Throughput Gain Research Articles

6LoWPAN Over Optical Wireless Communications for IPv6 Transport in Internet of Things Networks

As an emerging technology for the Internet of Things (IoT) wireless connectivity, there have been a lot of research and standardization activities on Visible Light Communications (VLC) and Optical Wireless Communications (OWC) using Light Emitting Diode (LED) lights. In the meantime, the Internet Protocol version 6 (IPv6) over Low Power Wireless Personal Area Network (6LoWPAN) has been discussed to provide the IPv6-based IoT services in wireless networks. However, the study on IoT systems using 6LoWPAN over OWC networks has not been made so far. This letter proposes a new architectural model to effectively use 6LoWPAN between IoT gateway and IoT device in the OWC-based IoT networks. The proposed model is easy to implement and provides the performance enhancement in OWC-based IoT networks, compared to the general IPv6 model. From testbed experimentations, it is shown that the proposed model provides the delay gain up to 5% and the throughput gain up to 19.52%, compared to the conventional IPv6 transport model.

Throughput Maximization for Backscatter- and Cache-Assisted Wireless Powered UAV Technology

This paper investigates a wireless powered unmanned aerial vehicle (UAV) communication network with backscatter and caching technologies. Specifically, we assume a self-energized UAV with a cache memory is deployed as a flying backscatter device (BD), term the UAV-enabled BD (UB), to relay the source’s signals to the destination. Whereas the source S can act as a wireless charging station or a base station to supply power or transmit information to the UB using the dynamic time splitting (DTS) method. The UAV utilizes its harvested energy for backscattering (i.e., passive communication) and transmit information (i.e., active communication) to the destination. In this context, we aim to maximize the total throughput by jointly optimizing the DTS ratio and the UB’s trajectory with caching capability at the UB. The formulation is troublesome to solve since it is a non-convex problem. To find solutions, we decompose the original problem into two sub-problems, whereas we first optimize the DTS ratio for a given UB’s trajectory and the UB’s trajectory optimization for a given DTS ratio. By using the KKT conditions, a closed-form expression for the optimal value of the DTS ratio is obtained, greatly reducing the computation time. Moreover, the solution of the second sub-problem can be acquired by adopting the successive convex approximation (SCA) technique. Consequently, an efficient alternating algorithm is proposed by leveraging the block coordinate descent (BCD) method. To show the advantages of the proposed BCD-based algorithm, we also provide the solution of the original problem applying the inner approximation (IA) method. Finally, the intensive numerical results demonstrate that our proposed schemes achieve significant throughput gain in comparison to the benchmark schemes.

Fair Downlink Communications for RIS-UAV Enabled Mobile Vehicles

This letter considers the sub-6GHz downlink communications from base station (BS) to mobile vehicles (MVs), where reconfigurable intelligent surface enabled unmanned aerial vehicle (RIS-UAV) serves as a mobile passive relay. To achieve communication fairness, we aim to maximize the minimum throughput among all the MVs via jointly optimizing RIS passive beamforming, MV scheduling, UAV trajectory and power allocation. The formulated problem is a mixed-integer non-convex optimization problem which is intractable to solve. Hence, the original problem is divided into three subproblems, joint passive beamforming and MV scheduling optimization, power allocation optimization, and trajectory optimization, which can be solved by the successive convex approximation (SCA) method. An alternating iterative optimization algorithm of the three subproblems is proposed to obtain the suboptimal solutions. Numerical results demonstrate the remarkable throughput gain of the proposed algorithm against other benchmarks.

Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks

A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order, power allocation coefficient vector and number of clusters, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the throughput gain of 35% can be achieved by invoking NOMA technique instead of orthogonal multiple access (OMA).

