Bad Channel Conditions Research Articles

Deep Learning Models For Symbol Detection in UFMC Systems

The limited availability of the frequency band in wireless communication systems is one of the major obstacles to achieving high-speed data transmission. To overcome this obstacle, multicarrier systems, which utilize the available frequency bandwidth most efficiently to ensure spectral efficiency and consequently high data rate transmission, are used. In the Universal Filtered Multi-Carrier (UFMC) technique, which is one of the multi-carrier systems, in addition to high-speed data transmission, the bandwidth is divided into many sub-bands and only the lower sidebands are filtered, and as a result, the inter-channel interference problem is minimized. However, in UFMC systems, the error-free reception of symbols at the receiver is directly dependent on the performance of the symbol detection algorithm. In this study, symbol detection was performed in UFMC systems by taking advantage of the learning ability of deep learning methods, providing flexible solutions in solving nonlinear problems, reducing the hardware load by using fewer parameters and the ability to perform parallel processing, and thus the symbol detection performance of the system under bad channel conditions was increased.

Optimizing Performance of Worst Case User in Ultra-dense Networks Utilizing Deep Q-learning

Abstract: This paper defines, analyzes, and improves the performance of the worst-case user in ultradense networks Background: In Ultra-Dense Networks (UDNs), where the Base Stations are distributed with a very high density, the users are possibly near the cells’ intersection. These users are called the Worst-Case Users (WCU) and usually experience very low performance. Objectives: Thus, improving the WCU performance is an urgent problem to secure the service requirement of future cellular networks. Methods: In this paper, the performance of the WCU is analyzed in UDNs with a maximum power algorithm and under the wireless environment with Stretched Path Loss model and Rayleigh fading. To improve the WCU data rate, the Deep Q Networks with and without Multi-Input-Multi-output (MIMO) are utilized in this paper. Results: The simulation results show that a system–based Deep Q Learning can dramatically improve the WCU performance compared to the system with the maximum power algorithm. In addition, the deployment of the MIMO technique in a system–based Deep Q-learning only has benefits in bad channel conditions. Conclusion: In any channel condition, utilization of Deep Q Learning is a suitable solution to improve the WCU performance. Furthermore, if the user experiences a good channel condition, the MIMO technique can be used with Deep Q Learning to obtain further performance improvement.

Improving user performance in cooperative NOMA millimeter wave networks under two-phase operation protocol

Improving user performance is the most important target of 5G and beyond 5G millimeter wave cellular network systems. Thus, cooperative communication with assistance from additional hardware has been introduced as the potential solution. The paper proposes a two-phase cooperative communication system without utilizing the additional hardware to assist the users with bad channel conditions. Particularly, each Cell-Edge User (CEU) is served by both the nearest and second nearest BSs where the former is the source and the latter works as the relay. In addition, the Non-Orthogonal Multiple Access (NOMA) technique is used to allocate the radio resource and power for the relays. To capture the performance behavior of the proposed system, the millimeter wave properties, which are modeled by Nakagami-m and the Stretch Path Loss, are used to derive the performance metrics of Cell-Center User (CCU), CEU, and the typical user. Through analytical results which are validated by Monte Carlo simulation, it is proved that the proposed system can provide a coverage probability of 0.93 (m = 2) which is 13.4% higher than the system without cooperative communication. Moreover, the proposed system can achieve an 8% higher data rate but consume an 16.7% power less than the system with full cooperative communication.

Energy-Efficient Backscatter-Assisted Coded Cooperative NOMA for B5G Wireless Communications

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the transmit power of the source, power allocation coefficients (PAC), and power of the relay node under imperfect successive interference cancellation (SIC) decoding. A three-stage low-complexity energy-efficient alternating optimization algorithm is introduced which optimizes the transmit power, PAC, and relay power by considering the quality of service (QoS), power budget, and cooperation constraints. Subsequently, a joint channel coding framework is introduced to enhance the performance of far user which has no direct communication link with the base station (BS) and has bad channel conditions. In the destination node, the far user data is jointly decoded using a Sum-product algorithm (SPA) based joint iterative decoder realized by jointly-designed Quasi-cyclic Low-density parity-check (QC-LDPC) codes. Simulation results evince that the proposed backscatter-enabled cooperative NOMA system outperforms its counterpart by providing an efficient performance in terms of energy efficiency. Also, proposed jointly-designed QC-LDPC codes provide an excellent bit-error-rate (BER) performance by jointly decoding the far user data for considered BSC cooperative NOMA system with only a few decoding iterations.

