Interference In Wireless Networks Research Articles

Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach

Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach

Burstiness of Interference Pikes in Wireless Networks

The temporal dynamics of interference in wireless networks affects their performance but has only been studied for some cases. This article addresses this gap by analyzing high-interference events, called pikes, concluding that they arrive in bursts in many cases. Specifically, we show that in Poisson networks with random access and multipath fading, the pike interarrival time increases with the interference correlation, irrespective of the source of correlation and burstiness of pikes. To demonstrate the applicability of this theory, we conduct a measurement campaign with an automotive user in different commercial 4G cellular networks. The experimental results indicate that interference pikes are bursty in the real world as well.

Learning Resilient Radio Resource Management Policies With Graph Neural Networks

We consider the problems of user selection and power control in wireless interference networks, comprising multiple access points (APs) communicating with a group of user equipment devices (UEs) over a shared wireless medium. To achieve a high aggregate rate, while ensuring fairness across all users, we formulate a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resilient</i> radio resource management (RRM) policy optimization problem with per-user minimum-capacity constraints that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adapt</i> to the underlying network conditions via learnable slack variables. We reformulate the problem in the Lagrangian dual domain, and show that we can parameterize the RRM policies using a finite set of parameters, which can be trained alongside the slack and dual variables via an unsupervised primal-dual approach thanks to a provably small duality gap. We use a scalable and permutation-equivariant graph neural network (GNN) architecture to parameterize the RRM policies based on a graph topology derived from the instantaneous channel conditions. Through experimental results, we verify that the minimum-capacity constraints adapt to the underlying network configurations and channel conditions. We further demonstrate that, thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5^\mathrm{th}$</tex-math></inline-formula> percentile rate—a metric that quantifies the level of fairness in the resource allocation decisions—as compared to baseline algorithms.

Over-the-Air Computation of Large-Scale Nomographic Functions in MapReduce Over the Edge Cloud Network

Motivated by increasing powerful edge devices with data-intensive computing and limited storage size, we study a MapReduce-based wireless distributed computing framework by allocating a portion of files in the remote data center to the network edge and utilizing computation and memory resources at the edge. Our framework is composed of three step phases: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Map</i> ; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shuffle</i> ; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reduce</i> . However, in the data shuffling stage, shuffling many data accounts for a large amount of the total running time over wireless interference networks will degrade its performance. Moreover, data shuffling between pervasive edge devices with limited spectrum bandwidth is very challenging. Today, many devices focus on computing functions rather than collecting all the individual wireless data centers. Therefore, we can use over-the-air computation (AirComp) technology to reliably compute multiple target functions by harnessing interference in the multiple-access channel with a higher computation efficiency than the traditional orthogonal multiaccess scheme that combats interference. We study a mixed-timescale optimization of the transmitting–receiving (Tx-Rx) policy and file allocation to minimize the averaged computation mean-squared error (MSE) under the power constraint of each device. File allocation control is adaptive to the long-term statistical channel state information (CSI), while the Tx-Rx policy is adaptive to the CSI and file allocation strategy. We decompose the problem into a short-term Tx-Rx policy and a long-term file allocation control problem to tackle the joint nonconvex optimization. Simulation results indicate the effectiveness of our proposed two-timescale algorithm and the advantages of our computation framework over the state-of-the-art baselines.

Energy-Balancing Resource Allocation for Wireless Cooperative IoT Networks With SWIPT

