Inter-user Interference Research Articles

Wavelet-Transform-Based Sparse Code Multiple Access for Power Line Communication

This paper presents Discrete Wavelet Transformed Sparse Code Multiple Access (DWT-SCMA) in Power Line Communication (PLC) systems. In the present internet of things era, PLC provides an established infrastructure for low-cost and reliable indoor connectivity. PLC systems can benefit from the Sparse Code Multiple Access (SCMA) technique, which allows multiple users to access a frequency slot simultaneously to maximize spectrum efficiency. However, interuser interference arises in SCMA when numerous users map their data to the same frequency resource; this, in turn, is likely to be enhanced by the noisy PLC channel. This article adopts the intriguing aspects of DWT to address the interference difficulties. A mathematical model of the proposed technique is also presented and compared with Fast Fourier Transformed SCMA (FFT-SCMA). In the PLC environment, DWT-SCMA is found to outperform FFT-SCMA.

FD-MCST design and analysis for multihop wireless networks

Full-duplex (FD) wireless communication improves the attainable spectral efficiency of the wireless network with the simultaneous transmission and reception over the same frequency channel at a single timeslot. However, the effect of self-interference (SI) and inter-user interference (IUI) become crucial to take into consideration compared to the current half-duplex system. Many researches have been studied to suppress the SI and IUI, to design the medium access control (MAC) protocol, and to propose resource management techniques for realizing the FD system and truly achieving the double transmission capacity in the network. In this paper, we propose a novel FD MAC protocol with a mixture of concurrent and sequential transmission scheme, namely FD-MCST, for maximizing the transmission capacity in multihop wireless networks. Through FD-MCST, the transmitting node can cooperatively share the transmission capacity status for maximizing the network capacity. As a result, numerical simulations reveal that the proposed FD-MCST can accomplish a higher achievable network capacity of up to 1.7 times and nearly twice the achievable throughput with a reasonable amount of achievable transmission overhead about 7.7%, compared to the existing FD MAC protocols.

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobility-induced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent onboard sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of 1.24\times and 1.28\times compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively

Spectral-efficient hybrid precoding for multi-antenna multi-user mmWave massive MIMO systems with low complexity

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems allow for a data transmission rate of gigabits per second owing to the large bandwidth available in the mmWave spectrum and the antenna gains provided by the massive MIMO system. However, hybrid precoding with high complexity and low spectral efficiency cannot address the challenge of high cost and power consumption of RF chains of multi-user systems. In this paper, we propose a low-complexity hybrid precoding scheme for downlink multi-antenna multi-user mmWave massive MIMO systems, aiming to enhance the sum spectral efficiency (SSE) performance. We first extend the dimension of the analog precoding matrix into a square matrix and find the optimal analog combiner by selecting some of the discrete Fourier transform (DFT) bases, which enhances the equivalent baseband channel matrix gain. Then, we directly aggregate the channel gain through the equal gain transmission (EGT) method to ensure the frequency efficiency performance. Finally, we propose an improved BD scheme to design the digital precoder and combiner to reduce the inter-user interference. We consider both the mmWave channel and the Rayleigh channel to evaluate the performance of the proposed algorithm. The simulation results verify that the proposed scheme enjoys near-optimal achievable sum spectrum efficiency and BER performance in both the mmWave channel and Rayleigh channel and performs even better in Rayleigh channel than in the mmWave channel.

Secure Transmission via IUI Engineering for IRS-Assisted NOMA Systems

Inter-user interference (IUI) engineering is utilized to degrade eavesdropping and enhance the physical layer security (PLS). In this letter, we propose a secure transmission strategy via IUI engineering for an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system without eavesdroppers channel state information (CSI). We aim to reduce the signal-to-interference-plus-noise ratio (SINR) of the passive eavesdropper to deteriorate eavesdropping by maximizing the transmit power of weaker users and modifying the successive interference cancelation (SIC) order. Moreover, two cases of combating eavesdropping via IUI engineering are considered according to different channel gain of secure users. The formulated problem is non-convex due to the highly coupled beamformers and reflection coefficients, we employ an alternating optimization (AO) framework to solve it. The results validate the advantages of introducing IUI engineering in the IRS-assisted NOMA system and the effectiveness of this optimization algorithm.

