International audience

The Internet of Things (IoT) is a major driving use case of future communication systems. 5G and beyond networks should be properly designed to meet the quality of service (QoS) requirements of IoT devices in terms of massive access, data rates, latency and reliability. In this paper, we study the uncoordinated spectrum and power allocation problems in an uplink IoT network. In the considered setting, IoT devices aim at organizing their transmissions, without any coordination, on the available frequency channels and power levels. To enhance performance, we propose the deployment of full-duplex relays as well as the use of non-orthogonal multiple access and queuing at the level of the relays. A new algorithm, based on the multi-player multi-armed bandits framework, is proposed with the aim of reducing the transmit power of IoT devices. Simulation results validate the convergence of the proposed algorithm. Results also confirm the superior performance of the proposed technique in terms of satisfying QoS requirements and reducing the needed user transmit power when compared to two baseline schemes.

Recently, Extreme Learning Machine (ELM) started gaining interest among researchers in wireless communications as an online training solution for machine learning based receivers. ELM has proven to provide high training speed and global optimization capabilities. However, the number of needed training pilots is still relatively high and increases rapidly with the number of subcarriers, thus rendering its deployment impractical. In this paper, we propose subcarrier-wise ELM receivers that are robust to the increase in the number of used subcarriers; we then extend them to exploit adjacent channel knowledge, hence providing superior performance in frequency selective channels. In addition, we propose a novel training architecture based on interpolated training that saves more than 50% of the computational and spectral resources of conventional ELM receivers. We show the robustness of the proposed technique in different channel scenarios and OFDM settings by means of both practical channel measurements and numerical simulations.

Device to device (D2D) and nonorthogonal multiple access (NOMA) are promising technologies to meet the challenges of the next generations of mobile communications in terms of network density and diversity for Internet of Things (IoT) services. This article tackles the problem of maximizing the D2D sum throughput in an IoT system underlaying a cellular network, through optimal channel and power allocation (PA). NOMA is used to manage the interference between cellular users (CUs) and full-duplex (FD) IoT devices. To this aim, mutual successive interference cancellation (SIC) conditions are identified to allow simultaneously the removal of the D2D devices interference at the level of the base station and the removal of the CUs interference at the level of D2D devices. To optimally solve the joint channel and PA problem, a time-efficient solution of the PA problem in the FD context is elaborated. By means of graphical representation, the complex nonconvex PA problem is efficiently solved in constant time complexity. This enables the global optimal resolution by successively solving the separate PA and channel assignment problems. The performance of the proposed strategy is compared against the classical state-of-the-art FD and half-duplex (HD) scenarios, where SIC is not applied between CUs and IoT devices. The results show that important gains can be achieved by applying mutual SIC NOMA in the IoT-cellular context, in either HD or FD scenarios.

To achieve better connectivity in future communication networks, the deployment of different types of access points (APs) is underway. APs are expected to be equipped with self-organizing capabilities to reduce costs. Moreover, due to the spectrum crunch, frequency reuse among the deployed APs is inevitable, exacerbating the problem of inter-cell interference (ICI). Therefore, ICI mitigation in self-organizing networks (SONs) is commonly identified as a key radio resource management mechanism to enhance performance. To this end, this paper proposes a novel solution for the uncoordinated channel and power allocation problems. Based on the multi-armed bandits (MAB) framework, the proposed technique does not require any communication between the APs. The case of varying channel rewards across APs is considered. In contrast to previous work on channel allocation using the MAB framework, APs are permitted to choose multiple channels for transmission. Moreover, non-orthogonal multiple access is used, allowing multiple APs to access each channel simultaneously. This results in an MAB model with varying channel rewards, multiple plays and non-zero reward on collision. The proposed algorithm has an expected regret in the order ofO(log2T), with extensive numerical results revealing it significantly outperforms a well-known baseline algorithm in terms of energy efficiency.

