International audience

This paper examines radio resource allocation in a full-duplex (FD) reconfigurable intelligent surface (RIS)-assisted wireless-powered (WP) industrial internet-of-Things (IIoT) communication network. The system model includes an FD access point (FD-AP) that communicates with FD energy harvesting (EH) mobile users (MUs), referred to as an FD-FD RIS-assisted WP-IIoT network. The FD-AP is equipped with two sets of multiple antennas: one set for downlink (DL) energy beamforming and another for uplink (UL) information signal reception. The FD-MUs have one antenna for DL energy signal reception and another for UL information transmission. This paper addresses the UL sum-rate maximization problem for the FD-FD RIS-assisted WP-IIoT system by jointly optimizing the RIS phase shift and power resources. Additionally, this paper presents two benchmarks as specific cases of the FD-FD RIS-assisted WP-IIoT system model, namely, (i) FD-HD RIS-assisted WP-IIoT network: FD AP and half-duplex (HD) two MUs groups, and (ii) HD-HD RIS-assisted WP-IIoT network: HD AP and HD MUs. The numerical results demonstrate that the FD-FD system outperforms both benchmarks in the low transmit power regime. Also, compared to the baseline non-RIS-assisted WP-IIoT network, the RIS-assisted WP-IIoT network achieved a significant UL sum-rate gain.

Integrating rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into vehicular communication systems for the Internet of vehicles (IoV) offers significant potential for enhancing quality of service, but also introduces challenges in radio resource allocation due to increased system complexity and the heterogeneous movement patterns of vehicles. In this paper, we propose an IoV communication system that leverages partitioned antenna arrays, RIS elements, and subarray-subsurface-vehicle mapping to reduce computational complexity, with RSMA serving multiple vehicles and users therein. This partitioning and mapping approach, combined with a zero-forcing technique that decouples precoding at the base station (BS) and decoding at the user equipment (UE) into a zero-forcing factor and a precoding/decoding factor, effectively mitigates inter-vehicular interference. For sum-rate maximization, we develop an alternating optimization algorithm that incorporates a dynamic partitioning scheme, which divides the BS antennas and RIS elements into subsets and maps them to individual vehicles for transmitting common and private streams to UEs therein. The passive beamforming for each RIS subsurface is optimized using the Riemannian conjugate gradient (RCG) method, while precoding at the BS subarrays and decoding at the UEs are optimized using a weighted minimum mean square error (WMMSE) approach. Simulation results and comparisons with benchmarks demonstrate that the proposed solution achieves robust system sum rate while significantly reducing computational complexity.

International audience

Backscatter communication has evolved, but the research interest has predominantly focused on a single carrier radio frequency source. In this paper, we propose a joint Repetition Coding (RC) and Multi-Source Temporal Combining (TC) framework to enhance the reliability and throughput of backscatter communication. Unlike conventional approaches that rely on a single carrier source or uniform repetition coding, our method dynamically assigns position-aware repetition codes while leveraging multiple ambient RF sources to exploit temporal diversity. We developed an analytical model to evaluate the bit error rate (BER) performance under different coding strategies and source distributions. Furthermore, we derive a closed-form optimization framework for adaptive weight allocation in TC, ensuring that the contribution of each RF source is weighted based on SNR, coherence time, and estimated BER.

In this paper, we propose a rate-splitting multiple access (RSMA) communication system assisted by multiple reconfigurable intelligent surface (RIS) devices, incorporating in-band over-the-air (OTA) control and feedback links for the RISs, and develop a corresponding radio resource allocation algorithm. To facilitate in-band OTA communication, each RIS is divided into two subsets: one dedicated to reflecting data signals from the base station (BS) toward users, and the other allocated for transmitting RIS feedback signals back to the BS. We present an alternating optimization approach that jointly optimizes the beamforming vectors at the BS and the reflection coefficients of the RISs. Specifically, our solution employs the weighted minimum mean square error (WMMSE) algorithm combined with a projection method to satisfy the RIS control rate constraints, while leveraging the Riemannian conjugate gradient algorithm for updating RIS reflection coefficients. Extensive simulation results demonstrate that the proposed scheme achieves robust and stable user sum-rate performance while effectively supporting the OTA control and feedback signaling. Additionally, the results provide insights into the impacts of RIS subset sizes, RIS control rate constraints, and BS transmit power on overall network performance.

To highlight the potential of RSMA and multicarrier (MC) techniques in vehicle-to-everything (V2X)communication, we propose and present a two-way communication MCRSMA V2X communication system that supports a large number of equipments (UEs) and promotes the dual function of sidelink and cellular communication. We employ$\mathbf{k}$-mean clustering for vehicle grouping and a suboptimal sub-carrier allocation with naive equal time and optimal precoder design for data transfer. To confirm the efficacy of the proposed system model, we compare the MC-RSMA solution against an MC-SDMA benchmark, using sum-rate as the performance metric. From the simulation results, the proposed system demonstrates significant improvements over the two multi-access technique benchmarks.

