Showing papers in "IEEE Communications Letters in 2022"
TL;DR: In this article , the authors proposed a sub-connected architecture of active RIS, where multiple elements control their phase shifts independently but share a same power amplifier, which significantly reduces the number of power amplifiers for power saving at the cost of fewer degrees of freedom (DoFs) for beamforming design.
Abstract: To overcome the “multiplicative fading effect” introduced by passive reconfigurable intelligent surface (RIS), the concept of active RIS has been recently proposed to amplify the radiated signals. However, the existing fully-connected architecture of active RIS consumes high power due to the additionally integrated active components. To address this issue, we propose the sub-connected architecture of active RIS. Different from fully-connected architecture, where each element integrates a dedicated power amplifier, in the sub-connected architecture, multiple elements control their phase shifts independently but share a same power amplifier, which significantly reduces the number of power amplifiers for power saving at the cost of fewer degrees of freedom (DoFs) for beamforming design. Fortunately, our analysis reveals that performance loss introduced by the sub-connected architecture is slight, indicating that it can achieve much higher energy efficiency (EE). Furthermore, we formulate the EE maximization problem in the active RIS-aided system for both architectures and develop a corresponding joint beamforming design. Simulation results verify the proposed sub-connected architecture as an energy-efficient realization of active RIS.
40 citations
TL;DR: Experimental results show that the proposed STAGCN model can achieve better prediction performance than conventional methods with superior training efficiency.
Abstract: Accurate cellular traffic prediction is challenging due to the complex spatial topology of cellular network and the dynamic temporal feature of mobile traffic. To overcome these problems, this letter proposes a spatial-temporal aggregation graph convolution network (STAGCN), in which the daily historical pattern and the hourly current-day pattern of mobile traffic are modeled. Moreover, the complex spatial-temporal correlation is captured by an aggregation graph convolution network for all nodes across different timestamps. The external factors’ impact on mobile traffic is fed into a regression module at the last step to obtain the predicted traffic. Experimental results show that the proposed model can achieve better prediction performance than conventional methods with superior training efficiency.
33 citations
TL;DR: Numerical results show that RSMA effectively mitigates the effect of pilot contamination in the downlink and achieves a significant performance gain over a conventional cell-free massive MIMO network.
Abstract: This letter focuses on integrating rate-splitting multiple-access (RSMA) with time-division-duplex Cell-free Massive MIMO (multiple-input multiple-output) for massive machine-type communications. Due to the large number of devices, their sporadic access behaviour and limited coherence interval, we assume a random access strategy with all active devices utilizing the same pilot for uplink channel estimation. This gives rise to a highly pilot-contaminated scenario, which inevitably deteriorates channel estimates. Motivated by the robustness of RSMA towards imperfect channel state information, we propose a novel RSMA-assisted downlink transmission framework for cell-free massive MIMO. On the basis of the downlink achievable spectral efficiency of the common and private streams, we devise a heuristic common precoder design and propose a novel max-min power control method for the proposed RSMA-assisted scheme. Numerical results show that RSMA effectively mitigates the effect of pilot contamination in the downlink and achieves a significant performance gain over a conventional cell-free massive MIMO network.
28 citations
TL;DR: In this article , the authors theoretically compare the active reconfigurable intelligent surface (RIS)-aided system with the passive RIS-aided systems and demonstrate that the active RIS would be superior if the power budget is not very small and the number of RIS elements was not very large.
Abstract: This letter theoretically compares the active reconfigurable intelligent surface (RIS)-aided system with the passive RIS-aided system. For a fair comparison, we consider that these two systems have the same overall power budget that can be used at both the base station (BS) and the RIS. For active RIS, we first derive the optimal power splitting between the BS’s transmit signal power and RIS’s output signal power. We also analyze the impact of various system parameters on the optimal power splitting ratio. Then, we theoretically and numerically compare the performance between the active RIS and the passive RIS, which demonstrates that the active RIS would be superior if the power budget is not very small and the number of RIS elements is not very large.
28 citations
TL;DR: A novel single-anchor localization algorithm for a state-of-the-art architecture where the position of the user equipment is to be estimated at the base station with the aid of a RIS.