Stochastic learning for opportunistic peer-to-peer computation offloading in IoT edge computing

Peer-to-peer computation offloading has been a promising approach that enables resource-limited Internet of Things (IoT) devices to offload their computation-intensive tasks to idle peer devices in proximity. Different from dedicated servers, the spare computation resources offered by peer devices are random and intermittent, which affects the offloading performance. The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions. In this work, we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals, dynamic interuser interference, and opportunistic availability of peer devices. Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion, which takes into consideration its energy consumption, task delay, and task loss due to buffer overflow. The dynamic decision process among multiple requestors is formulated as a stochastic game. By constructing the post-decision states, a decentralized online offloading algorithm is proposed, where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations. Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms, especially in the scenarios with large task arrival probability or small helper availability probability.

Low computational complexity joint iterative detection and decoding without ARQ in massive MIMO systems with UAVs

Unmanned aerial vehicles (UAVs) have a wide range of military and commercial applications. UAVs play an important role in 6G networks due to their low cost and flexible positioning. UAVs can play different roles in the network and act as an air base station, as a relay, or as users in cellular networks. One of the major challenges in these systems is to increase lifespan with low energy consumption. The automatic repeat request (ARQ) protocol is used to increase throughput and obtain dependable communications for multi-input multiple-input multiple-output (MIMO) systems, even in the presence of severe propagation conditions. On the other hand, (UAV)-enabled communication system provides flexibility and reliability compared to conventional ones which have been considered in the new generation of wireless communications. Thus, using ARQ protocols in this system faces serious challenges such as increasing delay and operational implementation complexities in the receiver. Therefore, to meet the ARQ challenge in the uplink of massive MIMO systems, we have investigated utilizing joint iterative detection and decoding methods with reduced computational complexity have been proposed in a UAV-enabled communication system in the condition that users are equipped with two antennas to provide a solution to reduce energy consumption. In this structure, system performance improves as the computational complexity increases. Therefore, we look for methods that reduce complexity while maintaining system performance at an acceptable level. In this study, we seek to provide a solution that can establish a reasonable compromise between system performance and computational complexity. This aim is achieved by establishing a connection between the components of soft joint detection and decoding, linear detection with approximation methods, and sorting. The first part of the receiver is sorting the users before detection, then, in the next module, a turbo-process-based low-complexity MIMO iterative detection and decoding (IDD) algorithm, with minimum mean square error (MMSE) detector and soft channel decoder, is used. For solving the challenge of computational complexity, utilizing of approximation-based detection method is proposed. This structure works in such a way that after sorting the users, in the first iteration, a certain number of users are decoded using a hard decoding scheme after the soft detection and decoding. Therefore, these data are subtracted from the soft feedback information (prior data) to the detector, as inter user interference (IUI) and the Joint IDD module continue the iterative cycle until all users’ data are decoded.

Coordinated Beamforming Design for Multi-User Multi-Cell MIMO VLC Networks

Inter-cell interference (ICI) and inter-user interference (IUI) constitute a major issue towards achieving the optimum spectral efficiency (SE) and energy efficiency (EE) performance in multi-cell visible light communication (VLC) networks. Hence, advanced multiple access techniques need to be leveraged in order to improve the provided service to the users of such interfering networks. To this end, the present contribution proposes the integration of coordinated beamforming (CB) with rate-splitting multiple access (RSMA) in multi-cell VLC systems. Specifically, we consider the design of beamformers for the common and private streams in a coordinated manner between different attocells, which is shown to provide efficient mitigation of the incurred interference. Additionally, the formulated optimization problem aims to minimize the sum of the mean squared error across all attocells in order to jointly determine the optimum receive filters and coordinated transmit beamformers for RSMA streams. In this context, we illustrate through extensive computer simulations, which are carried out in a realistic setup that assumes noisy channel state information acquisition, the distinct flexibility and robustness of CB-based RSMA in mitigating the incurred interference. Finally, the offered results demonstrate the superiority of CB-based RSMA in terms of achievable SE and EE performance in multi-cell VLC networks compared to the conventional CB-based space division multiple access counterpart.