This paper investigates a full-duplex (FD) reconfigurable intelligent surface (RIS)-assisted wireless powered communication network (WPCN) in which FD multi-user (FD-MUs) industrial internet-of-things (IIoT) sensor devices with energy harvesting (EH) capabilities communicate with an FD hybrid-access point (FD-AP). The FD-AP with dual-set of multiple antennas use a set of antennas to beamform energy in downlink (DL) transmission and the other set of antennas to receive information signals in uplink (UL) transmission from the dual-set of single antenna FD-MUs. The FD-MUs use one single antenna to receive energy signals, while the other single antenna is used to transmit information. For comparison, two special cases of the FD-FD RIS-assisted WPCN IIoT network are considered. The first scenario considers an FD-HD (half-duplex) RIS-assisted WPCN IIoT network made up of a FD-AP and HD-MUs while the second comprises an HD-HD RIS-assisted WPCN IIoT network consisting of HD-AP and HD-MUs. We investigate the sum-rate performance of the FD-FD system with random IRS phase shifts and compare it to FD-HD and HD-HD system models. It is shown from simulation results that the FD-FD system outperforms the two special cases in the low transmit power regime.

This paper presents a three-tier (3T) symbiotic radio device-to-device (D2D) routing Internet-of-Things network. The primary network (PN) consists of a base station routing data through multiple single antenna decode-and-forward sensor node(s) (DSNs) and to the destination sensor node. The secondary network (SN) consists of single-antenna active sensor tag nodes (ASN) transmitting sensed data to a multi-antenna reader using the backscatter (BC) technique. The tertiary network (TN) consists of single antenna semi-passive energy harvesting sensor tags (SSN) that transmit sensed data using BC to the same multi-antenna reader. The symbiotic nature exists where the ASNs and SSNs utilize the DSNs' radio frequency for data transfer. At the same time, the PN benefits from ASNs and SSNs assistive data transfer in terms of spatial diversity. In this work, we focus on maximizing the EE for the proposed 3T SRN D2D-routing by optimizing the DSN and ASN power allocation and the SSN reflection co-efficient. Based on the optimization problem, a minimum distance-based and a maximum channel gain-based D2D-routing algorithms are proposed. The proposed channel-gain-based 3T SRN D2D-routing algorithm is shown to be superior to the distance-based algorithm.

Precision agriculture in rural areas often faces challenges in communication due to the limited availability, robustness and power-constrained devices of existing infrastructure. The Constrained Application Protocol (CoAP), particularly when it is run over the User Datagram Protocol (UDP) transport layer, offers a lightweight and efficient communication solution for resource-constrained environments prevalent in precision agriculture. Therefore, this work focuses on the implementation of CoAP over UDP to bypass modem firmware and rely on the Zephyr operating system, providing a more accessible and scalable communication model for energy-efficient precision agricultural systems. The CoAP over UDP protocol implementation results highlight the practical advantages of CoAP for energy efficiency and reduced latency, particularly in scenarios with limited connectivity or high packet loss conditions. In addition, byte overhead analysis reveals a substantial reduction in overhead with CoAP, especially in small payload scenarios

In this paper, we develop an optimization framework for the symbiotic operation of a primary multi-vehicle-to-everything (MV2X) communication network employing downlink communication and a secondary backscatter (BC) communication (BackCom) network of wireless-powered sensor equipped reflecting configurable surfaces (RISs), sharing the same spectrum. The secondary semi-passive RISs provide the multiple primary receivers with a spatial diversity gain and enable transmitting their own data to a designated IoT central system equipped with a BC reader. We develop an alternating optimization algorithm for resource allocation to maximize the sum-rate for mutually beneficial symbiotic radio operation. The presented simulation results demonstrate the superiority of the proposed optimization algorithm over conventional beamforming designs and fixed RIS reflection coefficient benchmarks.

Symbiotic radio networks (SRN) have gained traction for efficient spectrum usage in wireless communication Internet-of- Things networks (IoTNs) applications. Furthermore, the efficient use of energy resources under energy-efficient communication is rising in IoTNs to satisfy and attain the United Nations' sustainable development goals on sustainable and renewable energy usage. Hence, this work focuses on energy and spectrum efficient usage through renewable radio frequency (RF) energy harvesting (EH) backscatter (BC) communication (BackCom) and SRN between a secondary network sensor-equipped EH intelligent reflective surface BC and a primary network base station to multiple user equipment using multiple access communication. This work focuses on resource allocation optimization to maximize the system energy-efficiency quality-of-service for the SRN. The superiority of the proposed scheme over existing benchmark schemes is also presented in this work.

This paper analyzes the spectral/energy efficiency (SE/EE) of a full-duplex (FD) cell-free (CF) massive multiple-input multiple-output (mMIMO) over Rician fading channels. Due to the FD radios, the access points (APs) suffer from self-interference (SI) and inter-AP interference (IAI) while the downlink (DL) users’ signals are corrupted by the uplink (UL) users’ transmissions. We consider the case, where low-resolution analog-to-digital converters (ADCs) are utilized at the APs and DL users, which introduces the quantization noise (QN). The combined effects of Rician$\kappa $-factor, residual SI/IAI, UL-to-DL interference, multi-user interference, and QN on the UL/DL SEs are characterized. The UL SE is degraded by the increase in DL power, whereas the growth in UL power deteriorates the DL SE. The effects of the residual SI/IAI and UL-to-DL interference are worsened by the low-resolution ADCs. We optimize the UL/DL transmit powers to maximize the overall sum SE of the network. It is observed that the proposed power allocation algorithm brings substantial sum SE gain. A trade-off analysis between the EE and SE, as a function of the ADCs’ resolution, shows that the entire envelope of the operating region of FD CF mMIMO is enhanced in Rician channels.