Abstract: Reconfigurable intelligent surfaces (RISs) are considered among the key techniques to be adopted for sixth-generation cellular networks (6G) to enhance not only communications but also localization performance. In this regard, we propose a novel single-anchor localization algorithm for a state-of-the-art architecture where the position of the user equipment (UE) is to be estimated at the base station (BS) with the aid of a RIS. We consider a practical model that accounts for both near-field propagation and multipath environments. The proposed scheme relies on a compressed sensing (CS) technique tailored to address the issues associated with near-field localization and model mismatches. Also, the RIS phases are optimized to enhance the positioning performance, achieving more than one order of magnitude gain in the localization accuracy compared to RISs with non-optimized phases.
26 citations
TL;DR: In this paper , a beamforming design problem is formulated to maximize the weighted sum of the communication throughput and the effective sensing power in a NOMA-empowered integrated sensing and communication (ISAC) framework.
Abstract: A non-orthogonal multiple access (NOMA) empowered integrated sensing and communication (ISAC) framework is investigated. A dual-functional base station serves multiple communication users employing NOMA, while the superimposed NOMA communication signal is simultaneously exploited for target sensing. A beamforming design problem is formulated to maximize the weighted sum of the communication throughput and the effective sensing power. To solve this problem, an efficient double-layer penalty-based algorithm is proposed by invoking successive convex approximation. Numerical results show that the proposed NOMA-ISAC outperforms the conventional ISAC in the underloaded regime experiencing highly correlated channels and in the overloaded regime.
26 citations
TL;DR: Numerical simulations verify the good performance of the proposed sparse MIMO array in high coupling scenarios and introduce the UF idea to design sparse M IMO arrays with reduced mutual coupling and increased uniform degrees of freedom.
Abstract: Uniform linear array (ULA) fitting (UF) principle is a newly proposed sparse array (SA) design scheme that aims to design SAs using cascaded ULAs, based on which a number of SAs with desired capacity are devised. In this letter, we introduce the UF idea to design sparse MIMO arrays with reduced mutual coupling and increased uniform degrees of freedom. However, existing sparse MIMO array design approach requires that the transmit and receive arrays in MIMO radar have hole-free difference coarrays (DCAs), which is no longer the best choice for the UF principle due to the fact that SAs designed via UF cannot guarantee hole-free DCAs. Considering the difference coarray of the sum coarray (DCSC) concept, a novel sparse MIMO array design strategy which is suitable for the UF principle is proposed. Based on the proposed strategy, a new sparse MIMO array is designed. Numerical simulations verify the good performance of the proposed sparse MIMO array in high coupling scenarios.
25 citations
TL;DR: This letter derives closed-form expressions for the outage probability of the transmitted messages and utilizes them to derive the throughput of the sources in a random access network with a medium access control protocol based on slotted ALOHA and RSMA.
Abstract: The use of new multiple access schemes emerges as a necessity in order to efficiently cope with the massive connectivity, reliability and high throughput requirements of the next generation Internet of Things. In this letter, we study the performance of an uplink rate-splitting multiple access (RSMA) network with two sources, in terms of outage probability and throughput. Specifically, we derive closed-form expressions for the outage probability of the transmitted messages and then, we utilize them to derive the throughput of the sources in a random access network with a medium access control protocol based on slotted ALOHA and RSMA.
24 citations
TL;DR: The AMC under few-shot conditions is considered, where a novel network architecture is proposed, namely automatic modulation classification relation network (AMCRN), and verified with the baseline methods.
Abstract: Deep learning (DL) has been widely applied in automatic modulation classification (AMC), while the superb performance highly depends on high-quality datasets. Motivated by this, the AMC under few-shot conditions is considered in this letter, where a novel network architecture is proposed, namely automatic modulation classification relation network (AMCRN), and verified with the baseline methods. Experimental results state that the accuracy of proposed AMCRN exceeds 90% and 10% to 50% improvements are obtained compared with classical schemes when the signal-to-noise ratio (SNR) is greater than −2 dB.
