5G and the next generation networks are expected to support diverse quality of service (QoS) requirements while ensuring network energy efficiency in the face of increasing mobile data traffic. However, wireless networks are resource-constrained, and ensuring QoS guarantees and maintaining energy efficiency is a challenging task. Rapid fluctuation of signal quality due to the mobility of the user equipment (UE) makes the task more complicated. Nevertheless, user mobility is not entirely random (e.g., UE with vehicle speed traveling along a highway) and results in channel gain that significantly varies with time. Since mobility factors (speed, location, direction of movement) and received signal strength are correlated, user mobility can be exploited to enhance resource allocation decisions and thus improve both UE QoS and network energy efficiency. In this paper, we propose a deep reinforcement learning (DRL)-based mobility and QoS-aware downlink scheduling algorithm that takes advantage of user mobility information to minimize network energy consumption. Using reference signal received power (RSRP) measurement reports, the proposed algorithm incorporates mobility awareness into the scheduling rule to decide whether to serve UE in the current time slot or future slots, within the packet delay budget. By jointly considering packet remaining due-time and mobility information, the algorithm schedules transmissions of packets at lower power, improving energy efficiency while meeting QoS requirements. Simulation results demonstrate that the proposed approach reduces energy consumption by 18.2% and improves QoS satisfaction compared to existing methods.

In this paper, we model and analyze the characteristics of a blockchain enable wireless Internet of Things. Blockchain is considered a vital technology for the development of a secure and trusted Internet of Things (IoT) in next-generation networks. However, the current blockchain architecture usually requires extensive computing power and storage capacity, which hinders its deployment in a wireless IoT network. Taking into account these limitations, we establish an analytical model for a lightweight blockchain architecture tailored to wireless IoT networks, and investigate the transmission success probability of the proposed model. We then capture the relationship between transmission link condition (e.g., the transmission success probability) and the success rate of Raft consensus inside the blockchain network. Our analysis shows that the Raft consensus success rate is a function of the transmission link condition and the number of network nodes. Furthermore, when we increase the number of nodes, the consensus success rate displays different tendencies with regard to uplink and downlink transmission link conditions. In particular, consensus success increases when the combination of transmission link probabilities on uplink and downlink is greater than 1/2, and decreases in the opposite scenario. The authenticity of our analysis is verified via simulations.

In this work, we propose a distributed learning framework that classifies users' transportation modes by leveraging heterogeneous networks (HetNets) architecture and employing a federated learning (FL) algorithm. The ultra-densification of small cells and the dynamic mobility of users impact performance by triggering unnecessary handovers. Therefore, information on user mobility allows the network to perform handover management more intelligently and efficiently. The proposed machine learning framework adopts distributed learning using a federated learning algorithm to detect transportation modes, including driving a car, riding a bicycle, walking, and running. In the proposed framework, local models are trained at small cells using user history information inherently distributed on the network side. A macro cell aggregates the local models to get a global model for classifying the transportation modes of users. Training the local models by small cells over user history information addresses critical FL issues, such as non-independent and identically distributed data and system heterogeneity. Simulation results demonstrate that the proposed framework achieves an accuracy of 98.85% in classifying transportation modes, utilizing input features extracted from user history information.

In this paper, we propose a power allocation algorithm to guarantee quality of experience (QoE) while considering the impact of imperfect channel state information (CSI) in multi-connectivity multiple-input multiple-output (MIMO) systems. Multi-connectivity is a promising solution for wireless technologies to fulfill ever-increasing demands for QoE via massive MIMO. However, the performance of MIMO systems mostly depends on the accuracy of the channel estimation algorithm that provides CSI. Under imperfect CSI scenarios, increasing transmit power to satisfy QoE for one user exacerbates interference with others, thus leading to a tradeoff between power consumption and user satisfaction. To guarantee QoE while minimizing transmission power under imperfect CSI, the effect of imperfect CSI is quantified, and a closed form signal-to-interference-plus-noise ratio (SINR) is derived. Then, a two-stage algorithm categorizes UEs into two groups: those that only need single connectivity and those that need dual connectivity (i.e., adaptive connectivity). After classification, transmission power is allocated to guarantee QoE via a power control strategy that minimizes the transmission power. By comparing performance with a single connectivity–based algorithm and fixed multi-connectivity–based algorithms, we show that our proposed algorithm not only satisfies all the UEs in the system but also consumes less transmission power than the benchmarks.

