In this paper, we study a movable antenna (MA) empowered secure transmission scheme for reconfigurable intelligent surface (RIS) aided cell-free symbiotic radio (SR) systems. Specifically, the MAs deployed at distributed access points (APs) work collaboratively with the RIS to establish high-quality propagation links for both primary and secondary transmissions, as well as suppressing the risk of eavesdropping on confidential primary information. We consider both continuous and discrete MA position cases and maximize the secrecy rate of primary transmission under the secondary transmission constraints, respectively. For the continuous position case, we propose a two-layer iterative optimization method based on differential evolution with one-in-one representation (DEO), to find a high-quality solution with relatively moderate computational complexity. For the discrete position case, we first extend the DEO based iterative framework by introducing the mapping and determination operations to handle the characteristic of discrete MA positions. To further reduce the computational complexity, we then design a single-layer iterative framework to solve all variables alternatively. In particular, we develop an efficient strategy to derive the sub-optimal solution for the discrete MA positions, superseding the DEO-based method. Numerical results validate the effectiveness of the proposed MA empowered secure transmission scheme along with its optimization algorithms.

This paper explores the application of movable antenna (MA), a cutting-edge technology with the capability of altering antenna positions, in a symbiotic radio (SR) system enabled by reconfigurable intelligent surface (RIS). The goal is to fully exploit the capabilities of both MA and RIS, constructing a better transmission environment for the co-existing primary and secondary transmission systems. For both parasitic SR (PSR) and commensal SR (CSR) scenarios with the channel uncertainties experienced by all transmission links, we design a robust transmission scheme with the goal of maximizing the primary rate while ensuring the secondary transmission quality. To address the maximization problem with thorny non-convex characteristics, we propose an alternating optimization framework that utilizes the general S-procedure, general sign-definiteness, successive convex approximation (SCA), and simulated annealing (SA) improved particle swarm optimization (SA-PSO) algorithms. Numerical results validate that the CSR scenario significantly outperforms the PSR scenario in terms of primary rate, and also show that compared to the fixed-position antenna scheme, the proposed MA scheme can increase the primary rate by 1.48 bps/Hz and 1.57 bps/Hz for the PSR and CSR scenarios, respectively.

LoRa-based backscatter communication technology is promising in enabling ubiquitous connectivity for the Internet of Things (IoT) over large distances with extremely low power consumption. In this paper, we design and implement CPLoRa, a high-throughput parallel LoRa backscatter communication system compatible with commodity LoRa receivers. The core idea of CPLoRa is to enable multiple backscatter tags to communicate with remote LoRa receivers simultaneously by generating standard LoRa packets from a common single-tone RF emitter, which can be extracted from ambient LoRa transmitters or generated from dedicated mobile devices. CPLoRa employs a modified low-power direct digital synthesizer (DDS) scheme for precise frequency synthesis, ensuring compatibility with commodity LoRa receivers and enhancing data rates for long-range backscatter transmissions. Each tag is assigned a unique frequency offset in the synthesizer, allowing parallel transmissions and creating orthogonal, independent LoRa channels. Moreover, we design a harmonic-canceling switch network at the RF front end to reduce mutual interference among different tags. Finally, we implement the CPLoRa tag prototype using low-cost circuit components and rigorously tested in outdoor and indoor environments, demonstrating that CPLoRa supports long-range transmissions of up to 1000 meters while achieving a throughput of 9.6 kbps with 10 parallel tags compatible with commodity LoRa receivers.

Clustering user devices (UDs) with similar traffic flows enables more effective transmission scheduling and resource allocation in wireless networks, especially for Internet of Things (IoT) with dominant demands for machine-to-machine (M2M) data communications. In this paper, we propose an experience-driven spatial-temporal graph attention network (Exp-STGAN) for UDs’ clustering, by exploiting the spatial-temporal correlations from UDs’ historical traffic flows. Considering the UDs’ heterogeneity, we first characterize each UD’s out-going traffic flows by a dynamic radiation pattern, which reflects the UD’s spatial distribution of traffic demands and its targeted receivers in different directions. We aim to explore UDs’ clustering based on traffic flows’ radiation patterns and propose a spatial-temporal graph attention to aggregate information from different UDs correlated in space and time domains. Without true labels for the UDs’ clustering, we formulate a flow similarity metric based on Kullback-Leibler (KL) divergence to quantify the clustering performance. Moreover, to improve the learning efficiency, we integrate salient human experience into the graph attention module and also continuously update the experience during the training process. Experiments demonstrate that the Exp-STGAN framework can effectively cluster similar UDs by their dynamic traffic flows, highlighting the potential for flow-aware network performance maximization in large-scale IoT systems.

ABSTRACT Reconfigurable intelligent surface (RIS) is an emerging technology that can enhance service coverage and spectral efficiency in sixth‐generation wireless networks. In contrast, relays are a traditional technique for achieving coverage extension. To explore potential benefits, we investigate three probable RIS‐relay cascaded scenarios over Nakagami‐ fading channels, where the full‐duplex relay is utilized for ensuring spectral efficiency. To evaluate the performance across different scenarios, we first derive their closed‐form expressions for the outage probability. Next, we determine the respective diversity order using asymptotic approximations in the high signal‐to‐noise‐ratio regime. Finally, we present the system throughput in delay‐limited transmission. Simulation results validate our analysis and demonstrate significant performance differences among various cascaded configurations, with the multi‐RIS‐assisted relay scenario achieving the best performance. Furthermore, the considered RIS‐decode‐and‐forward relay schemes outperform the corresponding RIS‐amplify‐and‐forward relay schemes in the low‐SNR regime.

