Scarce Bandwidth Research Articles

Coordinating a growing data enterprise: strategies for facilitating multiple research-practice relationships for the public good.

It takes time, energy, and goodwill to start a data partnership. The corollary to this truism is that maintaining and expanding such partnerships will demand as much, if not more, of these resources. Learn about the strategies we are using to expand our ability to produce rigorous, policy-relevant evidence to combat poverty and inequality while staying committed to addressing the needs of our long-term partners. Developed by the University of Wisconsin-Madison’s Institute for Research on Poverty (IRP), the Wisconsin Administrative Data Core (WADC) evolved from a series of large-scale evaluation projects conducted for the state starting in the 1980s. This data enterprise, built by linking administrative data from multiple agencies, allows for cross-program analyses that are otherwise impossible. Increased turnover since the pandemic coupled with more formalized data governance structures has highlighted the importance of maintaining good relationships and providing value back to partners who devote scarce bandwidth to sharing data. At the same time, we are adding new agency partners and working to increase usage of the WADC. These changes multiply the complexity involved in coordinating the data enterprise. We are adapting to this new world by focusing on relationships: creating formal structures that deepen the network of direct relationships among agency staff and researchers, taking advantage of increased turnover by hiring staff who have worked in state agencies, and seeking agency partners who want to be active partners versus data providers. More solid relationships will yield more policy-relevant research that agencies can use to better serve the state.

A low-complexity ADMM-based secure beamforming for IRS-assisted uplink cognitive radio networks with NOMA

Intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) are both powerful means to improve the spectrum efficiency and can assist the cognitive radio network (CRN) in achieving more effective spectrum allocation. However, spectrum sharing in CRNs and the utilization of the scarce bandwidth in NOMA make it vulnerable to security threats. The secure beamforming design in IRS-assisted CRNs with NOMA is complicated and non-convex. Traditional semidefinite relaxation along with Gaussian randomization (SDR-GR) algorithm can solve the non-convex problem, however a high computational complexity. This paper proposes an alternating direction method of multipliers (ADMM)-based sum secrecy rate maximization (A-SSRM) scheme to achieve secure transmission with low computation complexity in IRS-assisted uplink CRNs with NOMA. Specifically, we derive an appropriate transformation of the original problem based on the ADMM framework and decompose it into multiple sub-problems, which are carefully designed to have closed-form solutions. Furthermore, an eavesdropper zero forcing (EZF)-based sub-optimal scheme is presented. Simulation results reveal that our proposed A-SSRM scheme outperforms other benchmarks in terms of sum secrecy rate and reduces the average running time by 6 times compared with the traditional SDR-GR algorithm. This is attributed to our effective decoupling and decomposing of the original problem, which enables each sub-problem to obtain a simple closed-form solution through derivation.

Dynamic Traffic Grooming Based on Virtualization-Plane-Aided Optimization for Elastic Optical Satellite Networks

With the increase in global wireless traffic, the use of large-scale satellite networking to provide ubiquitous access is one of the essential trends of future 6G network development. Elastic optical satellite networks (EOSNs) are widely considered a flexible solution for future satellite communication. However, with the continuous proliferation of network devices and users, the growing disparity between user demands and the limited bandwidth and capacity of the network is becoming increasingly noticeable. This has led to issues such as constrained network resource utilization and resource fragmentation. Therefore, EOSNs must efficiently address the challenge of allocating scarce bandwidth resources. Effective traffic grooming methods will be applied to EOSNs to solve the problem of bandwidth shortage. This paper proposed a dynamic traffic grooming algorithm based on virtualization-plane-aided optimization (DTG-VPO) to facilitate the bandwidth allocation for EOSNs. Firstly, the nodes of the alternative paths were graded, and the weights of the subsequent hop links were modified. Then, the path was evaluated using link weights, alternative paths were selected in the virtual and physical topologies, respectively, and a path set was constructed. Finally, a resource block evaluation parameter was designed to quantify the quality of candidate resource blocks and rank them. A series of simulations have evaluated the traffic-blocking probability and wavelength utilization under different traffic loads. The link resource was more fully utilized compared with other traffic grooming algorithms. The blocking probability can be reduced by 75%, while wavelength utilization can be improved by 8.1%.

Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity

Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

It has become increasingly apparent that bandwidth scarcity is an issue as wireless communications advance. Alternatively, spectrum sensing techniques are used to detect licensed users. A spectrum sensor can detect energy, matched filters, and cyclostationary features. There are, however, some drawbacks to these methods. Energy detector performance is affected by noise power uncertainty. Every primary user needs a dedicated receiver for matched filter spectrum sensing. Computational effort and observation time are required for cyclo-stationary feature detection. Spectrum use is determined using particle swarm optimization (PSO), an algorithm for determining the best frequency allocation and highest accuracy. Using PSO operations, this paper proposes an improved energy detection method compared to conventional energy detection methods. Detecting energy and using the PSO channel allocation technique to detect fading channels is also mathematically described.

Boosting Erasure-Coded Multi-Stripe Repair in Rack Architecture and Heterogeneous Clusters: Design and Analysis

Large-scale distributed storage systems have introduced erasure codes to guarantee high data reliability, yet inevitably at the expense of high repair costs. In practice, storage nodes are usually divided into different racks, and data blocks in storage nodes are often organized into multiple stripes independently manipulated by erasure code. Due to the scarcity and heterogeneity of the cross-rack bandwidth, the cross-rack network transmission dominates the entire repair costs. We argue that when erasure code is deployed in a rack architecture, existing repair techniques are limited in different aspects: neglecting the heterogeneous cross-rack bandwidth, less consideration for multi-stripe failure, and no special treatment on repair link scheduling. In this paper, we present CMRepair, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cross-rack Multi-stripe Repair</i> technique that aims to reduce the repair time for multi-stripes failure repair in heterogeneous erasure-coded clusters. CMRepair first carefully chooses the nodes for reading/repairing blocks and searches for the multi-stripe repair solution. It adopts different algorithms to adjust per stripe solution, including the Computation Time Priority (CTP) algorithm based on the greedy idea and the Repair Time Priority (RTP) algorithm based on the meta-heuristics idea. Furthermore, CMRepair selectively schedules the execution orders of cross-rack links, with the primary objective of saturating the unused upload/download bandwidth resources and avoiding network congestion. The experiments show that CMRepair with the CTP algorithm can reduce 27.59%-58.12% of the repair time while only introducing negligible computation overhead, and CMRepair with the RTP algorithm can reduce 33.52%-97.75% of the repair time in an acceptable computation time, over existing repair techniques.

Masked360: Enabling Robust 360-degree Video Streaming with Ultra Low Bandwidth Consumption.

360-degree video streaming has gained tremendous growth over the past years. However, the delivery of 360-degree videos over the Internet still suffers from the scarcity of network bandwidth and adverse network conditions (e.g., packet loss, delay). In this paper, we propose a practical neural-enhanced 360-degree video streaming framework called Masked360, which can significantly reduce bandwidth consumption and achieve robustness against packet loss. In Masked360, instead of transmitting the complete video frame, the video server only transmits a masked low-resolution version of each video frame to reduce bandwidth significantly. When delivering masked video frames, the video server also sends a lightweight neural network model called MaskedEncoder to clients. Upon receiving masked frames, the client can reconstruct the original 360-degree video frames and start playback. To further improve the quality of video streaming, we also propose a set of optimization techniques, such as complexity-based patch selection, quarter masking strategy, redundant patch transmission and enhanced model training methods. In addition to bandwidth savings, Masked360 is also robust to packet loss during the transmission, because packet losses can be concealed by the reconstruction operation performed by the MaskedEncoder. Finally, we implement the whole Masked360 framework and evaluate its performance using real datasets. The experimental results show that Masked360 can achieve 4K 360-degree video streaming with bandwidth as low as 2.4 Mbps. Besides, video quality of Masked360 is also improved significantly, with an improvement of 5.24-16.61% in terms of PSNR and 4.74-16.15% in terms of SSIM compared to other baselines.

Mobility aware resource allocation for millimeter-wave D2D communications in presence of obstacles

The device-to-device (D2D) communication is envisioned as the solution to the bandwidth scarcity problem in the era of exponentially growing smart handheld devices. Although D2D communication in high frequency millimeter wave (mmWave) band offers high data rate, it requires obstacle free line-of-sight (LOS) communication path due to high propagation and penetration losses. The relay selection problem in D2D communication deals with careful selection of relay devices in order to establish a communication link when the direct LOS path is blocked or suffers from high interference from neighboring devices. With the limited number of available frequency channels, the channel allocation problem aims to judicially allocate the frequency channels to the activated D2D communication links. Due to the interdependence of the aforesaid two problems, it is a challenging task to optimally allocate frequency channels to the requesting D2D pairs along with proper selection of relay devices, specially in the presence of obstacles and user mobility. In this paper, we have proposed a mobility and obstacle aware base-station controlled centralized framework which jointly deals the channel allocation and relay selection problems for mmWave D2D communication. After proving the hardness of this joint problem, we provide a greedy solution along with its approximation bound. Our proposed algorithm has been shown to outperform an existing work through extensive simulations.

