Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA

The new era of IoT brings the necessity of smart synergy for diverse communication and computation entities. The two extremes are, on the one hand, the 5G Ultra-Reliable Low-Latency Communications (URLLC) required for Industrial IoT (IIoT) and Vehicle Communications (V2V, V2I, V2X). While on the other hand, the Ultra-Low Power, Wide-Range, Low Bit-rate Communications, such as Sigfox, LoRa/LoRaWAN, NB-IoT, Cat-M1, etc.; used for smart metering, smart logistics, monitoring, alarms, tracking applications. This extreme variety and diversity must work in synergy, all inter-operating/inter-working with the Internet. The communication solutions must mutually cooperate, but there must be a synergy in a broader sense that includes the various communication solutions and all the processing and storage capabilities from the edge cloud to the deep-cloud. In this paper, we consider a non-orthogonal multiple access (NOMA)-based device to device (D2D) communication system coexisting with a cellular network and utilize Greedy Asynchronous Distributed Interference Avoidance Algorithm (GADIA) for dynamic frequency allocation strategy. We analyze a max–min fairness optimization problem with energy budget constraints to provide a reasonable boundary rate for the downlink to all devices and cellular users in the network for a given total transmit power. A comprehensive simulation and numerical evaluation is performed. Further, we compare the performance of maximum achievable rate and energy efficiency (EE) at a given spectral efficiency (SE) while employing NOMA and orthogonal frequency-division multiple access (OFDMA).

Interference Management for the Coexistence of DTTV and LTE Systems within the Proposed Digital Dividend Band in Nigeria

This paper seeks to present the Interference Management for the Coexistence of DTTV and LTE Systems within the proposed digital dividend band in Nigeria. The study focused on LTE Down-link (DL) signal from the nearest cell site interfering with the Digital Terrestrial Television (DTTV) fixed outdoor receiving antenna in Port Harcourt, Nigeria. The qualitative signal analysis of the DTTV systems is essential as DTTV system cannot start to operate in the newly formed frequency band without the evaluation of the possible harmful influence of the coexisting systems. This research work investigated the Compatibility of the two systems and the Probability of interference of Channel 17 (490MHz) and Channel 51 (693MHz) when DTTV and LTE systems coexist within the proposed Digital Dividend band. A test-bed approach method was adopted for the generation of the required simulation data. Star Time transmitting Station in Port Harcourt and Smile LTE 4G Communication LTE Base Station (eNBs) Network also in Port Harcourt were adopted as the Victim Link Transmitter (VLT) and Interfering Link Transmitter (ILT) respectively. Data was obtained, analyzed, and evaluated. It was observed from the simulation result that the probability of interference is a function of the separation distance between ILT and VLR. The Compatibility analysis result shows that the resulting C/I is above the protection criteria (19dB), that is there’s a minimal rate of interference. Hence, the interference issue can be managed when the two systems coexist in700MHz band. It was also established that DTTV channel 51 suffers more interference when compared with DTTV channel 17 for the same separation distance. The study recommended the minimum protection distance approach (Interference Avoidance method) as the interference management techniques when DTTV and LTE systems coexist in the proposed digital dividend (700MHz) band in Nigeria.

A Deep Learning Based Automatic Interference Avoidance Resource Allocation Scheme for SCMA Systems

Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.

A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-enabled IoT

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.

DOA-based Localization Using Deep Learning for Wireless Seismic Acquisition

Oil and gas companies consider transforming conventional cable-based seismic acquisition to wireless acquisition as a promising step for cost and weight reduction in reservoir exploration. Wireless seismic acquisition requires large number of wireless geophone (WG) sensors to be deployed in the field. The locations of the WG sensors must be known when processing the collected data. The application of direction of arrival (DOA) estimation helps in localizing WGs and improves received signal level through beam steering and interference avoidance. Conventional DOA algorithms require high computational complexity which renders them inefficient for real-time response. In this paper, deep neural network (DNN) is proposed for DOA estimation of WGs at wireless gateway node (WGN) under different channel conditions. The estimated angle and corresponding coordinates of WGNs are used in least square estimation (LSE) to estimate the position of the WGs. The simulation results depict reasonable estimation and position accuracy in real-time.