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

An RTS–CTS based medium access control protocol for full-duplex wireless local area networks

In-band full-duplex (IB-FD) wireless is a promising next generation communication technology which can double the spectral efficiency with respect to the conventional in-band half-duplex (IB-HD) radio. Hidden stations degrade the performances of any practical wireless networks, but unlike IB-HD networks, hidden station scenarios are different in IB-FD wireless networks. In this paper, we have presented a novel energy-efficient medium access control protocol exploiting the network allocation vector, whereby mitigating the hidden station problem in an IB-FD wireless network. Moreover, an analytical model has been devised to unveil the impact of the distance between a primary–secondary transceiver pair in order to select a secondary receiver to initiate a successful relay like IB-FD link, thereby diminishing the inter-station interference problem. In addition, we have shown that the number of IB-FD transmission link increases if the secondary transmitter uses adaptive transmission power. We have evaluated the performance of the proposed protocol through detailed simulations using a modified version of NS-3 in which the wireless module has been re-designed to accommodate the concept of IB-FD radio. Results show that our proposed protocol achieves at least 61% and 41% throughput gain and consumes 58% and 60% lower energy than IEEE 802.11 DCF RTS–CTS and FD-MAC protocols, respectively. Our proposed protocol outperforms the baseline protocols with respect to throughput, energy consumption, average re-transmission rate, average packet drop rate, etc. The proposed scheme can be very useful to IB-FD wireless networks which contain bandwidth-hungry applications.

Distributed resource allocation for D2D communications underlaying cellular network based on Stackelberg game

With the development of artificial intelligence, the large-scale access of intelligence equipment with complex heterogeneity will bring unpredictable spectrum limitation and complex interferences to traditional cellular networks. This situation can be alleviated by device-to-device (D2D) technique which improves spectrum efficiency by reusing cellular resources. In this paper, a resource allocation framework comprising channel allocation and power control based on Stackelberg game is proposed for distributed interference coordination between D2D and cellular communications with quality-of-service guarantee. First, base station matches the channel to D2D pairs on the basis of potential throughput gain. Second, interferences from D2D pairs to cellular users are converted into a penalty for D2D pairs through the interlayer price, and the optimization problem of system throughput is decoupled into multiple subproblems those can be solved in distributed and iterative manner in each D2D pair. Simulation results show that two proposed distributed algorithms of channel allocation and power control on interference coordination perform well in convergence and overall system throughput.

Optimal Throughput for Multicast over MANET-Satellite Networks

The convergence of MANET and satellite networks has been considered as one of the potential solutions to provide emergency communication and disaster relief services. However, unreliable MANET and satellite links may influence seriously to data transmission. In this paper, we study the application of packet-level coding for enhancing network throughput for multicast services over integrated MANET-satellite networks. First, we characterize the performance of multicast networks in terms of the probability of packet delivery under different packet-level codes. We then formulate and solve the coding optimization problem for typical values of computational resources at the network nodes given some target probability of successful delivery to the receivers. Finally, several simulation results show significant gains in the average achievable rate at the receivers with respect to routing for representative scenarios with different types of network devices given a strictly-high probability of successful delivery of the multicast networks. Furthermore, the devices with higher computational resources can obtain significantly better throughput gain.

Spectrum Allocation With Adaptive Sub-Band Bandwidth for Terahertz Communication Systems

We study spectrum allocation for terahertz (THz) band communication (THzCom) systems, while considering the frequency and distance-dependent nature of THz channels. Different from existing studies, we explore multi-band-based spectrum allocation with adaptive sub-band bandwidth (ASB) by allowing the spectrum of interest to be divided into sub-bands with unequal bandwidths. Also, we investigate the impact of sub-band assignment on multi-connectivity (MC) enabled THzCom systems, where users associate and communicate with multiple access points simultaneously. We formulate resource allocation problems, with the primary focus on spectrum allocation, to determine sub-band assignment, sub-band bandwidth, and optimal transmit power. Thereafter, we propose reasonable approximations and transformations, and develop iterative algorithms based on the successive convex approximation technique to analytically solve the formulated problems. Aided by numerical results, we show that by enabling and optimizing ASB, significantly higher throughput can be achieved as compared to adopting equal sub-band bandwidth, and this throughput gain is most profound when the power budget constraint is more stringent. We also show that our sub-band assignment strategy in MC-enabled THzCom systems outperforms the state-of-the-art sub-band assignment strategies and the performance gain is most profound when the spectrum with the lowest average molecular absorption coefficient is selected during spectrum allocation.