In this article, orthogonal frequency-division multiplexing (OFDM)-based downlink simultaneous wireless information and power transfer (SWIPT) is considered in the Internet of Things (IoT) wireless interference network, where one hybrid access point (HAP) performs both wireless power transfer (WPT) and wireless information transfer (WIT) to energy harvesting (EH) and information decoding (ID) nodes while multiple coexisting energy access points (EAPs) carry energy to EH nodes. As the transmission methodology for interference avoidance, we improve and refine the subcarrier separation (SS), which allocates the dedicated energy subcarrier set orthogonal to the information subcarrier set. Based on the modified SS, to enhance the harvested energy while minimizing the number of dedicated energy subcarriers, we design the cooperative WPT with transmit diversity and adopt the time-division multiple access scheme for multiple EH nodes. The optimal joint subcarrier, node, and power allocation problem is formulated based on the proposed transmission strategies to maximize the achievable sum rate for given energy requirement with a practical nonlinear EH model for low-power IoT nodes. However, it is difficult to obtain the global optimal solution due to the nonconvexity of the problem, so we investigate the cooperative resource allocation scheme. First, to solve the subcarrier allocation and node selection jointly, two basic methods for maximizing energy and minimizing rate loss are designed, and the additional scheme considering both energy requirement and rate-loss minimization is proposed by introducing a weighting factor for balancing the total transmit power into WPT and WIT. Then, the optimal power allocation on the allocated subcarriers and selected nodes is performed with the split transmit power. Simulation results show that the proposed schemes achieve superior rate–energy (R-E) region to conventional schemes. Our study provides useful insights on how to transmit signals for improving performance in IoT interference networks with SWIPT.

Research on geometric algebra-based robust adaptive filtering algorithms in wireless communication systems

Noise and interference are the two most common and basic problems in wireless communication systems. The noise in wireless communication channels has the characteristics of randomness and impulsivity, so the performance of adaptive filtering algorithms based on geometric algebra (GA) and second-order statistics is greatly reduced in the wireless communication systems. In order to improve the performance of adaptive filtering algorithms in wireless communication systems, this paper proposes two novel GA-based adaptive filtering algorithms, which are deduced from the robust algorithms based on the minimum error entropy (MEE) criterion and the joint criterion (MSEMEE) of the MEE and the mean square error (MSE) with the help of GA theory. The noise interference in wireless communication is modeled by alpha-stable distribution which is in good agreement with the actual data in this paper. Simulation results show that for the mean square deviation (MSD) learning curve, the GA-based MEE (GA-MEE) algorithm has faster convergence rate and better steady-state accuracy compared to the GA-based maximum correntropy criterion algorithm (GA-MCC) under the same generalized signal-to-noise ratio (GSNR). The GA-MEE algorithm reduces the convergence rate, but improves the steady-state accuracy by 10–15 dB compared to the adaptive filtering algorithms based on GA and second-order statistics. For GA-based MSEMEE (GA-MSEMEE) algorithm, when GA-MSEMEE and the adaptive filtering algorithms based on GA and second-order statistics keep the same convergence rate, its steady-state accuracy is improved by 10–15 dB, and when GA-MSEMEE and GA-MEE maintain approximately steady-state accuracy, its convergence rate is improved by nearly 100 iterations. In addition, when the algorithms are applied to noise cancellation, the average recovery error of the two proposed algorithms is 7 points lower than that of other GA-based adaptive filtering algorithms. The results validate the effectiveness and superiority of the GA-MEE and GA-MSEMEE algorithms in the alpha-stable noise environment, providing new methods to deal with multi-channel interference in wireless networks.

Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning

Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make autonomous decisions on their transmission strategies with only local information. We demonstrate that with limited information exchange and faster convergence, DRL-CT algorithm can achieve 90% of the performance achieved by the combination of weighted minimum mean square error (WMMSE) algorithm for power control and exhaustive search for dynamic channel access. In the second part of this paper, we consider distributed multi-agent DRL scenario in which each user conducts its own training and makes its decisions individually, acting as a DRL agent. Finally, as a compromise between centralized and fully distributed scenarios, we consider federated DRL (FDRL) to approach the performance of DRL-CT with the use of a central unit in training while limiting the information exchange and preserving privacy of the users in the wireless system. Via simulation results, we show that proposed learning frameworks lead to efficient adaptive channel access and power control policies in dynamic environments.