Max-Min fairness-based resource allocation in massive MIMO systems

This work deals with power and spectrum allocation approaches for massive MIMO (M-MIMO) systems. An analysis is made to verify the efficiency of the solutions provided by schemes used for the spectral and energy efficiency (SE-EE) trade-off problem in massive antenna-based wireless communication systems. We first introduce the Geometry Based Stochastic Model (GBSM) channel model (One-ring model), describing the behavior of a uniform linear array of antennas (ULA) arrangement, revealing how the channel parameters affect the channel capacity. We also show that under this model, the SE still increases without boundary when the massive number of base-station (BS) antennas $M$ increases, provided that pilot contamination is substantially mitigated or eliminated but when the number of users equipment (UEs) $K$ increases with a fixed number of antennas in the BS, there is a increasing limitation from the combiners in mitigating the inter-user interference, making decoding difficult. The downlink (DL) M-MIMO scenario is analyzed, by introducing the generalized power allocation problem and derived the max-min fairness scheme from it. We propose a procedure to solve the max-min problem and

Opportunistic Temporal Fair Mode Selection and User Scheduling in Full-Duplex Systems

In-band full-duplex (FD) communication has emerged as one of the promising techniques to improve data rates in next generation wireless systems. Typical FD scenarios considered in the literature assume FD base stations (BSs) and half-duplex (HD) users activated either in uplink (UL) or downlink (DL), where inter-user interference (IUI) is treated as noise at the DL user. This paper considers more general FD scenarios where an arbitrary fraction of the users are capable of FD and/or they can perform successive interference cancellation (SIC) to mitigate IUI. Consequently, one user can be activated in either UL or DL (HD-UL and HD-DL modes), or simultaneously in both directions requiring self-interference mitigation (SIM) at that user (FD-SIM mode). Furthermore, two users can be scheduled, one in UL and the other in DL (both operating in HD), where the DL user can treat IUI as noise (FD-IN mode) or perform SIC to mitigate IUI (FD-SIC mode). This paper studies opportunistic mode selection and user scheduling under long-term and short-term temporal fairness in single-carrier and multi-carrier (OFDM) FD systems, with the goal of maximizing system utility (e.g. sum-rate). First, the feasible region of temporal demands is characterized for both long-term and short-term fairness. Subsequently, optimal temporal fair schedulers as well as practical low-complexity online algorithms are devised. Simulation results demonstrate that using SIC to mitigate IUI as well as having FD capability at users can improve FD throughput gains significantly especially, when user distribution is concentrated around a few hotspots.

Noncoherent Massive Random Access for Inhomogeneous Networks: From Message Passing to Deep Learning

Massive machine-type communications (mMTC) are expected to support a large amount of randomly deployed users for short package transmissions. Noncoherent random access provides an efficient and practical multi-access protocol for mMTC, and also poses new challenges for the receiver design. In this paper, we leverage two well-known methods, i.e., message passing and deep learning, to jointly detect the user activity and the desired data for the noncoherent mMTC. First, by exploiting the exact distribution information of the received signal, a generalized approximate message passing (GAMP)-based algorithm is proposed, which is shown to jointly detect the user activity and the desired data by two modules: inter-user interference elimination and data detection for each user. Inspired by the two-module GAMP-based algorithm, we then propose a model-driven deep learning method, which utilizes the deep neural networks (DNNs) to approximate both the two modules. The loss function for training the DNNs is derived by formulating the two-module detection as an unconstrained optimization problem. Simulation results reveal that the proposed GAMP-based algorithm outperforms the proposed deep learning method when the channel distribution is perfectly known, while it suffers from a significant performance degradation for the case with imperfect channel distribution information.