24 citations
TL;DR: An efficient federated transfer learning (FTL) framework with client selection for intrusion detection (ID) in mobile edge computing (MEC) is proposed, which significantly improves ID accuracy and communication efficiency as compared with the FL.
Abstract: In this letter, we propose an efficient federated transfer learning (FTL) framework with client selection for intrusion detection (ID) in mobile edge computing (MEC). Specifically, we leverage federated learning (FL) to preserve privacy by training model locally, and utilize transfer learning (TL) to improve training efficiency by knowledge transfer. For FL, unreliable and low-quality clients should not be selected to participate in the training. Therefore, we integrate FTL with a reinforcement learning (RL)-based client selection scheme to achieve the highest ID accuracy within a budget limit on the number of participating clients. Experimental results show that the FTL significantly improves ID accuracy and communication efficiency as compared with the FL. Furthermore, the FTL framework with RL-based client selection can achieve the highest accuracy within budget, which improves performance while saving cost.
24 citations
TL;DR: In this paper , an efficient uplink channel estimation design for a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted two-user communication system is proposed.
Abstract: In this letter, we study efficient uplink channel estimation design for a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted two-user communication systems. We first consider the time switching (TS) protocol for STAR-RIS and propose an efficient scheme to separately estimate the channels of the two users with optimized training (transmission/reflection) pattern. Next, we consider the energy splitting (ES) protocol for STAR-RIS under the practical coupled phase-shift model and devise a customized scheme to simultaneously estimate the channels of both users. Although the problem of minimizing the resultant channel estimation error for the ES protocol is difficult to solve, we propose an efficient algorithm to obtain a high-quality solution by jointly designing the pilot sequences, power-splitting ratio, and training patterns. Numerical results show the effectiveness of the proposed designs and reveal that the STAR-RIS under the TS protocol achieves a smaller channel estimation error than the ES case.
TL;DR: This work proposes the use of K-means and Q-learning assisted 3D ABS Placement and Power allocation algorithm (KQPP) to maximize the system sum capacity and achieves 6bps/Hz and 16bps/ Hz higher sum-capacity gain compared to equal power allocation and particle swarm optimization (PSO)-based power allocation schemes, respectively.
Abstract: Unmanned air vehicle (UAV) as an aerial base station (ABS) has attracted the attention of cellular service providers to enable emergency communications. However, the unplanned multiple ABS deployment poses severe interference challenges that degrade the user’s performance. To maximize the system sum capacity, we propose the use of K-means and Q-learning assisted 3D ABS Placement and Power allocation algorithm (KQPP). Specifically, we combine the benefits of K-means and Q-learning algorithms to achieve this goal. As a result, we successfully improve the sum capacity by satisfying all the users’ minimum data rate requirements. The proposed approach achieves 6bps/Hz and 16bps/Hz higher sum-capacity gain compared to equal power allocation and particle swarm optimization (PSO)-based power allocation schemes, respectively.
TL;DR: This work proposes the joint design of the TRBF strategy to manifest the balance of radar and communication in both perfect and imperfect channel state information scenarios by adopting the signal-to-interference-plus-noise ratio (SINR) as performance metrics for both Radar and communication.
Abstract: We consider the problem of the joint design of transmit and receive beamforming (TRBF) vectors for integrated sensing and communication systems in the signal-dependent interference scenario. The dual-functional base station can communicate with downlink users and detect targets simultaneously. By adopting the signal-to-interference-plus-noise ratio (SINR) as performance metrics for both radar and communication, we propose the joint design of the TRBF strategy to manifest the balance of radar and communication in both perfect and imperfect channel state information scenarios. Meanwhile, the upper bound performance of the SINR for target detection is analyzed. Numerical results show the SINR trade-offs between target detection and user communication.
TL;DR: In this article , the authors proposed a generalized MUSIC algorithm to jointly estimate the angle and range of the low-elevation target for meter-wave FDA-MIMO radar.