A two‐fold optimization framework using hybrid B2 algorithm for resource allocation in long‐term evolution based cognitive radio networks system

SummaryCognitive radio networks (CRNs) have allowed efficient spectrum sharing while also presenting new challenges to the conventional problems of resource allocation and wireless network interference management. The aim is to provide wireless connectivity to secondary stations on a best‐effort basis while preserving the primary station's service quality. In order to achieve this objective, we have developed a model using hybrid B2 optimization. This method is used for resource allocation in a CRN based on the long‐term evolution (LTE) standard platform of the Third Generation Partnership Project (3GPP). The hybrid B2 optimization algorithm is formed by integrating the bacterial foraging and black widow optimization algorithm. Initially, to optimize the level of service for primary customers, spectrum resources are allocated to primary stations using our proposed model. Later, the spare capacity of the primary channels is then spread among the secondary stations in order to increase the secondary users' service efficiency. The proposed work is twofold where it handles both the low traffic and high traffic scenarios. To reduce the prediction error in a high traffic and bursty environment, an adaptive weighted least square support vector machine (AWLSSVM) is presented. Furthermore, the proposed model is compared with other existing models to evaluate the system's effectiveness.

Interference Prediction in Wireless Networks: Stochastic Geometry Meets Recursive Filtering

This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that estimates future interference values by filtering measured interference at a given location. The predictor's parameterization is done offline by translating the autocorrelation of interference into an autoregressive moving average (ARMA) representation. This ARMA model is inserted into a steady-state Kalman filter enabling nodes to predict with low computational effort. Results show a good accuracy of predicted values versus true values for relevant time horizons. Although the predictor is parameterized for Poisson-distributed nodes, Rayleigh fading, and fixed message lengths, a sensitivity analysis shows that it also tends to work well in more general network scenarios. Numerical examples for underlay device-to-device communications, a common wireless sensor technology, and coexistence scenarios of Wi-Fi and LTE illustrate its broad applicability. The predictor can be applied as part of interference management to improve medium access, scheduling, and radio resource allocation.

Physical Layer Security Aided Wireless Interference Networks in the Presence of Strong Eavesdropper Channels

Under both long (infinite) and short (finite) blocklength transmissions, this paper considers physical layer security for a wireless interference network of multiple transmitter-user pairs, which is overheard by multiple eavesdroppers (EVs). The EVs are assumed to have better channel conditions than the legitimate users (UEs), making the conventional transmission unsecured. The paper develops a novel time-fraction based transmission, under which the information is transmitted to the UEs within a fraction of the time slot and artificial noise (AN) is transmitted within the remaining fraction to counter the strong EVs’ channels. Based on channel distribution information of UEs and EVs, the joint design of transmit beamforming, time fractions and AN power allocation to maximize the worst users’ secrecy rate is formulated in terms of nonconvex problems. Path-following algorithms of low complexity and rapid convergence are proposed for their solution. Simulations are provided to demonstrate the viability of the proposed methodology.

Interference-aware metrics impact on the performance of incremental multipath routing in WSNs

Multipath routing has been considered in the literature to provide bandwidth aggregation for high data rate applications. Because of the phenomena of inter-path interference in wireless networks, the overall throughput is far from being the summation of the provided ones by the different paths individually. One promising solution to this problem is to make use of interference aware metrics to build multiple paths such that the inter-path interference is minimised. In this work, we are interested in assessing the impact of a set of interference aware metrics on the performance of incremental multipath routing in Wireless Sensor Networks (WSN). Incremental multipath routing builds one path per request/reply session and hence is more suitable to be combined to interference aware metrics. We review, implement and evaluate both active and passive monitoring metrics and show that for constrained networks such as WSNs, the latter are more adapted. Additionally, we show that within the passive monitoring metrics, the best one is not necessarily the most expensive to measure.

Autocorrelation and Coherence Time of Interference in Poisson Networks

This article takes an analytical approach to investigate the temporal dynamics of interference in wireless networks. We propose a framework to calculate the autocorrelation of interference in Poisson networks and derive closed-form expressions for the case of Nakagami fading. The framework takes three correlation sources into account: the location of interferers, the wireless channel, and the data traffic. We introduce the interference coherence time-in analogy to the well-established channel coherence time-and show how its basic qualitative behavior depends on the source of correlation. The insights gained can be useful in the design of medium access control and retransmission protocols.

