This problem hinders their practical use as oversaturation scenarios are more frequent on faulty NoCs. To address this issue, a deflection-based deadlock recovery framework is proposed for higher oversaturation performance on faulty NoCs. First, we observe the low oversaturation performance of existing deadlock recovery approaches, and analyze the positive feedback loop that can amplify the negative impact of deadlocks and congestions, which necessitate handling both deadlocks and congestions in a deadlock recovery framework. Second, we propose a novel deadlock recovery framework, which includes an accurate, timely deadlock detection and a highly efficient deadlock recovery. Both the deadlock detection and recovery reduce the average packet traversal latency, thereby improving the average oversaturation throughput. Third, we propose a distributed implementation to make the entire network enter and exit the deflection mode, which is conducted by broadcasting special messages via a bufferless subnetwork. An average oversaturation throughput improvement of 1.1 ~ 8.1× over state-of-the-art approaches is achieved. In terms of fairness, the minimal oversaturation throughput is improved from near zero to half of the peak throughput.

Efficient resource allocation in the IEEE 802.11ax network leveraging OFDMA technology

The IEEE 802.11ax pave the way to deliver the high-speed communications in the Wi-Fi network even in the dense areas. In this regard, the most challenging task is to enhance the throughput as IEEE 802.11ax standard promises to provide four times improvement in average throughput per station. Unfortunately, none of the existing protocols could satisfy the demand of the standard yet. The performance of the IEEE 802.11ax protocol largely depends on the efficient and wise scheduling of resource units to the stations. The uplink scheduling is more challenging than the downlink since in the uplink path many stations send data to the access point where the stations must be synchronized for the OFDMA transmissions. This paper innovates an uplink scheduling protocol named Efficient Resource Allocation (ERA) that promises to provide a high-throughput to the Wireless LAN along with the reduction of retransmissions of the packets. The simulations and analyses show that the proposed protocol would be a robust one to satisfy the promises of the latest IEEE 802.11ax standard. To the best of our knowledge, the proposed protocol is the unique one of its kind where the resource units are distributed to the stations according to their available loads.

ABRAHAM: Machine Learning Backed Proactive Handover Algorithm Using SDN

An important aspect of managing multi access point (AP) IEEE 802.11 networks is the support for mobility management by controlling the handover process. Most handover algorithms, residing on the client station (STA), are reactive and take a long time to converge, and thus severely impact Quality of Service (QoS) and Quality of Experience (QoE). Centralized approaches to mobility and handover management are mostly proprietary, reactive and require changes to the client STA. In this paper, we first created an Software-Defined Networking (SDN) modular handover management framework called HuMOR, which can create, validate and evaluate handover algorithms that preserve QoS. Relying on the capabilities of HuMOR, we introduce ABRAHAM, a machine learning backed, proactive, handover algorithm that uses multiple metrics to predict the future state of the network and optimize the load to ensure the preservation of QoS. We compare ABRAHAM to a number of alternative handover algorithms in a comprehensive QoS study, and demonstrate that it outperforms them with an average throughput improvement of up to 139%, while statistical analysis shows that there is significant statistical difference between ABRAHAM and the rest of the algorithms.

SIMPLER MAGIC: Synthesis and Mapping of In-Memory Logic Executed in a Single Row to Improve Throughput

In-memory processing can dramatically improve the latency and energy consumption of computing systems by minimizing the data transfer between the memory and the processor. Efficient execution of processing operations within the memory is therefore, a highly motivated objective in modern computer architecture. This article presents a novel automatic framework for efficient implementation of arbitrary combinational logic functions within a memristive memory. Using tools from logic design, graph theory and compiler register allocation technology, we developed synthesis and in-memory mapping of logic execution in a single row (SIMPLER), a tool that optimizes the execution of in-memory logic operations in terms of throughput and area. Given a logical function, SIMPLER automatically generates a sequence of atomic memristor-aided logic (MAGIC) NOR operations and efficiently locates them within a single size-limited memory row, reusing cells to save area when needed. This approach fully exploits the parallelism offered by the MAGIC NOR gates. It allows multiple instances of the logic function to be performed concurrently, each compressed into a single row of the memory. This virtue makes SIMPLER an attractive candidate for designing in-memory single instruction, multiple data (SIMD) operations. Compared to the previous work (that optimizes latency rather than throughput for a single function), SIMPLER achieves an average throughput improvement of $435\times $ . When the previous tools are parallelized similarly to SIMPLER, SIMPLER achieves higher throughput of at least $5\times $ , with $23\times $ improvement in area and $20\times $ improvement in area efficiency. These improvements more than fully compensate for the increase (up to 17% on average) in latency.

LG-RAM: Load-aware global resource affinity management for virtualized multicore systems

Abstract Server consolidation and virtualization technologies enable multiple end-users to share a single physical server, substantially improving hardware utilization and reducing energy consumption. However, as modern multicore server architectures shift to non-uniform memory access (NUMA), the complex interplay between data access affinity and shared resource overhead continues to pose challenges to consolidation efficiency. In this paper, we first systematically characterize the performance impacts of server consolidation on NUMA systems with various cloud applications. We find that the virtual machine (VM) memory and network I/O access could both affect the cloud applications performance. Moreover, as consolidation density continues to grow, conventional approaches cannot manage the system loads and thus result in overall system performance degradation. Motivated by these two findings, we then propose a load-aware global resource affinity management framework (LG-RAM) that aims to optimize VM consolidation performance on NUMA systems. LG-RAM consists of three components: the VM resource access monitor quantifies the VM resource access behaviors, the shared resource load detector models the load on shared hardware resources, and the VM resource scheduler makes the scheduling decision according to the information from the above two components. Our evaluations on the two different systems indicate that, compared with state-of-the-art approaches, LG-RAM can exhibit an average throughput improvement of 41.5% and 54.2% on Intel and AMD NUMA machines, respectively. Additionally, LG-RAM only incurs an extra CPU usage of no more than 7% on average when consolidating 32 VMs.

Consistently High MIMO Rates via Switched-Beam Antennas

The demand for wireless bandwidth is rising to unprecedented levels. The industry has responded with the inclusion of advanced PHY techniques, most notably multi-user (MU) MIMO, in the most recent Wi-Fi and LTE standards. However, despite the theoretical promise for large multiplexing gains, in practice the rate gains are modest due to a combination of large overhead to collect channel state information and not-so-well-conditioned channel matrices. In this paper, we propose to replace omni-directional antennas with inexpensive switched-beam antennas to produce well-conditioned channel matrices for MU-MIMO purposes with very low overhead. Remarkably, the experimental results with both software-defined radios and commercial Wi-Fi chipsets show that, when appropriate antenna modes are used, this leads to a $3.5\times-5\times $ average throughput improvement in indoor environments. What is more, our backward compatible protocol extension coupled with an efficient algorithm to select appropriate antenna modes, achieve the aforementioned gains with almost zero overhead.

Positioning of UAVs for throughput maximization in software-defined disaster area UAV communication networks

The throughput of a communication system depends on the data traffic load and the available capacity to support that load. In an unmanned aerial vehicle (UAV)-based communication system, the UAV position is one of the major factor affecting the capacity available to the flows (data sessions) being served. This paper proposes a centralized algorithm for positioning UAVs to maximize the throughput of a software-defined disaster area UAV communication network. The software-defined networking controller maintains upto date information about the network topology as well as the data rate demands and paths of the flows. The proposed algorithm utilizes this information to determine the positions for the UAVs such that the total throughput is maximized. The algorithm was simulated using MATLAB. The results showed that an average throughput improvement of 26% can be achieved by optimally positioning the UAVs.