Scalable Secret Key Generation for Wireless Sensor Networks

Future sensor networks require energy and bandwidth efficient designs to support the growing number of nodes. The security aspect is often neglected due to the extra computational burden imposed on the sensor nodes. In this article, we propose a secret key generation method for wireless sensor networks by using the physical layer features. This key generation method is based on the superposition property of wireless channels. The proposed method exploits the multiple access property of the wireless channel with simultaneous transmissions as in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">analog function computation</i> technique to solve the latency and scarce bandwidth problems of highly populated dense networks. All nodes use the same time and frequency block to provide scalability that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linearly proportional</i> to the number of nodes. The proposed method also benefits from the network density to provide security against eavesdroppers that aim to sniff the secret key from the channel. The security of the proposed method against eavesdroppers is analytically studied. Moreover, their application in multiple layers is investigated. The presented results have shown that there is a tradeoff between the total power consumption and total used bandwidth for secret key generation. Lastly, the error probability of the generated keys due to thermal noise and channel estimation error is investigated with computer simulations and compared with broadcasting-based benchmark model.

OCHSA: Designing Energy-Efficient Lifetime-Aware Leisure Degree Adaptive Routing Protocol with Optimal Cluster Head Selection for 5G Communication Network Disaster Management

As an underlayment to cellular 5G communication network, device-to-device (D2D) communications will not only boost capacity utilization and power efficiency but also provide public health and public safety services. One of the most important requirements for these businesses is to have alternate access to cellular networks in the event that they are partially or completely disrupted as a result of a natural disaster. Despite limited communication coverage and bandwidth scarcity, the 3rd Generation Partnership Project (3GPP) must have developed a new device-to-device (D2D) communication method fundamental enhanced mobile that can strengthen spectral efficiencies besides allowing direct communication of gadgets in close propinquity devoid of transitory by elevated-node B (eNB). Unfortunately, enabling data transmission on a cellular connection offers a challenge in terms of two-way radio source administration, because D2D associates recycle cellular users’ uplink radio resources, which might create interference to D2D user equipment’s (DUE) receiving channels. In this study, we concentrate on optimal cluster head selection using the binary flower pollination optimization algorithm by designing an energy-efficient lifetime-aware leisure degree adaptive routing protocol named OptCH_L-LDAR. This topology is constructed with a multi-hop obliging communication system, instructed on the way to wrap an extensive remoteness connecting source and destination. The proposed OptCH_L-LDAR is compared with three state-of-art methods such as binary flower pollination (BFP) algorithm, time division multiple access (TDMA), and data-driven technique (DDT). As a result, the proposed OptCH_L-LDAR achieves 96% of energy efficiency, 89% of lifetime, 97% of outage probability, and 98% of spectral efficiency.

Communication Systems Performance at mm and THz as a Function of a Rain Rate Probability Density Function Model.

6G is already being planned and will employ much higher frequencies, leading to a revolutionary era in communication between people as well as things. It is well known that weather, especially rain, can cause increased attenuation of signal transmission for higher frequencies. The standard methods for evaluating the effect of rain on symbol error rate are based on long-term averaging. These methods are inaccurate, which results in an inefficient system design. This is critical regarding bandwidth scarcity and energy consumption and requires a more significant margin of effort to cope with the imprecision. Recently, we have developed a new and more precise method for calculating communication system performance in case of rain, using the probability density function of rain rate. For high rain rate (above 10 mm/h), for a typical set of parameters, our method shows the symbol error rate in this range to be higher by orders of magnitude than that found by ITU standard methods. Our model also indicates that sensing and measuring the rain rate probability is important in order to provide the required bit error rate to the users. This will enable the design of more efficient systems, enabling design of an adaptive system that will adjust itself to rain conditions in such a way that performance will be improved. To the best knowledge of the authors, this novel analysis is unique. It can constitute a more efficient performance metric for the new era of 6G communication and prevent disruption due to incorrect system design.

