Kelvin Anoh

Due to high power consumption and other problems, it is unlikely that orthogonal frequency-division multiplexing (OFDM) would be included in the uplink of the future 6G standard. High power consumption in OFDM systems is motivated by the high peak-to-average power ratio (PAPR) introduced by the inverse Fourier transform (IFFT) processing kernel in the time domain. Linear precoding of the symbols in the frequency domain using discrete Hartley transform (DHT) could be used to minimise the PAPR problem, however, at the cost of increased complexity and power consumption. In this study, we minimise the computation complexity of the DHT precoding on OFDM transceiver schemes and the consequent power consumption. We exploit the involutory properties of the processing kernels to process the DHT and IFFT as a single-processing block, thus reducing the system complexity and power consumption. These also enable a novel power-saving receiver design. We compare the results to three other precoding schemes and the standard OFDM scheme as the baseline; while improving the power consumption efficiency of a Class-A power amplifier from 4.16% to 16.56%, the bit error ratio is also enhanced by up to 5 dB when using a 1/2-rate error-correction coding and 7 dB with interleaving.

As the technical specifications of the 5th Generation (5G) wireless communication standard are being wrapped up, there are growing efforts amongst researchers, industrialists, and standardisation bodies on the enabling technologies of a 6G standard or the so-called Beyond 5G (B5G) one. Although the 5G standard has presented several benefits, there are still some limitations within it. Such limitations have motivated the setting up of study groups to determine suitable technologies that should operate in the year 2030 and beyond, i.e., after 5G. Consequently, this Special Issue of Future Internet concerning what possibilities lie ahead for a 6G wireless network includes four high-quality research papers (three of which are review papers with over 412 referred sources and one regular research). This editorial piece summarises the major contributions of the articles and the Special Issue, outlining future directions for new research.

Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing (P2P-ETS). However, as more distributed energy resources integrate into the distribution network, the impact of the communication link becomes significant. We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS. On one hand, the proposed algorithm minimizes the cost of energy generation and communication delay. On the other hand, it also maximizes the global utility of prosumers with fair resource allocation. We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss. The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms. It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others.

Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

The increasing integration of prosumers and smart metering devices into the energy distribution network, is transforming the traditional energy market to a community energy trading that requires peer-to-peer (P2P) interactions. Such P2P interactions result in complex data exchanges among prosumers, utility grid, and market operators. This inevitably introduces control complexity, security, and privacy challenges in the existing power system. The application of distributed ledger technology (DLT) has seen an increase in solving security challenges in the power network, specifically, in P2P energy exchanges. Thus, this study explores different DLT structures including blockchain and IOTA usage in energy P2P trading. A smart contract for managing trust and transactions is designed and implemented on IBM hyperledger fabric platform. In addition, we evaluated the performance of interconnected internet of things devices for energy transactions with IOTA protocol, which uses the directed acyclic graph as its DLT structure, against the Ethereum-based blockchain structure. It is shown that the end-to-end transaction delay with the IOTA DLT is lower than the Ethereum-based DLT implementation.

As the evolving communication standards would leverage on high data rates and low power consumption, future communication systems must be able to demonstrate these strengths. Space-time block codes (STBC) and quasi-orthogonal STBC (QO-STBC) including beamforming are multiple-input multiple output (MIMO) system design techniques used to improve data rates and reduce bit error ratio (BER). STBCs for larger antenna configurations use QO-STBC schemes which suffer from self-interference problems. The self-interference in QO-STBC systems diminishes the data rates and worsen the BER. In this study, we present three (3) methods of overcoming the self-interference problems in QO-STBC systems. We implement the interference-free QO-STBC systems with directional beamforming to improve the data rates and also reduce the BER. The results show significantly improved BER performance when the interferences are eliminated. An additional 3dB gain is achieved at 10(-4) BER when the interference-free QO-STBCs are operated with directional beamforming. In terms of data rates, up to 6 bits/s at reasonably low power consumption are realized when the Hadamard-based QO-STBC is operated with directional beamforming.

In residential homes, domestic energy storage in batteries have been proposed by many to support the grid. To foster its integration into the grid, virtual power plant (VPP) technology is used. In this paper, we evaluate Peukert condition of domestic battery storage within a given distribution level market. An evolutionary algorithm is applied to optimize the social welfare of stakeholders in a community VPP at different levels of Peukert conditions. The dynamic load performance of the VPP with respect to the grid requirements for demand-side management (DSM) is also presented to evaluate the impact of the Peukert effect on DSM. The results show that the social welfare of the VPP stakeholders decreases as Peukert effects increase.

Orthogonal frequency-division multiplexing (OFDM) is a popular multi-carrier technique used in many digital communication systems such as wireless fidelity (Wi-Fi), long term evolution (LTE) and power line communication systems. It can be designed using fast Fourier transform (FFT) or wavelet transform (WT). The major drawback in using WT is that it is computationally inefficient. In this study, we introduce a simple and computationally efficient WT, harmonic wavelet transform, for OFDM signal processing. The new WT uses the orthogonal basis functions of conventional FFT-OFDM except that it involves translation and dilation of the input signal; the new wavelets is referred to as harmonic wavelets (HW). When compared with pilot-assisted OFDM system in terms of reduction in the peak-to-average power ratio, the results show that HW-OFDM outperforms FFT-OFDM by 3 dB at 10(-4) CCDF (complementary cumulative distribution function). Over Rayleigh fading channel with additive white Gaussian noise (AWGN), the bit error ratio of both FFT-OFDM and HW-OFDM perfectly matched, showing that the proposed HW-OFDM is better in terms of peak-to-average power ratio reduction.

In residential homes, domestic energy storage in batteries have been proposed by many to support the grid. To foster its integration into the grid, virtual power plant (VPP) technology is used. In this paper, we evaluate Peukert condition of domestic battery storage within a given distribution level market. An evolutionary algorithm is applied to optimize the social welfare of stakeholders in a community VPP at different levels of Peukert conditions. The dynamic load performance of the VPP with respect to the grid requirements for demand-side management (DSM) is also presented to evaluate the impact of the Peukert effect on DSM. The results show that the social welfare of the VPP stakeholders decreases as Peukert effects increase.

Traditionally, energy consumers pay non-commodity charges (e.g. transmission, environmental and network costs) as a major component of their energy bills. With the distributed energy generation, enabling energy consumption close to producers can minimize such costs. The physically constrained energy prosumers in power networks can be logically grouped into virtual microgrids (VMGs) using communication systems. Prosumer benefits can be optimised by modelling the energy trading interactions among producers and consumers in a VMG as a Stackelberg game in which producers lead and consumers follow. Considering renewable (RES) and non-renewable energy (nRES) resources, and given that RES are unpredictable thus unschedulable, we also describe cost and utility models that include load uncertainty demands of producers. The results show that under Stackelberg equilibrium (SE), the costs incurred by a consumer for procuring either the RES or nRES are significantly reduced while the derived utility by producer is maximized. We further show that when the number of prosumers in the VMG increases, the CO2 emission cost and consequently the energy cost are minimized at the SE. Lastly, we evaluate the peer-to-peer (P2P) energy trading scenario involving noncooperative energy prosumers with and without Stackelberg game. The results show that the P2P energy prosumers attain 47% higher benefits with Stackelberg game.