For a self-sustaining wireless communication system in the Internet-of-Things (IoT) networks, energy harvesting (EH) can be implemented at each user node as a constant renewable power supply source. Hence, an investigation into the use of wireless-powered communication network (WPCN) protocols to facilitate communication between an access point (AP) and multiple mobile users (MUs) is presented in this article. The AP has multiple antennas and operates in the full-duplex (FD) mode. The MUs, on the other hand, have single antennas and works in the half-duplex (HD) mode. Each MU communicating with the FD-AP is assigned to one of two groups, based on the time allocation and channel access for either uplink (UL) or downlink (DL) communication. The channel assignment, time resource, and power resource allocations are optimized to maximize the UL weighted sum rate. The sum-rate optimization problem is found to be nonconvex. Therefore, an iterative algorithm is investigated to optimize the UL weighted sum rate of the proposed FD-WPCN system. Next, the proposed FD-WPCN algorithm is modified for HD-WPCN-enabled communication between the AP and multiple MUs. Extensive simulations are conducted to verify the proposed algorithm for FD-WPCN and compare its performance with the HD-WPCN counterpart. From the simulation results, FD-WPCN outperformed HD-WPCN at a low AP transmit signal-to-noise ratio (SNR) region. The opposite behavior is observed for high AP transmit SNR due to increasing residual self-interference at the FD-AP.

A study of a multi-hop decode-and-forward (DF) simultaneous wireless information and power transfer (SWIPT) sensor network system is presented in this work. In the studied system model, a source communicates with a destination through the aid of multi-hop relays which harvest energy from their received signals. We apply power splitting (PS) based SWIPT relaying protocols for the relays harvesting energy. Focused on DF relaying protocol, we aim to minimize the transmit power at the source under a set end-to-end throughput constraint by optimizing PS ratios at the relays. Based on convex optimization techniques, the globally optimal PS ratio solution is obtained as a closed-form solution. Numerical results demonstrate the efficacy of the proposed optimal design over the conventional fixed PS ratio scheme.

This article studies a multihop decode-and-forward (DF) simultaneous wireless information and power transfer (SWIPT) system where a source sends data to a destination with the aid of multihop relays which do not depend on an external energy source. To this end, we apply power splitting (PS)-based SWIPT relaying protocol so that the relays can harvest energy from the received signals from the previous hop to reliably forward the information of the source to the destination. We aim to solve two optimization problems relevant to our system model. First, we minimize the transmit power at the source under the individual quality-of-service (QoS) threshold constraints of the relays and the destination nodes by optimizing PS ratios at the relays. The second is to maximize the minimum system achievable rate by optimizing the PS ratio at each relay. Based on the convex optimization techniques, the globally optimal PS ratio solution is obtained in closed-form for both problems. By setting the QoS threshold constraint, the same for each node for the source transmit power problem, we discovered that either the minimum source transmit power or the maximum system throughput can be found using the same approach. Numerical results demonstrate the superiority of the proposed optimal SWIPT PS design over conventional fixed PS ratio schemes.

Simultaneous wireless information and power transfer (SWIPT) is investigated in amplify-and- forward (AF) relay systems. Each relay node facilitates communication between a source node and a destination node. The relay nodes are equipped with radio frequency (RF) energy harvesters to supply power for retransmission of information signals from the source node to the destination node. In this paper, both power splitting (PS) ratio and adaptive power control are jointly optimized for optimal SWIPT multiple- relay systems performance. We demonstrate that the optimal power allocation strategy in joint optimization problem is to use full harvested power. We propose both an optimal centralized PS scheme and a distributed suboptimal scheme suitable for practical implementation. Simulation results demonstrate that the proposed schemes outperform naive PS schemes in terms of the average rate and the bit-error rate.

In this paper, we investigate a wireless powered communication network (WPCN) protocol in which mobile stations (MS) with energy harvesting (EH) communicate with a full-duplex access point (FD- AP). The FD-AP is equipped with multiple antennas, while MSs have a single antenna and operate in half-duplex (HD) mode. The MSs are assigned two groups according to the time slot and their access channel state for either uplink or downlink communication with the FD-AP. Channel assignment, time resource allocation, and power allocation is jointly optimized for maximizing the sum-rate. An iterative algorithm based on the relationship between rate and minimum mean squared error (MMSE) is proposed to maximize the sum-rate by examining the Lagrangian problem. The proposed algorithm for FD-WPCN is compared to its HD counterpart. It is shown from simulation results that the proposed FD-WPCN scheme outperforms the HD mode when low transmit power is used although the FD-WPCN performance is degraded at high transmit power regime due to the presence of residual self- interference (RSI) at the FD-AP node.