Biologically inspired underwater acoustic communication based on discrete cosine transform

Reliable underwater communication is a challenging problem due to the scarce bandwidth and spreading loss. In the presence of eavesdroppers, it adds a further complexity as the secure end to end communication is also necessitated. In this context, we propose a biologically inspired, watermark technology based underwater acoustic communication method using discrete cosine transform (DCT) of the cetacean sounds. The information-modulated pseudo-noise sequence is embedded into the select DCT frequency points of the broadband cetacean sound with a certain weight. The final transmitted signal is obtained by inverse discrete cosine transform after information modulation. At the receiver end, the synchronization is achieved through the cross-correlation between the received watermarked signal and the original cetacean sound. After extracting the selected DCT frequency points of each received sound sub-unit, the demodulation is conducted by the sequence correlation based on the local library. Similarity between the watermarked signal and the original cetacean sound is evaluated through the waveform correlation coefficient and PESQ score. Simulation and lake experiment verified the feasibility of the proposed biologically inspired communication method. A reliable communication rate of 54.92 bps was achieved at a distance of 1.22 km with the mean waveform correlation coefficient and PESQ score over 0.85 and 3.6 respectively in the lake trial and the bit error rate is lower than 10-3.

Cross-Layer Design Based N-ary Huffman Coding for Performance Analysis of DSDV Routing Protocol in MANETs

The implementation of the optimal Huffman coding technique for achieving proficient data compression, lower information redundancy and minimal utilization of the scarce bandwidth is essential to handle the effective processing of massive network data. In this paper, we employ cross-layer design technique among the data link, network and presentation layers of the traditional protocol stack to develop the improved DSDV routing protocol based on n-ary Huffman coding procedure. The entropy-based adaptive prefix codewords with variable length are assigned to the probability of packet successful delivery across the ad-hoc wireless network. Several coding and network efficiency parameters are deployed to assess the performance of the proposed routing scheme under three distinctive IPv4 network scenarios. These investigated network protocols include the default IPv4, multi IPv4 and dynamic IPv4 routing methodologies. Diversified simulation settings are employed with deviating network size to measure the multitude of essential wireless network characteristics incorporating the average delay, packet error rate, packet delivery fraction, data rate, ping loss rate, entropy rate, and reception cache hit. In addition, the set of key data compression/coding indicators are examined through comprehensive numerical analysis such as codeword length, source information rate, mean redundancy, and coding efficiency. Beyond, we significantly compare the performance of our developed cross-layer coded mobile routing model with several previous algorithms to validate its enhanced feasibility and superiority in terms of crucial network operation metrics such as throughput, packet drop rate, mean delay and packet delivery ratio.

Multi-Agent Reinforcement Learning With Measured Difference Reward for Multi-Association in Ultra-Dense mmWave Network

Millimeter Wave (mmWave) communication technology is anticipated to play a vital role in meeting the growing demand for the scarce bandwidth in wireless communications. However, mmWave networks are highly susceptible to blockage. Thus, some mitigation techniques, such as multi-connectivity, need to be considered. Densely deploying mmWave base stations (mBSs) to form an ultra-dense network (UDN) also helps. With a mix of different technologies, optimally allocating resources becomes challenging. In this paper, we study mmWave user multi-association in a two-tier heterogeneous ultra-dense network (HetUDN) with a relatively large number of user equipments (UEs). We propose a framework of multi-agent reinforcement learning (MARL) to tackle the complicated optimization problem, leveraging its adaptivity to the communication environment. The proposed scheme considers mmWave beam-division based multi-connectivity and takes advantage of a macro base station (MBS) for indirect cooperation among agents (UEs). In particular, we borrow a credit-assignment technique called difference reward (DR) to deal with a relatively large MARL system with a large action space, which, to the best of our knowledge, is the first time to apply MARL with DR in user association. Furthermore, the proposed schemes are scalable mainly due to fixed observation dimensions and individual actions taken by UEs independently, ensuring that the operation is independent of the numbers of mBSs and UEs. Numerical results suggest that the two MARL schemes with measured DR could achieve a good balance between energy efficiency and QoS outage, and the one using extended DR (EDR) offers additional performance improvement.

Next-Generation OTT Distribution Architecture Supporting Multicast-Assisted ABR (mABR) and HTTP/3 Over QUIC