Cooperative Control of Physical Collision and Transmission Power for UAV Swarm: A Dual-Fields Enabled Approach

This article studies the collision avoidance and interference mitigation for unmanned aerial vehicle (UAV) swarm where many UAVs track a common target. The considered problem is formulated to jointly minimize the average interference and ensure the collision avoidance among UAVs. By exploiting the problem characteristics, our major contributions are summarized as follows. First, the singular case tolerance (SCT)-artificial potential field (APF) is proposed to overcome the failure of traditional APFs in collision avoidance, where the repulsive force gain coefficient among UAVs is dynamically controlled by the corresponding interferences. Second, the mean-field game (MFG) model is established to control communication power, where the instantaneous interferences among flying UAVs are represented by the mean-field approximation. Third, considering the tight coupling of trajectory and interference of UAVs, a cooperative control approach enabled by dual-fields is proposed to jointly adjust the trajectories and power of UAVs. Simulation results validate the significant performance improvement of the cooperative control approach enabled by dual-fields. Compared with separate APF and MFG, the proposed dual-field-cooperation approach can achieve about 117% throughput gain and 88% interference reduction when the UAV swarm is close to the target.

Interference-Aware Mobile Backscatter Communication: A PHY-Assisted Rate Adaptive Approach

Over the past decade, backscatter nodes have received booming interest for many emerging mobile applications, such as sports analytics and interactive gaming. However, backscatter networks are not ready to provide a high-throughput and stable communication platform for billions of such mobile nodes due to two main factors in rate adaptation. First, the common mapping paradigm that chooses the optimal rate based on RSSIs is hardly adaptable to hardware diversity. Second, the current probing processes are not optimized for mobile scenarios due to inefficient probing trigger, inaccurate channel estimation, and unique self-interference. To address those issues, we propose MobiRate, a mobility-aware rate adaptation link-layer that fully exploits the mobility hints from PHY information to deliver a high-throughput link-layer for mobile backscatter networks. The key insight is that mobility-hints can greatly benefit link-layer design, including rate selection and channel probing. Specifically, we introduce a novel velocity-based loss-rate estimation module, a mobility-assisted probing trigger, a selective probing module, and a robust self-interference detection module, significantly saving probing time and improving probing accuracy. As MobiRate is fully compatible with the current standard, we prototype it using COTS RFID readers and commercial tags. Our extensive experiments demonstrate that MobiRate can successfully identify self-interference with detection accuracy over 90% for tags of different velocities. Moreover, it achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

AI Empowered RIS-Assisted NOMA Networks: Deep Learning or Reinforcement Learning?

A reconfigurable intelligent surface (RIS)-assisted multi-user downlink communication system over fading channels is investigated, where both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes are employed. In particular, the time overhead for configuring the RIS reflective elements at the beginning of each fading channel is considered. The optimization goal is maximizing the effective throughput of the entire transmission period by jointly optimizing the phase shift of the RIS and the power allocation of the AP for each channel block. In an effort to solve the formulated problem and fill the research vacancy of the performance comparison between different machine learning tools in wireless networks, a deep learning (DL) approach and a reinforcement learning (RL) approach are proposed and their representative superiority and inferiority are investigated. The DL approach can locate the optimal phase shifts with the deep neural network fitting as well as the corresponding power allocation for each user. From the perspective of long-term reward, the phase shift control with configuration overhead can be regarded as a Markov decision process and the RL algorithm is proficient in solving such problems with the assistance of the Bellman equation. The numerical results indicate that: 1) From the perspective of the wireless network, NOMA can achieve a throughput gain of about 42% compared with OMA; 2) The well-trained RL and DL agents are able to achieve the same performance in Rician channel, while RL is superior in the Rayleigh channel; 3) The DL approach has lower complexity and faster convergence, while the RL approach has preferable strategy flexibility.