This paper studies the combination of full-duplex (FD) and half-duplex (HD) device-to-device (D2D) communications while underlaying a cellular system. Non-orthogonal multiple access (NOMA) is used to manage the interference between devices and cellular users using mutual successive interference cancellation (SIC) and to boost the performance of D2D underlay systems, which are mainly interference-limited. For this purpose, we derive the conditions allowing for the application of mutual SIC in FD-D2D and HD-D2D systems. We compare the performance of the proposed strategy against state-of-the-art, where SIC is not applied between D2D devices and cellular users. The results show that important gains can be achieved by using NOMA in this context and highlight the importance of SIC factors for determining the best transmission mode.

In this paper, an algorithm based on the multiplayer multi-armed bandit (MAB) framework is proposed to solve an uncoordinated spectrum access problem. The proposed technique does not require any communication or coordination between users. The case of varying channel rewards across users is considered. In contrast to previous work, the users are permitted to choose multiple channels for transmission, resulting in a MAB model with multiple plays. The proposed algorithm has an expected regret of the order O(log2T), which is validated by simulation results.

The densification of mobile networks should enable the fifth generation (5G) mobile networks to cope with the ever increasing demand for higher rate traffic, reduced latency, and improved reliability. The large scale deployment of small cells and distributed antenna systems in heterogeneous environments will require more elaborate interference mitigating techniques to increase spectral efficiency and to help unlock the expected performance leaps from the new network topologies. Coordinated multi-point (CoMP) is the most advanced framework for interference management enabling the cooperation between base stations to mitigate inter-cell interference and boost cell-edge user performance. In this paper, we study the combination of CoMP with mutual SIC, an interference cancellation technique based on power-domain non-orthogonal multiple access (NOMA) that enables multiplexed users to simultaneously cancel their corresponding interfering signals. A highly efficient inter-cell interference cancellation scheme is then devised, that can encompass several deployment configurations and coordination techniques. The obtained results prove the superiority of this approach compared to conventional NOMA-CoMP systems.

In this paper, we study the traffic-aware resource allocation problem for a system with mixed traffic types. The considered framework encompasses real-time (RT) users having strict QoS requirements (in terms of amount of data and latency), and best-effort (BE) users for which the system tries to strike a balance between throughput and fairness. The resource allocation problem is studied in different contexts: orthogonal and non-orthogonal multiple access (OMA and NOMA respectively) in either centralized or distributed antenna systems (CAS and DAS respectively). Following the formulation of the resource optimization problem, we propose a low complexity suboptimal solution based on matching theory for each system context. We also propose an iterative approach to determine the number of subbands per antenna for the DAS contexts. The proposed techniques aim at guaranteeing the requirements of RT users while maximizing the utility function of BE users. Simulation results show that the proposed allocation method based on matching theory greatly outperforms a previously proposed greedy approach, especially in terms of RT users satisfaction.

Unmanned aerial vehicle (UAV)-base stations (BS) are envisioned to provide wireless access to areas where the existing wireless infrastructure is either not deployed, damaged or simply congested. In this paper, the problem of minimizing the transmit power of a UAV-BS, while serving users with their QoS requirements and accounting for the wireless backhaul limitation of the UAV, is investigated. To this end, an algorithm that finds the optimal bandwidth assignment in the backhaul link, the optimal 3D position of the UAV, as well as the transmit power distribution in the access and the backhaul links is proposed. Simulation results show that, using the proposed approach, the needed power is greatly reduced, when compared to a strategy that separates between the frequency band used in the access and the backhaul links. In addition, the performance enhancement of a UAV-enabled system over a traditional one in which the macro base station (MBS) directly serves users is shown.

This paper proposes a new family of recursive systematic convolutional codes, defined in the non-binary domain over different Galois fields GF(q) and intended to be used as component codes for the design of non-binary turbo codes. A general framework for the design of the best codes over different GF(q) is described. The designed codes offer better performance than the non-binary convolutional codes found in the literature. They also outperform their binary counterparts when combined with their corresponding QAM modulation or with lower order modulations.