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The potential audience for streaming media services is massive, but satisfying that kind of demand poses a huge technical challenge. Even if existing content delivery networks (CDNs) were capable of distributing such loads, the costs at that scale would remain beyond the monetary capabilities of any broadcaster. By reducing transmission costs associated with content delivery at scale, broadcasters would pay less to the CDNs, making the content cheaper to the end user. In traditional broadcasting technologies, a majority of commercially available content distribution architectures are based on a paradigm rooted in linear broadcast services, having a very basic media service composition, solely focusing on the delivery of content interpretable on a vast amount of compatible TV sets with built-in tuners.While this helped transform the industry by facilitating the transition from analogue to digital broadcast content distribution, it now leaves content providers and broadcast network operators wondering how they can remain relevant in the age of web and internet streaming. This article will take its point of departure in evolved content distribution systems and the resulting system architectures for next-generation broadcast over-the-top (OTT) live and on-demand streaming. It will focus on a solution for efficient and secure transport of well-known low-latency streaming protocols by employing QUIC as a primary transport protocol for the industry leading IP multicast architectures supporting multicast-assisted adaptive bitrate (mABR) streaming, enabled by novel HTTP/3 server push techniques. At scale, the solution could significantly reduce the amount of open streaming sessions, improving the scalability of existing audio–video streaming architectures, and reducing the wastage of scarce bandwidth. Placing physical gateway boxes within users’ home network that fully support QUIC and multicast content reception can reduce the prevalent lack of user agent support for reception of multicast QUIC content. This new model with the core transport protocol based on QUIC will be the foundation for unified content distribution architectures, spanning internet streaming, IPTV, 5G, and the latest ATSC and DVB physical layers, allowing for an efficient, IP-based delivery of linear content to thin clients.</i>

A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

&lt;span lang="EN-US"&gt;Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, &lt;/span&gt;&lt;span lang="EN-US"&gt;multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.&lt;/span&gt;

Random Access Using Deep Reinforcement Learning in Dense Mobile Networks.

5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access is considered a challenging function in which users attempt to select an efficient TRxP by random access within a given time. Ideally, an efficient TRxP is less congested, minimizing delays in users’ random access. However, owing to the nature of random access, it is not feasible to deploy a centralized controller estimating the congestion level of each cell and deliver this information back to users during random access. To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. The proposed scheme estimates congestion of TRxPs in service and selects the optimal access point. Mathematically, this approach is beneficial in approximating and minimizing a random access delay function. Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. Notably, the average access delay is improved by 58.89% from the original 3GPP algorithm, and the probability of successful access also improved.

Design of utility functions for game-based channel allocation in cognitive radio wireless sensor network

Cognitive radio enabled wireless sensor network is capable of reducing the spectrum scarcity problem of the wireless networks. Looking at the scarcity of available bandwidth, and the high growth in the number of communication devices in recent times, cognitive radio technology has proven to be a promising technology for the days to come. The application of Game Theory in cognitive radio networks has been visible in recent research works. However, only limited literature is available in which possibilities of applying the game-theory based approaches for the challenging task of channel assignment in cognitive radio wireless sensor are available in the literature. It is understood that the crux of the solution to the problem of scheming games for allocation of the channel is centered on the selection of the utility function in order to increase the efficiency of the channel allocation algorithm. Accordingly, the study regarding the influence of several utility functions on the performance of the corresponding channel allocation algorithm is important. Such a study enables designers to arrive at the optimal utility function to be used in game-theory based channel allocation algorithms, and the same is explored to the best extent, in this paper. The detailed procedure of allocating channels to all the contending nodes through game-based channel allocation has been discussed in this paper. Moreover, the performance of six different utility functions proposed which can be used for channel allocation using game theory has been evaluated for respective performances through MATLAB-based simulations.

Implementation and Performance Analysis of Cognitive Radio with Frequency Updating Algorithm on Software-defined Radio Platform

Wireless communication is being used in all communication standards. However, with each passing day, the bandwidth scarcity has become a significant concern for the upcoming wireless technologies. In order to address this concern, various techniques based on artificial intelligence have been designed. The basic intelligent radio called cognitive radio has been devised. It works on the basic principle of spectrum sensing and detecting the free frequency for transmission of the secondary user, who is an unlicensed user. This work proposes an efficient technique that has been developed to design cognitive radio based on SDR platform. The frequency updating algorithm has been added for the performance assessment of the proposed technique. The analysis posits that for every 10dB rise in Gaussian Noise, the bit error rate of secondary transmitter and spectrum sensor, cause an increment of 19.59% and 29.39%, respectively. It has been found that spectrum sensor is more prone to noise and that the Gaussian noise degrades the performance of the system. Therefore, it is pertinent that the spectrum sensor should be programmed carefully. This analysis shows that the best range of spectrum sensor under Gaussian noise is 0 to 0.1dB and the bit error rate is within this specified range.

Monitoring in fog computing: state-of-the-art and research challenges

Fog computing has rapidly become a widely accepted computing paradigm to mitigate cloud computing-based infrastructure limitations such as scarcity of bandwidth, large latency, security, and privac...