Architecting a Flash-Based Storage System for Low-Cost Inference of Extreme-Scale DNNs

The size of deep neural network (DNN) models has been exploding rapidly, demanding a colossal amount of memory capacity. For example, Google has recently scaled its Switch Transformer to have a parameter size of up to 6.4 TB. However, today's HBM DRAM-based memory system for GPUs and DNN accelerators is suboptimal for these extreme-scale DNNs as it fails to provide enough capacity while its massive bandwidth is poorly utilized. Thus, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Leviathan</i> , a DNN inference accelerator, which integrates a cost-effective <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flash-based</i> storage system, instead. We carefully architect the storage system to provide enough memory bandwidth while preventing performance drop caused by read disturbance errors. Our evaluation of Leviathan demonstrates an 8.3× throughput gain compared to the iso-FLOPS DNN accelerator with conventional SSDs and up to 19.5× higher memory cost-efficiency than the HBM-based DNN accelerator.

Network Communication Protocol Design from the Perspective of Batched Network Coding

Most existing network communication systems, such as the Internet, are built around the TCP/IP protocol stack. However, TCP/IP cannot effectively support the upcoming network communication systems with more wireless communication links and higher requirements in terms of throughput, latency, and reliability. In this article, we introduce a general network communication protocol design based on batched network coding (BNC), which includes the existing framework as a special case and whose design can be guided by channel coding theory. BNC-based protocols can have significant throughput gain for single-hop or multihop wireless communications and provide better trade-off among throughput, latency and reliability.

Scalable 3D Beam-Steering for Directional Millimeter Wave Wireless Networks

Multi-Gbps 60 GHz millimeter wave (mmWave) networks, are considered as the enabling technology for emerging applications such as untethered VR and 4K/8K Miracast. However, user motion, and even orientation change, can cause mis-alignment between mmWave transceivers’ directional beams and thus severe link outage. Within the practical 3D spaces, the combination of location and orientation dynamics leads to the exponential growth of beam searching complexity, which substantially exacerbates the outage. In this paper, we first measure the impact of 3D motion on 60 GHz link performance in the context of VR and Miracast applications. We find that 3D motion exhibits inherent non-predictability, so conventional beam steering solutions are no longer effective. Therefore, we propose a model-driven 3D beam-steering mechanism called Parallel Scanner (PSCAN), which can maintain high performance for mobile 60 GHz links. To enable PSCAN, we first discover and prove a hidden interaction between 3D beams and the spatial channel profile of 60 GHz radios. Leveraging on which, PSCAN strategically scans the 3D space to reduce the search latency by more than one order of magnitude. Experiment results based on a custom-built 60 GHz platform demonstrate PSCAN’s remarkable throughput gain, up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> , compared with the state-of-the-art.

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

Wi-Fi Channel Bonding: An All-Channel System and Experimental Study From Urban Hotspots to a Sold-Out Stadium

In this paper, we present WACA, the first system to simultaneously measure all 24 Wi-Fi channels that allow channel bonding at 5 GHz with microsecond scale granularity. With WACA, we perform a first-of-its-kind measurement study in areas including urban hotspots, residential neighborhoods, universities, and even a game in Futbol Club Barcelona’s Camp Nou, a sold-out stadium with 98,000 fans and 12,000 simultaneous Wi-Fi connections. We study channel bonding in this environment, and our experimental findings reveal the underpinning factors controlling throughput gain, including channel bonding policy and spectrum occupancy statistics. We then show the significance of the gathered dataset for finding insights, which would not be possible otherwise, given that simple channel occupancy models severely underestimate the available gains. Likewise, we characterize the risks of channel bonding due to other BSS’s, including their missed transmission opportunities and potential collisions due to imperfect sensing of bonded transmissions. We explore 802.11ax which imposes constraints on bonded channels to avoid fragmentation and defines different modes that can trade implementation complexity for throughput. Lastly, we show that the stadium, while seemingly too highly occupied for channel bonding gains, has transient gaps yielding impressive gains.