This paper proposes a new traffic-aware resource allocation technique, employing non-orthogonal multiple access (NOMA) in a downlink distributed antenna system (DAS). The studied framework consists of users with mixed traffic types: real-time (RT) users having strict QoS requirements (in terms of amount of data and latency), and best-effort (BE) users aiming to maximize their throughput and fairness. After formulating the resource and power optimization problem, we propose a low complexity sub-optimal algorithm that aims at guaranteeing the requirements of RT users while maximizing the utility function of BE users. Simulation results show a remarkable performance enhancement of the proposed algorithm over baseline techniques in terms of RT users satisfaction. Also, the proposed technique achieves near optimal fairness for BE users while maximizing their average throughput.

A weighted proportional fair (PF) scheduling method is proposed in the context of nonorthogonal multiple access (NOMA) with successive interference cancellation (SIC) at the receiver side. The new scheme introduces weights that adapt the classical PF metric to the NOMA scenario, improving performance indicators and enabling new services. The distinguishing value of the proposal resides in its ability to improve long‐term fairness and total system throughput while achieving a high level of fairness in every scheduling slot. Finally, it is shown that the additional complexity caused by the weight calculation has only a limited impact on the overall scheduler complexity, while simulation results confirm the claimed improvements, making the proposal an appealing alternative for resource allocation in a cellular downlink system.

This paper considers the problem of Base Station power minimization in downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. It aims at providing new solutions for the reduction of the total energy consumption. Such solutions rely on joint subcarrier and power allocation, under the constraints of requested per-user data rate. Practical results show that the proposed techniques allow a significant decrease of transmission power with an affordable complexity, compared to well-referenced methods from the literature.

Non-Orthogonal Multiple Access (NOMA) has proven itself as a serious candidate for multiple access schemes in the 5thgeneration of wireless communication systems. In this paper, we propose new resource allocation techniques that allow the coupling of NOMA with multiple-input-multiple-output (MIMO) systems. As in most of downlink NOMA systems, the proportional fairness (PF) scheduler is used. As for power allocation (PA), most previous works assume an equal PA between antennas and subbands, which is sub-optimal. In this paper, we first propose a technique to reduce the complexity of the PF scheduler, and then we introduce an iterative waterfilling-based power allocation to the Single-User MIMO (SU-MIMO) case. Extensive simulations show that the novel PA technique improves the quality of experience of users suffering from bad channel conditions, as well as the user fairness.

This paper investigates a new strategy for radio resource allocation applying a non-orthogonal multiple access (NOMA) scheme. It calls for the cohabitation of users in the power domain at the transmitter side and for successive interference canceller (SIC) at the receiver side. Taking into account multi-user scheduling, subband assignment and transmit power allocation, a hybrid NOMA scheme is introduced. Adaptive switching to orthogonal signaling (OS) is performed whenever the non-orthogonal cohabitation in the power domain does not improve the achieved data rate per subband. In addition, a new power allocation technique based on waterfilling is introduced to improve the total achieved system throughput. We show that the proposed strategy for resource allocation improves both the spectral efficiency and the cell-edge user throughput. It also proves to be robust in the case of communications in crowded areas.

The side information (SI) in Distributed Video Coding (DVC) is estimated using the available decoded frames and exploited for the decoding and reconstruction of other frames. The quality of the SI has a strong impact on the performance of DVC. Here, we propose a new approach that combines both global and local SI to improve coding performance. Since the background pixels in a frame are assigned to global estimation and the foreground objects to local estimation, one needs to estimate foreground objects in the SI using the backward and forward foreground objects, the background pixels are directly taken from the global SI. Specifically, elastic curves and local motion compensation are used to generate the foreground objects masks in the SI. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.39 dB for a group of picture (GOP) size of 2, and up to 4.73 dB for larger GOP sizes, with respect to the reference DISCOVER codec.