On Interference Dynamics in Matérn Networks

A thorough understanding of the temporal dynamics of interference in wireless networks is crucial for the design of communication protocols, scheduling, and interference management. This article applies stochastic geometry to investigate the interference dynamics in a network of nodes that use carrier sense multiple access (CSMA). This type of network is approximated by a Matérn hard-core point process of type II with Nakagami fading. We derive and analyze expressions for the variance, covariance, and correlation of the interference power at an arbitrary location. Results show that even though the commonly used Poisson approximation to CSMA may have the same average interference as the Matérn model, the interference dynamics behaves significantly different. In this way, this study reveals a pitfall in the modeling of wireless systems and contributes to the theory of interference calculus.

Heterogeneous Networks in 5g using Joint Path Selection and Rate Allocation Framework

One of the major challenges in evolving wireless cellular networks whose return network is constrained by capability and heterogeneity (wired, wireless, and hybrid) is the design of distributed management mechanism. This study proposes a new method of managing wireless network interference with the ability to detect return network. The proposed approach helps Macro-cell User Equipment (MUE) to maximize its output using adjacent small cell base stations to maximize its uplink. Considering the radio access network and backhaul (possibly heterogeneous), this issue is represented as a non-cooperative game between MUE that attempts to maximize its compensation for delay levels. A new distributed learning algorithm is proposed to solve this problem, which uses the algorithm to autonomously choose the best uplink transmission strategy, provided a limited amount of available knowledge. The algorithm's convergence is demonstrated, and its output is studied. The simulation results show that, in comparison to the existing reference algorithm, The proposed approach has substantial efficiency benefits for various forms of backhaul in terms of average output and delay in MUE.

Doherty PAs for 5G Massive MIMO: Energy-Efficient Integrated DPA MMICs for Sub-6-GHz and mm-Wave 5G Massive MIMO Systems

To accommodate growing user demand for faster data rates and extensive connectivity, modern wireless communication systems must evolve to support a sharply increasing number of subscribers, all requesting service at the same time. This trend encourages the broad application of multiple input/multiple output (MIMO) systems. In fact, MIMO techniques can increase data rates, coverage of service areas, and communication reliability without additional RFs. In recent proposals for 5G systems, the required separate RF chains in massive MIMO RF front ends can reach up to 256, with bandwidths of up to 800 MHz per RF chain [1], [2]. Massive MIMO is a critical technology that helps significantly in increasing network capacity and spectral efficiency, while reducing wireless network interference, ultimately improving the end-user experience.

Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks

Resource allocation plays a pivotal role in improving the performance of wireless and communication networks. However, the optimization of resource allocation is typically formulated as a mixed-integer non-linear programming (MINLP) problem, which is non-convex and NP-hard by nature. Usually, solving such a problem is challenging and requires specific methods due to the major shortcomings of the traditional approaches, such as exponential computation complexity of global optimization, no performance optimality guarantee of heuristic schemes, and large training time and generating a standard dataset of machine learning based approaches. Whale optimization algorithm (WOA) has recently gained the attention of the research community as an efficient method to solve a variety of optimization problems. As an alternative to the existing methods, our main goal in this article is to study the applicability of WOA to solve resource allocation problems in wireless networks. First, we present the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints. Then, we demonstrate three examples of WOA to resource allocation in wireless networks, including power allocation for energy-and-spectral efficiency tradeoff in wireless interference networks, power allocation for secure throughput maximization, and mobile edge computation offloading. Lastly, we present the adoption of WOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.