The following topics are dealt with: wireless channels; optimisation; resource allocation; cellular radio; learning (artificial intelligence); MIMO communication; telecommunication network reliability; telecommunication traffic; 5G mobile communication; radiofrequency interference.

In this paper, we analyze vertical handovers in heterogeneous networks. Vertical handovers are an important ramification of user mobility in a heterogeneous wireless network since they directly impact network performance, such as throughput, packet delay, or call blocking probability. However, the study of vertical handovers faces many difficulties when it comes to analysis, including network modeling which is extremely complicated due to topology diversity. To tackle such analytic challenges, we propose a stochastic geometric analysis scheme on user mobility and small cell networks, in order to capture the impacts of different topologies. We derive a theoretical expression for the expected number of handovers that occur and a corresponding approximation. Our analysis shows that the number of vertical handovers is determined as a concave function of small cell radius r when given the small cell density. Via simulations our analysis is verified in various wireless environments.

Handover failure and ping-pongs are the common thorny issues in modern mobile networks. While handover failures caused by radio link failure (RLF) significantly reduces the reliability of network operation, ping-pongs drastically waste signaling resources. In the upcoming fifth-generation (5G) networks especially, a complex deployment of small cells can exacerbate the two problems, even though the network can be integrated with a self-organizing network (SON), which is an automation-based solution. Due to the coupling of RLFs and ping-pongs as explained in the literature, it is difficult to analyze handovers and minimize both RLFs and ping-pongs simultaneously. In this paper, we model a handover procedure with geometric elements (Apollonian circles and the straight line), and analyze handover performance. The analysis provides an optimal handover setting for minimizing both RLFs and ping-pongs together, whereas previous works only considered trade-offs between them. We show that our analysis accurately estimates the optimal setting by comparing it with an NS-3 simulation. From the analysis, different environments can require different optimal values: fading (as well as interference) limit the optimal values; user speed has a scaling impact; and time-to-trigger has a shifting effect.

Milimeter-wave links can provide GBit/s data rates but are highly susceptible to blockage. In case a direct line-of-sight communication path becomes blocked, communication via a reflected path may allow to maintain connectivity. A common approach is to switch to such an alternative path whenever the first path becomes blocked. However, this requires detecting the blockage and then reconfiguring the transceiver to use the new path which incurs latency. For traffic with strict latency or reliability requirements, or in highly dynamic environments where path switching would be frequent, using both paths concurrently can be more beneficial. In this paper, we consider using multiple paths and dividing the transmission power over those paths, instead of path switching. We propose an algorithm to allocate power among the different mmWave communication paths to overcome link blockage under randomly distributed obstacles. The power allocation algorithm is based on analysis of the blockage probabilities of the direct and reflected paths using geometric probability, to statistically maximize the overall capacity of the path between two nodes. We evaluate the performance of the proposed algorithm via simulation for various wireless environments.

Small cells were introduced to support high data-rate services and for dense deployment. Owing to user equipment (UE) mobility and small-cell coverage, the load across a small-cell network recurrently becomes unbalanced. Such unbalanced loads result in performance degradation in throughput and handover success and can even cause radio link failure. In this paper, we propose a mobility load balancing algorithm for small-cell networks by adapting network load status and considering load estimation. To that end, the proposed algorithm adjusts handover parameters depending on the overloaded cells and adjacent cells. Resource usage depends on signal qualities and traffic demands of connected UEs in long-term evolution. Hence, we define a resource block-utilization ratio as a measurement of cell load and employ an adaptive threshold to determine overloaded cells, according to the network load situation. Moreover, to avoid performance oscillation, the impact of moving loads on the network is considered. Through system-level simulations, the performance of the proposed algorithm is evaluated in various environments. Simulation results show that the proposed algorithm provides a more balanced load across networks (i.e., smaller standard deviation across the cells) and higher network throughput than previous algorithms.