Abstract: Frequency diversity array multiple input multiple output (FDA-MIMO) communication system has the advantages of frequency diversity and waveform diversity, so it has attracted extensive attention, especially the joint estimation of angle and range. The existing researches mainly focus on incoherent signals, and conventional coherent signals, which can be solved by spatial smoothing based technologies. However, meter-wave MIMO radar, the target echo signal in low-elevation area has its uniqueness, and decoherence technologies such as spatial smoothing cannot be used. At present, the field of joint angle and range estimation of low-elevation target for meter-wave FDA-MIMO radar is still a gap. This letter focuses on deriving the signal model and proposes a generalized MUSIC algorithm to fill this gap. Simulation results show the effectiveness of the algorithm.
TL;DR: This letter proposes a multi-scale radio transformer with dual-channel representation for fine-grained modulation classification (FMC) and demonstrates that Ms-RaT achieves superior modulation classification accuracy with similar or lower computational complexity than existing state-of-the-art deep learning methods.
Abstract: Automatic modulation classification (AMC) plays a critical role in both civilian and military applications. In this letter, we propose a multi-scale radio transformer (Ms-RaT) with dual-channel representation for fine-grained modulation classification (FMC). In Ms-RaT, a dual-channel representation (DcR) of radio signals is designed to help the model learn discriminative features by converging the multi-modality information, including frequency, amplitude, and phase. During the learning process, multi-scale analysis is introduced into the model to form the tighter decision boundary. Finally, extensive simulation results demonstrate that Ms-RaT achieves superior modulation classification accuracy with similar or lower computational complexity than existing state-of-the-art deep learning methods. Through ablation studies, we also validate the effectiveness of DcR and multi-scale analysis in Ms-RaT.
TL;DR: In this paper , a novel complex Fourier neural operator (CFNO) was proposed, which introduces a time and frequency domain attention mechanism for specific emitter identification (SEI).
Abstract: Specific emitter identification (SEI) is a well-established approach to providing precise target information for civilian and military applications. For most deep learning (DL) based SEI schemes, neural operators directly learn mappings from the raw baseband waveform or its transformed representation. Different from existing schemes, we propose a novel complex Fourier neural operator (CFNO) in this letter, which introduces a time and frequency domain attention mechanism. With the CFNO block, features are fully learned from different domain perspectives. We evaluate the proposed method based on the joint distortion model of the transmitter and compare it with several state-of-the-art SEI algorithms. Simulation results demonstrate its excellent performance, making the CFNO block a good candidate for extracting fingerprints.
TL;DR: In this paper , a number of fast port selection algorithms utilizing a combination of machine learning methods and analytical approximation when the system observes only a few ports are proposed, and the simulation results illustrate that with only 10% of the ports observed, more than an order of magnitude reduction in the outage probability can be achieved.
Abstract: Fluid antenna system promises to obtain enormous diversity in the small space of a mobile device by switching the position of the radiating element to the most desirable position from a large number of prescribed locations of the given space. Previous researches have revealed the promising performance of fluid antenna systems if the position with the maximum received signal-to-noise ratio (SNR) is chosen. However, selecting the best position, referred to as port selection, requires a huge number of SNR observations from the ports and may prove to be infeasible. This letter tackles this problem by devising a number of fast port selection algorithms utilizing a combination of machine learning methods and analytical approximation when the system observes only a few ports. Simulation results illustrate that with only 10% of the ports observed, more than an order of magnitude reduction in the outage probability can be achieved. Even in the extreme cases where only one port is observed, considerable performance improvements are possible using the proposed algorithms.
TL;DR: This work proposed a deep learning-based approach with the traditional orthogonal matching pursuit followed by the residual network to improve the performance and a straightforward network structure is proposed to reduce computational complexity.
Abstract: The benefits of intelligent reflecting surfaces (IRS) assisted massive multiple-input multiple-output systems are based on the accurate acquisition of channel state information but at the cost of the high pilot overhead. In this work, the sparse structure in angular domain is revealed, and then the uplink cascaded channel estimation is converted into a compressive sensing (CS) problem. However, the angle of arrival and angle of departure are fundamentally continuous values, and thus they usually do not fall precisely on the discrete grids, resulting in grid mismatch. In this case, the typical CS solution usually yields the compromised reconstruction performance. To address this issue, we proposed a deep learning-based approach with the traditional orthogonal matching pursuit followed by the residual network to improve the performance. Furthermore, a straightforward network structure is proposed to reduce computational complexity. Simulation results demonstrate that the proposed solutions achieve a better estimation performance and require lower pilot overhead compared with the state-of-the-art ones.