Receiver Design With an Adjustable Energy-Signal-Quality Tradeoff for IoT Networks

The energy efficiency of an Internet of Things (IoT) receiver can be improved by introducing an adjustable tradeoff between signal quality and energy consumption. In good channel conditions, the receiver can be set to consume less energy per bit, without compromising signal quality in bad channel conditions. We propose a system-level receiver design that enables adequate configuration and combination of signal quality and energy tradeoffs in multiple receiver components. Co-design of all components is essential. We identify the most energy-efficient configurations in our system-level design under different channel conditions. With those configurations, the proposed receiver outperforms a state-of-the-art adjustable receiver with only an adjustable analog front end by several tens of percent in energy per successfully received bit and by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> in energy-sensitivity configuration range. To show the efficacy of the proposed approach, we integrate a model of the proposed design into the OMNeT++ simulator and show the benefits on an environmental monitoring scenario. In this scenario, we report 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">$6\times $ </tex-math></inline-formula> energy savings for the entire transceiver compared to the conventional transceiver design without adjustable receiver.

Analysis and Design of Enhanced Distributed Fountain Codes in Multiple Access Networks with Cooperative Relay

Distributed fountain coding plays an important role in rateless code research. The reliability and effectiveness of these coding schemes are increasingly challenged with the growing applications. In this paper, a novel multiple-access network with cooperative relay is presented, and a novel enhanced distributed fountain coding scheme for this network is proposed. The overall degree distributions are derived, and the asymptotic decoding performance is analyzed theoretically by employing the And-Or tree method. On this basis, a design method using joint iterative optimization algorithms is proposed to optimize the degree distributions of the sources and relays. Simulation results show that the proposed enhanced distributed fountain codes outperform the existing generalized distributed fountain codes (GDFC) and have a good performance on both lossless and lossy channels. It reveals that the proposed codes can provide unequal error protection (UEP) property for different sources by introducing the extra cooperative relay. The performance improvement is not restricted to the sources connected to the cooperative relay but applies to all sources. With the additional relay, the proposed codes are able to overcome the effects of bad channel conditions caused by terrain, obstacles, and so on, to avoid communication interruptions and improve the reliability of the network.

Reconfigurable Intelligent Surface for Physical Layer Key Generation: Constructive or Destructive?

Physical layer key generation (PKG) is a promising means to provide on-the-fly shared secret keys by exploiting the intrinsic randomness of the radio channel. However, the performance of PKG is highly dependent on the propagation environments. Due to its feature of controlling the wireless environment, reconfigurable intelligent surface (RIS) is appealing to be applied in PKG. In this article, in contrast to the existing literature, we investigate both the constructive and destructive effects of RIS on the PKG scheme. For the constructive aspect, we have identified static and wave-blockage environments as two RIS-empowered-PKG applications in future wireless systems. In particular, our experimental results in a static environment showed that RIS can enhance the entropy of the secret key, achieving a key generation rate (KGR) of 97.39 bit/s with a bit disagreement rate (BDR) of 0.083. In multi-user systems where some remote users are in worse channel conditions, the proposed RIS-assisted PKG algorithm improves the sum secret key rate by more than 2 dB, compared to the literature. Furthermore, we point out that RIS could be utilized by an attacker to perform new jamming and leakage attacks and give countermeasures, respectively. Finally, we outline future research directions for PKG systems in light of the RIS.

Intelligent Computation Offloading for MEC-Based Cooperative Vehicle Infrastructure System: A Deep Reinforcement Learning Approach

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy.