In this paper, we propose a semiparametric regression model that relates the received signal strength indicators (RSSIs) to the distances separating stationary sensors and moving sensors in a wireless sensor network. This model combines the well-known log-distance theoretical propagation model with a nonlinear fluctuation term, estimated within the framework of kernel-based machines. This leads to a more robust propagation model. A fully comprehensive study of the choices of parameters is provided, and a comparison to state-of-the-art models using real and simulated data is given as well.

In the last decade, an important research effort has been dedicated to implement objective image quality assessment metrics that reflect effectively human perception. Therefore, the aim of this paper is to propose new objective metrics that fulfill the demands of the image quality assessment field. For this sake, we propose two main full-reference (FR) quality metrics, and then adapt them in such a way to constitute several new reduced-reference (RR) quality metrics, for the case where the complete reference image is not available. We evaluate the influence of five types of distortion such as JPEG, JPEG2000, Gaussian Blur, AWGN, and Contrast change, on the image quality. The proposed metrics are based on the number of Scale-Invariant Feature Transform (SIFT) points, the number of SIFT matches between the unpaired and distorted images, and the Structural Similarity index (SSIM). In order to validate our proposed metrics, we compute the correlation between our metrics' scores and the subjective evaluation results. The results show a high correlation and a better quality range compared to well-known metrics, as well as a good robustness to reduced-reference situations.

Convex optimization problems involving information measures have been extensively investigated in source and channel coding. These measures can also be successfully used in inverse problems encountered in signal and image processing. The related optimization problems are often challenging due to their large size. In this paper, we derive closed-form expressions of the proximity operators of Kullback-Leibler and Jeffreys-Kullback divergences. Building upon these results, we develop an efficient primal-dual proximal approach. This allows us to address a wide range of convex optimization problems whose objective function expression includes one of these divergences. An image registration application serves as an example for illustrating the good performance of the proposed method.

The quality of side information plays a key role in distributed video coding. In this paper, we propose a new approach that consists of combining global and local motion compensation at the decoder side. The parameters of the global motion are estimated at the encoder using scale invariant feature transform features. Those estimated parameters are sent to the decoder in order to generate a globally motion compensated side information. Conversely, a locally motion compensated side information is generated at the decoder based on motion-compensated temporal interpolation of neighboring reference frames. Moreover, an improved fusion of global and local side information during the decoding process is achieved using the partially decoded Wyner-Ziv frame and decoded reference frames. The proposed technique improves significantly the quality of the side information, especially for sequences containing high global motion. Experimental results show that, as far as the rate-distortion performance is concerned, the proposed approach can achieve a PSNR improvement of up to 1.9 dB for a Group of Pictures (GOP) size of 2, and up to 4.65 dB for larger GOP sizes, with respect to the reference DISCOVER codec.

Wireless sensor networks have gained considerable interest in the research community during the last decade. In this paper, we target the lower layers in the protocol stack of wireless sensor networks, for the aim of providing a reliable backbone for multimedia applications where error propagation and relatively high error rates are a major concern. Decoding and fusion techniques are developed and evaluated for multi-hop relay wireless sensor networks incorporating the “Amplify and Forward” and “Decode and Forward” relaying protocols, based on turbo codes. Simulation results show a significant gain in bit error rate performance when the different streams are fused at the relays or clustering nodes, compared to the case where these streams are blended at the final fusion center. Results also show that in situations where transmission conditions do not allow for a good exploitation of multi-stream diversity at the fusion center, the “Amplify and Forward” protocol outperforms “Decode and Forward” in terms of improved BER performance and reduced relay complexity, at the expense of a linear increase in transmission rate.