TL;DR: This letter investigates intelligent reflecting surface backscatter communication (IRS-BackCom) assisted downlink multi-cell multiple-input single-output network for the first time and verifies the feasibility of the proposed IRS-Back com enabled CoMP scheme.
Abstract: This letter investigates intelligent reflecting surface backscatter communication (IRS-BackCom) assisted downlink multi-cell multiple-input single-output network for the first time. In such multi-cell network, each IRS instead of active transmit antennas serves as a cell base station (BS) to communicate with its single-antenna users through modulation and backscatter of ambient wireless signal from a power beacon (PB). To maximize weighted sum rate (WSR) of network subject to total power budget, coordinated multipoint transmission (CoMP) is implemented at the IRSs. By alternately optimizing the beamforming vectors at the PB and all the IRSs, the sub-optimal WSR is obtained. Simulations show the achievable WSR and verify the feasibility of the proposed IRS-BackCom enabled CoMP scheme.
TL;DR: In this paper , the authors proposed a scheme to optimize the IRS for illumination of the area centered around a mobile user in a blockage region, which requires the estimation of the MU's position and not the full CSI.
Abstract: Most algorithms developed for the optimization of Intelligent Reflecting Surfaces (IRSs) so far require knowledge of full Channel State Information (CSI). However, the resulting acquisition overhead constitutes a major bottleneck for the realization of IRS-assisted wireless systems in practice. In contrast, in this letter, focusing on downlink transmissions from a Base Station (BS) to a Mobile User (MU) that is located in a blockage region, we propose to optimize the IRS for illumination of the area centered around the MU. Hence, the proposed design requires the estimation of the MU’s position and not the full CSI. For a given IRS phase-shift configuration, the end-to-end BS-IRS-MU channel can then be estimated using conventional channel estimation techniques. The IRS reconfiguration overhead for the proposed scheme depends on the MU mobility as well as on how wide the coverage of the IRS illumination is. Therefore, we develop a general IRS phase-shift design, which is valid for both the near- and far-field regimes and features a parameter for tuning the size of the illumination area. Moreover, we study a special case where the IRS illuminates the entire blockage area, which implies that the IRS phase shifts do not change over time leading to zero overhead for IRS reconfiguration.
TL;DR: In this paper , a deep reinforcement learning (DRL)-based framework for joint UAV placement and resource allocation is proposed to enable sustainable FL with energy harvesting user devices, which aims to maximize the long-term FL performance considering the limited resources in the network, such as harvested energy, bandwidth resources, and UAV's energy budget.
Abstract: Federated learning (FL) is a promising solution to privacy preservation for data-driven deep learning approaches. However, enabling FL in unmanned aerial vehicle (UAV)-assisted wireless networks is still challenging due to limited resources and battery capacity in the UAV and user devices. In this regard, we propose a deep reinforcement learning (DRL)-based framework for joint UAV placement and resource allocation to enable sustainable FL with energy harvesting user devices. We aim to maximize the long-term FL performance considering the limited resources in the network, such as harvested energy, bandwidth resources, and UAV’s energy budget. To reduce the complexity of the original problem, we leverage the Lyapunov optimization technique to transform a long-term energy constraint into a deterministic problem. We reformulate the optimization problem as the framework of a Markov decision process (MDP) and design a DRL-based algorithm to solve the MDP. The proposed solution can guarantee the sustainable operation of UAV-aided wireless networks by improving energy conservation of the network in the long run.
TL;DR: The theoretical expression of bit-error rate (BER) and diversity order of the TD-DCSK scheme over multipath Rayleigh fading channels are derived, and its hardware complexity is analyzed to validate the accuracy of the theoretical derivation.