Joint Optimization of Received Signal Power and Signal Space Dimensions for MIMO Broadcast Channels

In Multiple-input multiple-output (MIMO) broadcast channels (BCs), the transmitter simultaneously broadcasts signals to multiple receivers at same frequency band, resulting in that the communication capacity is affected by both interference, channel fading and random noise. Although channel fading can be mitigated by transceivers, the signal-to-noise-ratio (SNR) of the practical communication system is still dynamically changing due to the randomly changing noise. Traditional MIMO transceiver optimization algorithms can not flexibly adapt to the dynamic changes of SNR, resulting in large performance degradation. In this paper, we comprehensively consider signal power and signal space dimensions of the received signal in MIMO BCs, and propose two transceiver optimization algorithms which can dynamically adapt to the variance of SNRs. In the proposed algorithms, SNR is adopted to be an adjustment factor to cope with its variance. When SNR is low, i.e, large noise, the algorithm parameters are automatically adjusted so that the signal power is preserved as much as possible to combat the loss of communication capacity caused by large random noise. Correspondingly, under the condition of high SNR environment, the algorithm parameters are adjusted automatically to effectively compress the inter-user-interference (IUI) and intra-user-inter-stream-interference (ISI) by optimizing signal space dimensions. Simulation results show that in different SNR environments, the proposed algorithms can automatically adjust the focus of optimization, so that the optimization of signal power and signal space dimensions can automatically adapt to different SNRs. Compared with traditional transceiver optimization algorithms, the proposed algorithms can improve the communication capacity within a large dynamic range of SNR.

Investigation of pattern division multiple access technique in wireless communication networks

Recently, <span>pattern division multiple access (PDMA) is a non-orthogonal multiple access system that is now being developed in next-generation telecoms to address the requirement for mass connectivity. The core premise of non-orthogonal multiple access is to simultaneously serve multiple users with varying power levels across the same spectrum resources such as time, frequency, code, as well as space with minimal inter-user interference. A simulation analysis of significant technology enhancements focusing on PDMA aims to describe the benefits of the two plans now being examined by the third-generation partnership project for 5G technologies, namely filtered orthogonal frequency division multiplexing (F-OFDM) and windowed orthogonal frequency division multiplexing (W-OFDM), and to compare them to alternative modulation processes such as 16, 32, and 128 modulations. The research results explained the PDMA is less bit error rate used in multiple access technologies compare with W-OFDM and F-OFDM.</span>

Improving the Performance of Cell-Edge Users in 6G and Beyond Networks by Utilizing a Novel Precoding-based Hybrid CoMP Transmission Design

The authors propose a novel hybrid coordinated multi-point (H-CoMP) design for cell-edge users in 6G networks to combat inter-cell interference (ICI), inter-user interference (IUI), and improve spectral efficiency via using a new, novel channel-based precoding-based approach.

Deep learning-based transceiver design for multi-user MIMO systems

Multi-user multiple-input multiple-output (MIMO) is a key technique to increase both the channel capacity and the number of users that can be served simultaneously. One of the main challenges related to the deployment of such systems is the complexity of the transceiver processing. Although the conventional optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In contrast to existing work, we study a DL-based transceiver design scheme for a downlink MIMO broadcasting channel (MIMO BC) system, which consists of a base station (BS) serving multi-users. The objective of this work is to maximize the sum-rate of all users by jointly optimizing the transmitter and receivers under the total power constraint, while suppressing interference as much as possible. Due to the inter-user interference in such system, the considered problem is nonconvex and NP-hard. Different from traditional optimization algorithms, we rely on the convolutional neural networks (CNNs) to optimize the transceivers in an adaptive way. In the proposed scheme, we develop an unsupervised learning strategy, where a loss function is constructed innovatively for reducing the inter-user interference. Simulation results show that the inter-user interference is reduced effectively by our proposed CNN-based transceiver optimization method.

Favorable Propagation for Massive MIMO With Circular and Cylindrical Antenna Arrays

Massive MIMO systems with uniform circular and cylindrical antenna arrays are studied. Favorable propagation property is rigorously shown to hold asymptotically in LOS environment for a fixed antenna spacing and under some mild conditions. The analysis is based on a novel method using a Bessel function expansion, a new bounding technique and a simplified representation of inter-user interference for array geometries obeying Kronecker structure.