Stereo matching is an active area of research in image processing. In a recent work, a convex programming approach was developed in order to generate a dense disparity field. In this paper, we address the same estimation problem and pro pose to solve it in a more general convex optimization frame work based on proximal methods. More precisely, unlike previous works where the criterion must satisfy some restrictive conditions in order to be able to numerically solve the minimization problem, this work offers a great flexibility in the choice of the involved criterion. The method is validated in a stereo image coding framework, and the results demonstrate the good performance of the proposed parallel proximal algorithm.

Side information construction in Wyner-Ziv video coding is a sensible task which strongly influences the final ratedistortion performance of the scheme. This side information is usually generated through an interpolation of the previous and next images. Some of the zones of a scene however, such as the occlusions, cannot be estimated with other frames. In this paper we propose to avoid this problem by sending some hash information for these unpredictable zones of the image. The resulting algorithm is described and tested here. The obtained results show the advantages of using localized hash information for the high error zones in distributed video coding.

This paper presents a novel algorithm for content adaptive GOP size control in distributed video coding. The GOP size is dynamically varied along the sequence, depending on motion activity. Automatic mode selection allows the system to switch between H.264 intra-coding and Wyner-Ziv coding modes to optimize the overall performance. Furthermore, the encoder determines a suitable compression ratio for the Wyner-Ziv frames without the need for a feedback channel. Simulation results show significant improvement in the average system performance, compared to fixed GOP Wyner-Ziv and H.264 intra-coding.

This work aims at improving the quality of the side information in distributed video coding. Based on genetic algorithms, our proposed technique combines several frames, interpolated using previously developed methods, in a fusion-based approach. Simulation results show a significant improvement in the side information quality compared to other interpolation techniques available in the literature, which greatly improves the rate-distortion performance of a distributed video codec, where the gain in PSNR can reach 6 dB.

In this paper, we present a rate-distortion analysis for a distributed video coding system, which allows for the selection of a suitable quantization parameter for the H.264 coded key frames, under rate constraints. On the other hand, an adaptive rate allocation and quantization algorithm with a frame dropping strategy is used for the transmission of Wyner-Ziv frames in a multi-user scenario, without the need for a permanent feedback channel. The available bandwidth is dynamically shared between Wyner-Ziv and Key frames. Turbo-codes are used for the joint source-channel coding of the Wyner-Ziv frames and for the protection of the H.264 bitstream from channel errors. Simulation results show the superior performance of the proposed technique, compared to traditional systems with fixed quantization parameters and compression rates.

A novel unequal error protection technique is presented for the transmission of H263 compressed and turbo-coded video sequences over a UMTS/TDD wireless system. The overall redundancy added to the compressed stream is non-uniformly distributed between the succeeding video frames in order to minimize the mean distortion over the transmitted sequence. The repartition of the redundancy on the video stream is optimized using an analytical approach, which aims to alleviate the error propagation along the sequence. Different puncturing patterns of the rate 1/3 turbo-coder were considered in our simulations. Experimental results show that the gain (in terms of the peak signal to noise ratio) over the classical equal error protection scheme can reach 1.5 dB without any increase in the transmission rate or the receiver complexity.

We aim to present a comparative study of the performances, in terms of the mean PSNR (peak signal-to-noise ratio), of a UMTS/TDD transmission system for H.263 compressed and turbo-coded video sequences. Several parameters are varied, among them: the rate of occurrence of intra coded frames; the video compression level; the bit error rate of the transmission channel. We also present a methodology for determining the best scheme for repartition of the global channel bandwidth between source and channel coding, in order to minimize the global distortion on the transmitted sequences.

We study the behaviour of a UMTS/TDD synchronous downlink receiver, under different scenarios. Computer simulations were conducted to compare the overall system performances obtained with turbo coding to the convolutional coded and channel non coded systems. The propagation environment is a six-path channel with Rayleigh fading amplitudes and fixed delays. Moreover, we propose a method for reducing the iteration number via a cyclic redundancy check (CRC), and estimate the channel decoding complexity to be expected, for different channel conditions.