Enhancing Secrecy Performance of Cooperative NOMA-Based IoT Networks via Multiantenna-Aided Artificial Noise

With the increasing demand for security in many sectors, such as defense and health systems, developing secure Internet of Things (IoT) networks is a matter of great urgency. Looking at a potential solution for secure IoT systems, we investigate the physical layer security of cooperative nonorthogonal multiple access (NOMA) systems. After decoding information signal, the idea that a strong IoT node can serve as a relay node for other weak IoT nodes in enhancing their signal reception reliability, is known as cooperative NOMA. We consider both single-antenna and multiantenna aided transmission scenarios, where the base station (BS) communicates with two IoT nodes of different strengths. In the multiantenna scenario, artificial noise (AN) is generated at the BS and the strong IoT node for improving the security of the system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both the IoT nodes under both the single-antenna and multiantenna aided scenarios. For the single-antenna scenario, we show that the power optimization at the BS and the strong IoT node can enhance the secrecy performance to some extent. For this case, we further study the secrecy diversity order of the overall system, which is mainly determined by the IoT node with the worse channel condition. For the multiantenna scenario, we derive the asymptotic secrecy outage probability (SOP) when the number of antennas tends to infinity. Extensive simulations have been conducted to verify the accuracy and effectiveness of the proposed analytical derivations. The presented results verify that the security performance of the cooperative NOMA-based IoT network can be improved through an appropriate power control scheme and by generating AN at the BS and the strong IoT node. The simulation results further illustrate that the asymptotic SOP is close to the exact one.

Dynamic Asynchronous Anti Poisoning Federated Deep Learning with Blockchain-Based Reputation-Aware Solutions.

As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous anti poisoning federated deep learning framework to pursue both efficiency and security. This paper proposes a lightweight dynamic asynchronous algorithm considering the averaging frequency control and parameter selection for federated learning to speed up model averaging and improve efficiency, which enables federated learning to adaptively remove the stragglers with low computing power, bad channel conditions, or anomalous parameters. In addition, a novel local reliability mutual evaluation mechanism is presented to enhance the security of poisoning attacks, which enables federated learning to detect the anomalous parameter of poisoning attacks and adjust the weight proportion of in model aggregation based on evaluation score. The experiment results on three datasets illustrate that our design can reduce the training time by 30% and is robust to the representative poisoning attacks significantly, confirming the applicability of our scheme.

Uplink performance of nonorthogonal multiple access ultradense networks with power control

SummaryThis paper studies power control in ultradense networks which are potential network models of 5G cellular systems. The power control model that bases on stretched path loss model (SPLM) is introduced to allow each user to proactively adjust its transmission power to adapt to power loss and network status. The moment‐generating function (MGF) is utilized through the paper to analyze the uplink coverage probability (UCP) in which it is assumed that the base station (BS) of interest does not have information about fast fading of interfering channels. The analytical results indicate that the proposed power control model can reduce user power consumption while maintaining the UCP. Two interesting facts have been stated when comparing the performance of the networks in good and bad channel conditions. First, the user equipment (UE) in bad channel conditions outperforms others in good channel conditions. Second, the deployment of nonorthogonal multiple access (NOMA) technique can significantly improve the area spectral efficiency (ASE) in bad channel conditions.

Joint Model Pruning and Device Selection for Communication-Efficient Federated Edge Learning

In recent years, wireless <i>federated learning</i> (FL) has been proposed to support the mobile intelligent applications over the wireless network, which protects the data privacy and security by exchanging the parameter between mobile devices and the <i>base station</i> (BS). However, the learning latency increases with the neural network scale due to the limited local computing power and communication bandwidth. To tackle this issue, we introduce model pruning for wireless FL to reduce the neural network scale. Device selection is also considered to further improve the learning performance. By removing the stragglers with low computing power or bad channel condition, the model aggregation loss caused by model pruning can be alleviated and the communication overhead can be effectively reduced. We analyze the convergence rate and learning latency of the proposed model pruning method and formulate an optimization problem to maximize the convergence rate under the given learning latency budget via jointly optimizing the pruning ratio, device selection, and wireless resource allocation. By solving the problem, the closed-form solutions of pruning ratio and wireless resource allocation are derived and the threshold-based device selection strategy is developed. Finally, extensive experiments are carried out to demonstrate that the proposed model pruning algorithm outperforms other existing schemes.