Due to high propagation loss and directivity, mmWave links are very susceptible to obstacles blocking the direct line-of-sight path for communication. In this case, indirect communication via a relay may help to circumvent the blockage. In this paper, we propose a two-hop relay selection algorithm for mmWave communications. For the relay selection, we analyze the probability that an indirect path is available given that the direct path is blocked trough geometric analysis. We then choose the most promising node among neighbors as relay. The analysis shows that the probability of an indirect path is a function of the obstacle density as well as the location of relay nodes. When the density is low, the correlation between the direct path and an indirect path is dominant, i.e., the angle between the direct path and the path to relay should be large, whereas the blockage probability of an indirect path becomes more dominant as the density increases, i.e., relay links should not be too long. The probability analysis also allows to decide an initial antenna angle for beam-training in mobile mmWave environments. Through numerical studies, we verify our analytical results.

A vehicle-to-vehicle (V2V) network is one type of mobile ad hoc network. Due to mobility, the topology in a V2V network is time-varying, which complicates the analysis and evaluation of network performance. In this paper, we model the network as geometric elements of lines and points and analyze the connectivity and capacity of the network using geometric probability. Under the assumption that n vehicles randomly arrive with a Poisson distribution, our analysis shows that the spatial distribution of vehicles within a given distance D, is uniform and that the average number of vehicles to be fully connected is approximately (1/a)(log (1/a) + log log (1/a)) for a = RT/D, where RT is the maximum transmission range of a vehicle. When a random access scheme is adopted, only (1/2)(1 - e-2)nof links comprised of two adjacent nodes are simultaneously activated, on average, so the expected network capacity increases in a way linearly proportional to (1/2)(1-e-2) as the number of vehicles increases. Through numerical studies and simulations, we verify the efficacy of our analytical results.

In this study, a concatenated coding scheme based on Reed–Muller (RM) codes and bit‐extension codes is proposed for equivocation of a wiretap channel. RM codes and their cosets are adopted for message encoding, and bit‐extension codes are used to enhance the equivocation capability for a wiretapper's channel. The average equivocation is discussed when only RM codes are used in the system, and the probability causing imperfect secrecy is also determined. Analytical results show that the proposed code can be used for the equivocation capability of wiretap channels and suggest a proper management over a wiretap channel.

In this paper, we develop an energy-efficient routing scheme that takes into account four key wireless system elements: transmission power; interference; residual energy; and energy replenishment. Since energy is a scarce resource, many energy-aware routing algorithms have been proposed to improve network performance. However, previous algorithms have been designed for a subset of these four main elements, which could limit their applicability. Thus, our contribution is here to develop a unified routing algorithm called the Energy-efficient Unified Routing (EURo) algorithm that accommodates any combination of these above key elements and adapts to varying wireless environments. We study the impact of key wireless elements on routing, and show via simulations that EURo outperforms the state-of-the-art.

In this paper, we propose a QoS uplink scheduling algorithm for LTE collaborating with delay estimation. Unlike downlink scheduling, uplink scheduling cannot incorporate packet delay information due to specification constraints of LTE. The limited information results in difficulty ensuring a QoS when conventional scheduling algorithms are employed. Using a delay estimation tailored to LTE, we have shown that the uplink performance of the proposed QoS scheduling scheme can be improved more than 11% even with a simple estimation via simulations.