Abstract: A novel differential chaos shift keying scheme with transmit diversity, referred to as TD-DCSK scheme, is proposed in this letter. The proposed TD-DCSK scheme can achieve full diversity and high data rate by advisably designing the space-time (ST) block of the transmitted signal. With the designed ST block, the transmitter with multiple transmit antennas in the TD-DCSK scheme requires only one radio frequency chain, which is particularly important for keeping low hardware complexity. We derive the theoretical expression of bit-error rate (BER) and diversity order of the TD-DCSK scheme over multipath Rayleigh fading channels, and analyze its hardware complexity. Simulation results illustrate the superiority of the proposed scheme and validate the accuracy of the theoretical derivation. The proposed TD-DCSK scheme stands out as a promising solution for low-power and low-cost short-range wireless communications.
TL;DR: In this letter, the design of non-orthogonal multiple access (NOMA) beamforming is investigated in a spatial divisionmultiple access (SDMA) legacy system and two popular beamforming strategies in the NOMA literature are adopted and compared.
Abstract: In this letter, the design of non-orthogonal multiple access (NOMA) beamforming is investigated in a spatial division multiple access (SDMA) legacy system. In particular, two popular beamforming strategies in the NOMA literature, one to use existing SDMA beams and the other to form new beams, are adopted and compared. The studies carried out in the letter show that the two strategies realize different tradeoffs between system performance and complexity. For example, riding on existing beams offers a significant reduction in computational complexity, at the price of a slight performance loss. Furthermore, this simple strategy can realize the optimal performance when the users’ channels are structured.
TL;DR: In this paper , the authors investigated the ergodic achievable rate of RIS-aided massive MIMO systems with a two-timescale design, where the zero-forcing (ZF) detector is applied at the BS based on instantaneous aggregated channel state information (CSI).
Abstract: This letter investigates the reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with a two-timescale design. First, the zero-forcing (ZF) detector is applied at the base station (BS) based on instantaneous aggregated channel state information (CSI), which is the superposition of the direct channel and the cascaded user-RIS-BS channel. Then, by leveraging the channel statistical property, we derive the closed-form ergodic achievable rate expression. Using a gradient ascent method, we design the RIS passive beamforming relying only on the long-term statistical CSI. We prove that the ergodic rate scales on the order of $\mathcal {O}\left ({\log _{2}\left ({MN}\right)}\right)$ , where $M$ and $N$ denote the number of BS antennas and RIS elements, respectively. We also prove the striking superiority of the considered RIS-aided system with ZF detectors over the RIS-free systems and RIS-aided systems with maximum-ratio combining (MRC).
TL;DR: In this paper , the authors proposed an approach for specific emitter identification (SEI) by introducing model-agnostic meta-learning, which can achieve high accuracy in the case of a limited number of labeled training samples.
Abstract: It is necessary but difficult to obtain a large number of labeled samples to train the classification model in many real scenes. This letter proposes an approach for specific emitter identification(SEI) by introducing model-agnostic meta-learning, which can achieve high accuracy in the case of a limited number of labeled training samples. Specially, we improve the approach to make it suitable for the classification of electromagnetic signals of multiple types of equipments, without spending a lot of time and data to retrain the model structure. The data collected from ZigBee devices and UAVs are used to verify the proposed approach. The simulation results shows that the accuracy of proposed approach can reach more than 90% even though the training task and testing task are two types of devices.
TL;DR: In this paper , a three-part tutorial focusing on rate-splitting multiple access (RSMA) for 6G is presented, where the authors delineate the design principle and basic transmission frameworks of downlink and uplink RSMA.
Abstract: This letter is the first part of a three-part tutorial focusing on rate-splitting multiple access (RSMA) for 6G. As Part I of the tutorial, the letter presents the basics of RSMA and its applications in light of 6G. To begin with, we first delineate the design principle and basic transmission frameworks of downlink and uplink RSMA. We then illustrate the applications of RSMA for addressing the challenges of various potential enabling technologies and use cases, consequently making it a promising next generation multiple access (NGMA) scheme for future networks such as 6G and beyond. We briefly discuss the challenges of RSMA and conclude the letter. In continuation of Part I, we will focus on the interplay of RSMA with integrated sensing and communication, and reconfigurable intelligent surfaces, respectively in Part II and Part III of this tutorial.
TL;DR: RSMA is a promising technology for next generation multiple access (NGMA) and future networks such as 6G and beyond and the research challenges and open problems for RIS-aided RSMA systems are summarized.