Power Domain Multiplexing Waveform for 5G Wireless Networks

Power domain non-orthogonal multiple access combined with a universal filtered multi-carrier (NOMA-UFMC) has the potential to cope with fifth generation (5G) unprecedented challenges. NOMA employs power-domain multiplexing to support several users, whereas UFMC is robust to timing and frequency misalignments. Unfortunately, NOMA-UFMC waveform has a high peak-to-average power (PAPR) issue that creates a negative affect due to multicarrier modulations, rendering it is inefficient for the impending 5G mobile and wireless networks. Therefore, this article seeks to presents a discrete Hartley transform (DHT) pre-coding-based NOMA enabled universal filter multicarrier (UFMC) (DHT-NOMA-UFMC) waveform design for lowering the high PAPR. Additionally, DHT precoding also takes frequency advantage variations in the multipath wireless channel to get significant bit error rate (BER) gain. In the recommended arrangement, the throughput of the system is improved by multiplexing the users in the power domain and permitting the users with good and bad channel conditions to concurrently access the apportioned resources. The simulation outcomes divulge that the projected algorithm accomplished a gain of 5.8 dB as related to the conventional framework. Hence, it is established that the proposed DHT-NOMA-UFMC outperforms the existing NOMA-UFMC waveform. The key benefit of the proposed method over the other waveforms proposed for 5G is content gain due to the power domain multiplexing at the transmitting side. Thus, a huge count of mobile devices could be supported under specific restrictions. DHT-NOMA-UFMC can be regarded as the most effective applications for 5G Mobile and Wireless Networks. However, the main drawback of the proposed method is that the Fourier peak and phase signal is not easily estimated.

Noise Reduction Method for Low Voltage Power Line Carrier Communication

In order to improve the performance of electricity information collection system, this paper studies and proposes a new method of low-voltage power line carrier signal frequency domain noise reduction. It includes frequency domain decomposition of carrier signal, filtering noise reduction and signal frequency domain restoration. Then compared our method with the wavelet packet noise reduction method based on multi-resolution. The experimental results show that the proposed algorithm is better than the comparison algorithm under different modulation modes and carrier frequencies, especially suitable for carrier signal noise reduction under bad channel conditions. The algorithm proposed in this paper has good anti signal attenuation performance, and it can effectively enhance the SNR, and ensure the transmission performance and system reliability of the electricity information collection system.

The check-bits-weight-adjustable scheme of rate compatible modulation based on the systematic Polar code

Rate compatible modulation is a novel adaptive transmission technology that does not require channel state information feedback, can achieve rate adaptive adjustment, and smoothly approach channel capacity. However, due to the nature of incremental decoding, the decoding complexity is too high and the transmission performance is reduced under bad channel conditions. Based on the feature of the systematic Polar code that separates the information bits and the check bits, this paper optimizes the design of the hybrid cascade of the systematic Polar code and rate-compatible modulation, and proposes a feasible scheme for adjusting the corresponding weight of check bits. The simulation results show that the proposed scheme can achieve better transmission performance in a wider range of signal-to-noise ratio with relatively lower decoding overhead.

Trusted and secured D2D-aided communications in 5G networks

The design of the forthcoming fifth generation (5G) system shall meet the severe requirement of managing an always increasing amount of traffic generated by both humans and machines, while guaranteeing data security. Among the enabling technologies that will turn 5G into a reality, Device-to-Device (D2D) and Multicasting will certainly play a key role because of their capability to largely improve network resources utilization and to address emerging use cases requiring the delivery of the same content to a large number of devices. D2D communications can help to improve traditional point-to-multipoint transmissions by reducing the multicast coverage area and exploiting properly selected relay nodes as data forwarders toward users with worse channel conditions. However, security issues are even more challenging for D2D connections, as data exchange happens directly between nodes in proximity. To enhance performance and security of delivered traffic in 5G-oriented networks, in this paper we design SeT-D2D (Secure and Trust D2D), according to which trustworthiness inferred from both direct interactions and social-awareness parameters is exploited to properly select relay nodes. Main contributions of our research consist in the introduction of a model for the assessment of network nodes’ trustworthiness and the implementation of security mechanisms to protect the data transmitted in D2D communications and the privacy of the involved users. The conducted simulation campaign testifies to the ability of the proposed solution to effectively select relay nodes, which leads to an improved network performance.