In this paper, we solve a cell identification problem in a femtocell environment. In the densely deployed area, the number of Physical Cell Identifiers (PCIs) for femtocells may not be sufficient. The scarcity of PCIs results in identification ambiguity due to duplicated PCIs in a certain area, which may result in handover failure. Using time synchronization between femtocells and macro cells, we propose a scheme to efficiently identify cells in a femtocell environment. Our scheme can increase the number of cell identifiers up to 1024 times using even coarse local time synchronization, without changing the existing physical layer specification. Since time synchronization occurs locally, we can apply the proposed scheme to asynchronous as well as synchronous wireless systems. We verify the proposed scheme via simulations.

In this paper, we develop an energy-efficient routing scheme that takes into account the interference created by existing flows in the network. The routing scheme chooses a route such that the network expends the minimum energy satisfying with the minimum constraints of flows. Unlike previous works, we explicitly study the impact of routing a new flow on the energy consumption of the network. Under certain assumptions on how links are scheduled, we can show that our proposed algorithm is asymptotically (in time) optimal in terms of minimizing the average energy consumption. We also develop a distributed version of the algorithm. Our algorithm automatically detours around a congested area in the network, which helps mitigate network congestion and improve overall network performance. Using simulations, we show that the routes chosen by our algorithm (centralized and distributed) are more energy efficient than the state of the art.

In this paper, we develop an energy-efficient routing scheme that takes into account three key wireless system elements: transmission power; interference; and residual energy. Since energy is a scarce resource, many energy-aware routing algorithms have been proposed to improve network performance. However, previous algorithms have been designed for a subset of these three main elements, which could limit their applicability. Thus, our contribution is here to develop a unified routing algorithm called the Energy-efficient Unified Routing (EURo) algorithm that accommodates any combination of these above key elements. We show via simulations that EURo outperforms the state-of-the-art.

In this paper, we analyze the impact of straight line routing in large homogeneous multi-hop wireless networks. We estimate the nodal load, which is defined as the number of packets served at a node, induced by straight line routing. For a given total offered load on the network, our analysis shows that the nodal load at each node is a function of the node's Voronoi cell, the node's location in the network, and the traffic pattern specified by the source and destination randomness and straight line routing. The traffic pattern determines where the hot spot is created in the network, and straight line routing itself can balance the relay load in certain cases. In the asymptotic regime, each node's probability that the node serves a packet arriving to the network can be approximated as the multiplication of a half length of its Voronoi cell perimeter and the probability density function that a packet goes through the node's location. Both simulations and analysis confirm that this approximation converges to the exact value. The scaling order of network performance in our analysis is independent of traffic patterns generated by source-destination pair randomness, but for a given node the performance of each node is strongly related to the source-destination pair randomness.

In this paper, we develop an energy efficient routing scheme that takes into account the interference created by existing flows in the network. Unlike previous works, we explicitly study the impact of routing a new flow on the energy consumption of the network. Under certain assumptions on how links are scheduled, we can show that our proposed algorithm is asymptotically (in time) optimal in terms of minimizing the average energy consumption. We also develop a distributed version of the algorithm. Our algorithm automatically detours around a congested area in the network, which helps mitigate network congestion and improve overall network performance. Using simulations, we show that the routes chosen by our algorithm (centralized and distributed) are more energy efficient than the state of the art.

In this paper, we propose a new geographic routing algorithm that alleviates the effect of location errors on routing in wireless ad hoc networks. In most previous work, geographic routing has been studied assuming perfect location information. However, in practice there could be significant errors in obtaining location estimates, even when nodes use GPS. Hence, existing geographic routing schemes will need to be appropriately modified. We investigate how such location errors affect the performance of geographic routing strategies. We incorporate location errors into our objective function by considering both transmission failures and backward progress. Each node then forwards packets to the node that maximizes this objective function. We call this strategy maximum expectation within transmission range (MER). Simulation results with MER show that accounting for location errors significantly improves the performance of geographic routing. We also show that MER is robust to the location error model and model parameters. Further, via simulations, we show that in a mobile environment MER performs better than existing approaches.