Throughput Maximization of Network-Coded and Multi-Level Cache-Enabled Heterogeneous Network

One of the paramount advantages of multi-level cache-enabled (MLCE) networks is pushing contents proximity to the network edge and proactively caching them at multiple transmitters (i.e., small base-stations (SBSs), unmanned aerial vehicles (UAVs), and cache-enabled device-to-device (CE-D2D) users). As such, the fronthaul congestion between a core network and a large number of transmitters is alleviated. We consider the throughput maximization problem that optimizes jointly the network-coded user scheduling and power allocation, subject to fronthaul capacity, transmit power, and NC constraints. Given the intractability of the problem, we decouple it into two separate subproblems. In the first subproblem, we consider the network-coded user scheduling problem for the given power allocation, while in the second subproblem, we use the NC resulting user schedule to optimize the power levels. We design an innovative two-layered rate-aware NC (RA-IDNC) graph to solve the first subproblem and solve the second subproblem using an iterative function evaluation (IFE) approach. Simulation results are presented to depict the throughput gain of the proposed approach over the existing solutions.

Analysis of FBMC Waveform for 5G Network Based Smart Hospitals

Nowadays, many prevalent frameworks for medical care have been projected, studied, and implemented. The load and challenges of traditional hospitals are increasing daily, leading to inefficient service in the health system. Smart hospitals based on advanced techniques play a crucial part in advancing the health services of rural people. It spares the time and money involved in travel, and patient medical reports can be shared instantly with the experts regardless of geographical constraints. Currently, the role of technology in hospitals is limited due to various restrictions, such as the obtainability of a high spectrum, low latency, and high-speed network. In this paper, we focused on the implementation of an advanced waveform with high spectral performance. Filer Bank Multi-Carrier (FBMC) is considered a strong contender for the upcoming 5G-centered smart hospitals due to its high data rate, no leakage of the spectrum, and less sensitivity to frequency error. In addition, a comparison of the spectral utilization of orthogonal frequency division multiplexing (OFDM) and FBMC in terms of bit error rate (BER), peak power (PP), power spectral density (PSD), noise-PSD, capacity and magnitude, and phase response is illustrated. Numerical results show that the FBMC achieved a throughput gain of 1 dB and its spectral performance is better than the OFDM; hence, it is a better choice for the proposed application compared to the current standard OFDM.

Efficient Load Balancing Algorithm in Long Term Evolution (LTE) Heterogeneous Network Based on Dynamic Cell Range Expansion Bias

The traditional scheme for load balancing in a homogeneous Long Term Evolution (LTE) Network where User Equipment (UEs) associate to a node with the strongest received signal strength is not practical for LTE Heterogeneous Network (LTE HetNet) due to power disparity between the nodes. Therefore, dynamic Cell Range Expansion (CRE) based load-balancing schemes were employed by several scholars to address the challenges in the LTE HetNet. However, the fairness index in achieving the desired average user throughput and UE offloading effect is relatively low. In this work, an efficient load-balancing algorithm for LTE HetNet based on dynamic Cell Range Expansion (CRE) was developed to improve the fairness of the network for the desired throughput and UE offloading effect. The simulation results achieve a throughput gain improvement of up to 11%, while the fairness index improves by 6% compared to the existing algorithm. Further, the UEs offloading effect shows a significant improvement of 3% relative to the existing algorithm.&#x0D; Keywords: Fairness Index; Cell Range Expansion; Load Balancing; LTE Heterogeneous Network; Throughput