Energy Efficiency of Uplink Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channel

Cell-free massive MIMO (CF-mMIMO) provides wireless connectivity for a large number of user equipments (UEs) using access points (APs) distributed across a wide area with high spectral efficiency (SE). The energy efficiency (EE) of the uplink is determined by (i) the transmit power control (TPC) algorithms, (ii) the numbers, configurations, and locations of the APs and the UEs, and (iii) the propagation channels between the APs and the UEs. This paper investigates all three aspects, based on extensive (~30,000 possible AP locations and 128 possible UE locations) channel measurement data at 3.5 GHz. We compare three different TPC algorithms, namely maximization of transmit power (max-power), maximization of minimum SE (max-min SE), and maximization of minimum EE (max-min EE) while guaranteeing a target SE. We also compare various antenna arrangements including fully-distributed and semi-distributed systems, where APs can be located on a regular grid or randomly, and the UEs can be placed in clusters or far apart. Overall, we show that the max-min EE TPC is highly effective in improving the uplink EE, especially when no UE within a set of served UEs is in a bad channel condition and when the BS antennas are fully-distributed.

Lightweight routing protocol for low‐tension narrowband powerline systems

AbstractThis article proposes a combination between source routing technique and on‐demand ad hoc routing algorithm to increase link reliability in power line communications over low‐tension power grid. The proposed methodology is an enhanced low‐complexity routing protocol for smart metering application (ELCSM). The algorithm considers data concentrator (DC) located directly after the low‐tension step‐down transformer. The data concentrator is polling the reachable meters connected to the power line and then it sends information about energy consumption, loading profile and any other crucial data to the utility through the cloud. The designed protocol minimizes the processing complexity at the meter's side, whereas most of the intelligence is shifted to the DC. The protocol takes into consideration the asymmetric characteristics of the power line channel, where some nodes could suffer from very bad downlink quality due to significant noise at the meter's side. These nodes that suffer from bad channel conditions and could not receive data sent from DC and/or other nodes are therefore classified as deaf nodes, although their transmission could be received properly by adjacent nodes. Moreover, unique lightweight packet structure is proposed to minimize algorithm overhead and to enhance the system robustness. The protocol performance is compared to LCSM and LOADng‐CTP in terms of protocol overhead, end‐to‐end delay, and packet delivery ratio.

Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning

In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which selects the device consuming the least updating time obtained by the optimal bandwidth allocation in each step, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius.

Stable Matching for Wireless URLLC in Multi-Cellular, Multi-User Systems

Ultra-Reliable Low-Latency Communications (URLLC) are considered as one of the key services of the upcoming fifth generation (5G) of wireless communications systems. Enabling URLLC is especially challenging due to the strict requirements in terms of latency and reliability. Multi-connectivity is a powerful approach to increase reliability. However, most of the current research is restricted to single-user scenarios, neglecting the challenges of multi-cellular, multi-user systems, i.e., interference and the competition for limited resources. In this article, we develop analytic comparisons of different connectivity approaches, showing that multi-connectivity may not always be optimal in the considered scenario. Moreover, we propose and evaluate novel resource allocation approaches based on stable matching theory to enable wireless URLLC. We extend the pure many-to-one stable matching procedure by utilizing the optimal connectivity approach for each user, optimizing the maximum number of matched resources, and providing a resource reservation mechanism for users suffering from bad channel conditions. System-level simulations demonstrate that the proposed algorithm outperforms baseline resource allocation approaches in outage probability by up to three orders of magnitude. Even in a highly loaded system, an outage probability in the range of ${10^{-5}}$ is achieved.