Hybrid Precoding-Based Millimeter Wave Massive MIMO-NOMA Systems

A symmetry-based hybrid precoder and combiner is a high spectral efficiency structure in millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system. To improve the spectral efficiency of the mmWave mMIMO-NOMA system, we first propose a user grouping scheme to suppress the strong inter-user interference caused by NOMA, then the hybrid precoder based on user channel alignment and the zero-forcing algorithm is constructed to further improve the signal-to-interference-plus-noise ratio (SINR) of the receiver. Subsequently, the non-convex spectral efficiency optimization problem is transformed into a convex optimization problem of inter-cluster power allocation and the closed-form solution for the optimal power under the minimum rate constraint is obtained by solving the KKT condition to further improve the spectral efficiency. The simulation results show that the proposed scheme can achieve higher spectral efficiency compared to orthogonal multiple access (OMA), fixed power allocation (FPA), K-means, and cluster head selection (CHS).

Latency and energy aware rate maximization in MC-NOMA-based multi-access edge computing: A two-stage deep reinforcement learning approach

Future network services are emerging with an inevitable need for high wireless capacity along with strong computational capabilities, stringent latency and reduced energy consumption Two technologies are promising, showing potential to support these requirements: multi-access (or mobile) edge computing (MEC) and non-orthogonal multiple access (NOMA). While MEC allows users to access the abundant computing resources at the edge of the network, NOMA technology enables an increase in the density of a cellular network. However, integrating NOMA technology into MEC systems faces challenges in terms of joint offloading decisions (remote or local computation) and inter-user interference management. In this paper, with the objective of maximizing the system-wide sum computation rate under latency and energy consumption constraints, we propose a two-stage deep reinforcement learning algorithm to solve the joint problem in a multicarrier NOMA-based MEC system, in which the first-stage agent handles offloading decisions while the second-stage agent considers the offloading decisions to determine the resource block assignments for users. Simulation results show that compared with other benchmark algorithms, the proposed algorithm improves the sum computation rate while meeting the latency and energy consumption requirements, and it outperforms the approach in which a single agent handles both offloading decisions and resource block assignments due to faster convergence performance.

Near-Field Modeling and Performance Analysis for Multi-User Extremely Large-Scale MIMO Communication

This letter studies the near-field modeling and performance analysis for multi-user extremely large-scale multiple-input multiple-output (XL-MIMO) communication. With the spherical wavefront phase modeling, and by taking into account the variations of signal amplitude and projected aperture across array elements, the performance of the three typical beamforming schemes are investigated, namely the maximal-ratio combining (MRC), zero-forcing (ZF), and minimum mean-square error (MMSE) beamforming. Compared to existing uniform plane wave (UPW) model where inter-user interference (IUI) can only be suppressed in angular domain, XL-MIMO enables a new degree-of-freedom (DoF) for IUI suppression by distance separation, even for users along the same direction. Simulation results are provided to validate the modeling and performance analysis of multi-user XL-MIMO communications.

Exploiting Cache-Free/Cache-Aided TWR-NOMA in Cognitive Hybrid Satellite-Terrestrial Networks

In this paper, we consider an underlay cognitive hybridsatellite-terrestrial network (CHSTN) which constitutes a primary satellite source communicating with its multiple terrestrial primary receivers and two non-orthogonal multiple access (NOMA) secondary terrestrial users exchanging their information with the help of a half-duplex decode-and-forward based secondary relay. We demonstrate there exists an inevitable inter-user interference (IUI) due to the NOMA scheme, which causes an adverse effect on the performance of the secondary network. Hence, to achieve the improved performance and subsequently the low latency requirements, the wireless content caching is employed, whereby the relay can store the most popular contents of both the NOMA users. Further, the pertinent hybrid channels are characterized by the shadowed-Rician fading and Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ {m}$</tex-math></inline-formula> fading models. Hereby, exploiting the mutual interference between primary and secondary networks, and the realistic assumption of NOMA-based imperfect successive interference cancellation, we analyze the performance of CHSTN for the schemes viz., cache-free (CF) two-way relay (TWR) NOMA and cache-aided (CA) TWR-NOMA, while assessing the outage probability (OP), throughput, and average transmission time. Also, we carry out the asymptotic OP analysis at a high signal-to-noise ratio to present the insights on the attainable diversity orders. We manifest that zero diversity order results for both the schemes due to unavoidable IUI. However, one can anticipate the remarkable enhancement in the performance for CA TWR-NOMA scheme over its CF TWR-NOMA counterpart, owing to the reduced IUI and the efficient utilization of available spectrum resources.