Abstract: This letter is the third part of a three-part tutorial that focuses on rate-splitting multiple access (RSMA) for 6G. As Part III of the tutorial, this letter provides an overview of integrating RSMA and reconfigurable intelligent surface (RIS). We first introduce two potential PHY layer techniques, namely, RSMA and RIS, including the need for integrating RSMA with RIS and how they could help each other. Next, we provide a general model of an RIS-aided RSMA system and summarize some key performance metrics. Then, we discuss the major advantages of RIS-aided RSMA networks, and illustrate the rate region of RIS-aided RSMA for both perfect and imperfect channel conditions. Finally, we summarize the research challenges and open problems for RIS-aided RSMA systems. In conclusion, RSMA is a promising technology for next generation multiple access (NGMA) and future networks such as 6G and beyond.
TL;DR: This letter proposes a new constant-envelope modulation scheme, named slope-shift-keying and interleaved-chirp spreading (SSK-ICS) LoRa modulation, that can deliver higher data rates than the conventional Lo Ra modulation scheme and simulation results show that the proposed scheme outperformsLoRa modulation in both data rate and bit-error rate.
Abstract: Long-range (LoRa) modulation is an orthogonal modulation scheme that uses linearly-modulated up chirps to represent information bits. Its constant envelope and good bit-error-rate performance make it one of the key players in establishing low-power wide-area networks for the Internet of things applications. However, LoRa modulation has low data rates. In this letter, we propose a new constant-envelope modulation scheme, named slope-shift-keying and interleaved-chirp spreading (SSK-ICS) LoRa modulation, that can deliver higher data rates than the conventional LoRa modulation scheme. Succinctly, the proposed SSK-ICS-LoRa modulation uses up chirps, down chirps, interleaved up chirps and interleaved down chirps to expand the signal set and hence can carry more bits per transmission symbol. For the same spreading factor and bandwidth consumption, the proposed scheme is able to improve the data rate of the conventional LoRa scheme up to 28.6%. We also present the optimal maximum-likelihood detectors for both coherent and non-coherent demodulators for the proposed scheme. Simulation results show that the proposed scheme outperforms LoRa modulation in both data rate and bit-error rate.
TL;DR: This letter introduces a phase vector to the objective beam pattern and proposes an alternating minimization method to iteratively optimize the transmit beam and the phase vector, which involves second-order cone programming and constrained least squared estimation, respectively.
Abstract: In this letter, we consider hybrid beamforming for millimeter wave (mmWave) MIMO integrated sensing and communications (ISAC). We design the transmit beam of a dual-functional radar-communication (DFRC) base station (BS), aiming at approaching the objective radar beam pattern, subject to the constraints of the signal to interference-plus-noise ratio (SINR) of communication users and total transmission power of the DFRC BS. To provide additional degree of freedom for the beam design problem, we introduce a phase vector to the objective beam pattern and propose an alternating minimization method to iteratively optimize the transmit beam and the phase vector, which involves second-order cone programming and constrained least squared estimation, respectively. Then based on the designed transmit beam, we determine the analog beamformer and digital beamformer subject to the constant envelop constraint of phase shifter network in mmWave MIMO, still using the alternating minimization method. Simulation results show that under the same SINR constraint of communication users, larger antenna array can achieve better radar beam quality.
TL;DR: In this article , the authors proposed a novel architecture to minimize the population exposure to EMF by considering a smart radio environment with a reconfigurable intelligent surface (RIS) and optimized the RIS phases to minimize exposure in terms of the exposure index (EI) while maintaining a minimum target QoS.
Abstract: The deployment of the 5th-generation cellular networks (5G) and beyond has triggered health concerns due to the electric and magnetic fields (EMF) exposure. In this letter, we propose a novel architecture to minimize the population exposure to EMF by considering a smart radio environment with a reconfigurable intelligent surface (RIS). Then, we optimize the RIS phases to minimize the exposure in terms of the exposure index (EI) while maintaining a minimum target quality of service. The proposed scheme achieves up to 20% reduction in EI compared to schemes without RISs.