Topology Optimization for 6G Networks: A Network Information-Theoretic Approach

The classical approach of avoiding or ignoring interference in wireless networks cannot accommodate the ambitious quality-of-service (QoS) demands of ultradense cellular networks (CNs). However, recent ground-breaking information-theoretic advances have changed our perception of interference from that of a foe to a friend. This article aims to shed light on harnessing the benefits of integrating modern interference management (IM) schemes into future CNs. To this end, we envision a hybrid multiple-access (HMA) scheme that decomposes the network into subtopologies of potential IM schemes for more efficient utilization of network resources. Preliminary results show that an HMA scheme can multiply nonorthogonal multiple-access (NOMA) performance, especially under dense user deployment.

Wireless MapReduce Distributed Computing

Motivated by mobile edge computing and wireless data centers, we study a wireless distributed computing framework where the distributed nodes exchange information over a wireless interference network. Our framework follows the structure of MapReduce. This framework consists of Map, Shuffle, and Reduce phases, where Map and Reduce are computation phases and Shuffle is a data transmission phase. In our setting, we assume that the transmission is operated over a wireless interference network. We demonstrate that, by duplicating the computation work at a cluster of distributed nodes in the Map phase, one can reduce the amount of transmission load required for the Shuffle phase. In this work, we characterize the fundamental tradeoff between computation load and communication load, under the assumption of one-shot linear schemes. The proposed scheme is based on side information cancellation and zero-forcing, and we prove that it is optimal in terms of computation-communication tradeoff. The proposed scheme outperforms the naive TDMA scheme with single node transmission at a time, as well as the coded TDMA scheme that allows coding across data, in terms of the computation-communication tradeoff.

Degrees of Freedom in Wireless Interference Networks With Cooperative Transmission and Backhaul Load Constraints

Degrees of freedom (DoFs) gains are studied in wireless networks with cooperative transmission under a backhaul load constraint that limits the average number of messages that can be delivered from a centralized controller to base station transmitters. The backhaul load is defined as the sum of all the messages available at all the transmitters per channel use, normalized by the number of users. For Wyner's linear interference network, where each transmitter is connected to the receiver having the same index as well as one succeeding receiver, the per user DoF is characterized and the optimal scheme is presented. Furthermore, it is shown that the optimal assignment of messages to transmitters is asymmetric and satisfies a local cooperation constraint and the optimal coding scheme relies only on one-shot cooperative zero-forcing transmit beamforming. Using insights from the analysis of Wyner's linear interference network, the results are extended to the more practical hexagonal sectored cellular network, and coding schemes based on cooperative zero-forcing are shown to deliver significant DoF gains. It is established that by allowing for cooperative transmission and a flexible message assignment that is constrained only by an average backhaul load, one can deliver the rate gains promised by information-theoretic upper bounds with practical one-shot schemes that incur little or no additional load on the backhaul. Finally, useful upper bounds on the per user DoF for schemes based on cooperative zero-forcing are presented for lower values of the average backhaul load constraint, and an optimization framework is formulated for the general converse problem.

Limited feedback distributed interference alignment in cellular networks with large scale antennas

Interference alignment (IA) is an effective scheme for counteracting multi-user interference in wireless networks. Unfortunately, IA is sensitive to channel-state-information (CSI) imperfections. Achieving perfect CSI knowledge at a central node in large scale antenna wireless networks implies a huge feedback which is prohibitive. In this paper, we assume a distributed multicellular scenario, where there is no central node that knows global CSI and optimizing IA’s precoder and combiner matrices is done by exchanging the local channel information between users and base stations (BSs) in several iterations. By using low-rank matrix approximation theory, we propose an efficient method to iteratively optimize precoder and combiner matrices for distributed IA. In each iteration, only a part of the CSI is fed back to BSs. More precisely, based on the latest available CSI and certain performance criteria, a few columns of the effective channel are sent back to the transmitters to approximate the interference covariance matrix which is then used to update the precoder matrices. We also propose a new method for quantizing the channel information matrix non-uniformly, which improves upon the conventional channel feedback quantization techniques. We evaluate the proposed methods by simulating a cellular network with various number of BS antennas and different feedback channel capacities. Simulation results show that our methods outperform both the conventional and improved channel feedback quantization algorithms significantly.