Experiment Control and Monitoring System for LOG-a-TEC Testbed

The LOG-a-TEC testbed is a combined outdoor and indoor heterogeneous wireless testbed for experimentation with sensor networks and machine-type communications, which is included within the Fed4FIRE+ federation. It supports continuous deployment principles; however, it is missing an option to monitor and control the experiment in real-time, which is required for experiment execution under comparable conditions. The paper describes the implementation of the experiment control and monitoring system (EC and MS) as the upgrade of the LOG-a-TEC testbed. EC and MS is implemented within existing infrastructure management and built systems as a new service. The EC and MS is accessible as a new tab in sensor management system portal. It supports several commands, including start, stop and restart application, exit the experiment, flash or reset the target device, and displays the real-time status of the experiment application. When nodes apply Contiki-NG as their operating system, the Contiki-NG shell tool is accessible with the help of the newly developed tool, giving further experiment execution control capabilities to the user. By using the ZeroMQ concurrency framework as a message exchange system, information can be asynchronously sent to one or many devices at the same time, providing a real-time data exchange mechanism. The proposed upgrade does not disrupt any continuous deployment functionality and enables remote control and monitoring of the experiment. To evaluate the EC and MS functionality, two experiments were conducted: the first demonstrated the Bluetooth Low Energy (BLE) localization, while the second analysed interference avoidance in the 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) wireless technology for the industrial Internet of Things (IIoT).

An Overview and Mechanism for the Coexistence of 5G NR-U (New Radio Unlicensed) in the Millimeter-Wave Spectrum for Indoor Small Cells

In this paper, we first give an overview of the coexistence of cellular with IEEE 802.11 technologies in the unlicensed bands. We then present a coexistence mechanism for Fifth-Generation (5G) New Radio on Unlicensed (NR-U) small cells located within buildings to coexist with the IEEE 802.11ad/ay, also termed as Wireless Gigabit (WiGig). Small cells are dual-band enabled operating in the 60 GHz unlicensed and 28 GHz licensed millimeter-wave (mmW) bands. We develop an interference avoidance scheme in the time domain to avoid cochannel interference (CCI) between in-building NR-U small cells and WiGig access points (APs). We then derive average capacity, spectral efficiency (SE), and energy efficiency (EE) performance metrics of in-building small cells. Extensive system-level numerical and simulation results and analyses are carried out for a number of variants of NR-U, including NR standalone, NR-U standalone, and NR-U anchored. We also analyze the impact of the spatial reuse of both mmW spectra of multiple NR-U anchored operators with a WiGig operator. It is shown that NR-U anchored provides the best average capacity and EE performances, whereas NR-U standalone provides the best SE performance. Moreover, both vertical spatial reuse intrabuilding level and horizontal spatial reuse interbuilding level of mmW spectra in small cells of an NR-U anchored can improve its SE and EE performances. Finally, we show that by choosing appropriate values of vertical and horizontal spatial reuse factors, the proposed coexistence mechanism can achieve the expected SE and EE requirements for the future Sixth-Generation (6G) mobile networks.

A modified GADIA-based upper-bound to the capacity of Gaussian general N-relay networks

In this paper, we present a general Gaussian N-relay network by allowing relays to communicate to each other and allowing a direct channel between source and destination as compared to the standard diamond network in Nazaroğlu et al. (IEEE Trans Inf Theory 60:6329–6341, 2014) at the cost of extra channel uses. Our main focus is to examine the min-cut bound capacities of the relay network. Very recently, the results in Uykan (IEEE Trans Neural Netw Learn Syst 31:3294–3304, 2020) imply that the GADIA in Babadi and Tarokh (IEEE Trans Inf Theory 56:6228–6252, 2010), a pioneering algorithm in the interference avoidance literature, actually performs max-cut of a given power-domain (nonnegative) link gain matrix in the 2-channel case. Using the results of the diamond network in Nazaroğlu et al. (2014) and the results in Uykan (2020), in this paper, we (i) turn the mutual information maximization problem in the Gaussian N-relay network into an upper bound minimization problem, (ii) propose a modified GADIA-based algorithm to find the min-cut capacity bound and (iii) present an upper and a lower bound to its min-cut capacity bound using the modified GADIA as applied to the defined “squared channel gain matrix/graph”. Some advantages of the proposed modified GADIA-based simple algorithm are as follows: (1) The Gaussian N-relay network can determine the relay clusters in a distributed fashion and (2) the presented upper bound gives an insight into whether allowing the relays to communicate to each other pays off the extra channel uses or not as far as the min-cut capacity bound is concerned. The simulation results confirm the findings. Furthermore, the min-cut upper bound found by the proposed modified-GADIA is verified by the cut-set bounds found by the spectral clustering based solutions as well.