In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs’ trajectory planning and operating modes, the ARIS’s passive beamforming, and the GUs’ transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs’ trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme.

In this paper, we propose a movable antenna (MA) empowered scheme for symbiotic radio (SR) communication systems. Specifically, multiple antennas at the primary transmitter (PT) can be flexibly moved to favorable locations to boost the channel conditions of the primary and secondary transmissions. The primary transmission is achieved by the active transmission from the PT to the primary user (PU), while the backscatter device (BD) takes a ride over the incident signal from the PT to passively send the secondary signal to the PU. Under this setup, we consider a primary rate maximization problem by jointly optimizing the transmit beamforming and the positions of MAs at the PT under a practical bit error rate constraint on the secondary transmission. Then, an alternating optimization framework with the utilization of the successive convex approximation, semi-definite processing and simulated annealing (SA) modified particle swarm optimization (SA-PSO) methods is proposed to find the solution of the transmit beamforming and MAs' positions. Finally, numerical results are provided to demonstrate the performance improvement provided by the proposed MA empowered scheme and the proposed algorithm.

This paper proposes a movable antenna (MA) empowered scheme for wireless powered and backscattering mobile edge computing (WPB-MEC) system. In the considered system, the access point (AP) transmits signals to enable wireless power transfer (WPT) to wireless devices (WDs) and passive offloading from the WDs to the MEC server. Moreover, the WDs are capable of actively offloading their tasks to the MEC server based on the previously harvested energy. The MAs are deployed at the AP and MEC server to improve the efficiency of WPT and hybrid offloading by flexibly adjusting their positions within an available area. Under this setup, we formulate a sum computational bits (SCBs) maximization problem and propose a block coordinate descent based optimization framework with the successive convex approximation method and the genetic algorithm based particle swarm optimization algorithm to find its accurate solution. Numerical results verify that utilizing the MAs can improve up to 81.77% SCBs compared to the scheme with fixed position antennas.

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode.

The progressing development of Internet of Things (IoT) has accelerated the emergence of resource-intensive and latency-sensitive mobile applications, which throws out a great challenge to the battery-powered wireless devices (WDs) with low-computing capabilities. To solve this intractable issue, we investigate an intelligent reflecting surface (IRS)-assisted multiantenna wireless-powered mobile edge computing (WP-MEC) system, in which WDs first harvest wireless energy emitted by a hybrid access point (HAP), then offload their tasks to the edge server, and finally download the results. In consideration of the practical scenarios, the finite computing capability of edge server and the nonlinear end-to-end power conversion of energy harvesting (EH) circuits at WDs are considered. In addition, an IRS is deployed to improve the efficiency of wireless power transfer (WPT) and the rate of data transmission between HAP and WDs. Under this setup, both space division multiple access (SDMA) and time division multiple access (TDMA) protocols are exploited and evaluated for data transmission. For each protocol, we maximize the computational rate by jointly optimizing time allocation, beamforming designs of HAP and IRS, as well as offloading strategies of WDs. To solve the problem formulated under the SDMA protocol, we propose an efficient alternating optimization (AO) algorithm. For the problem under the TDMA protocol, an AO algorithm with low complexity is proposed. Numerical results demonstrate the high effectiveness of the proposed algorithms and the superiority of the SDMA protocol over the TDMA protocol.

In this article, we propose a movable antenna (MA)-enhanced scheme for wireless-powered mobile-edge computing (WP-MEC) system, where the hybrid access point (HAP) equipped with multiple MAs first emits wireless energy to charge wireless devices (WDs), and then receives the offloaded tasks from the WDs for edge computing. The MAs deployed at the HAP enhance the spatial Degrees of Freedom (DoFs) by flexibly adjusting the positions of MAs within an available region, thereby improving the efficiency of both downlink wireless energy transfer (WPT) and uplink task offloading. To balance the performance enhancement against the implementation intricacy, we further propose three types of MA positioning configurations, i.e., dynamic MA positioning, semidynamic MA positioning, and static MA positioning. In addition, the nonlinear power conversion of energy harvesting (EH) circuits at the WDs and the finite computing capability at the edge server are taken into account. Our objective is to maximize the sum computational rate (SCR) by jointly optimizing the time allocation, positions of MAs, energy beamforming matrix, receive combing vectors, and offloading strategies of WDs. To solve the nonconvex problems, efficient alternating optimization (AO) frameworks are proposed. Moreover, we propose a hybrid algorithm of particle swarm optimization with variable local search (PSO-VLS) to solve the subproblem of MA positioning. Numerical results validate the superiority of exploiting MAs over the fixed-position antennas (FPAs) for enhancing the SCR performance of WP-MEC systems.

In this paper, we propose an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS) assisted wireless information and power transfer system. The aSTAR-RIS is employed to not only assist the downlink simultaneous wireless information and power transfer for the energy harvesting at the wireless powered devices (WPDs) and information receiving at the information receiving devices (IRDs), but also aid the uplink wireless information transmission from the WPDs to the hybrid access point. Our objective is to maximize the uplink sum-rate, considering the quality-of-service requirements of IRDs, by optimizing the power allocation, time scheduling, transmission and reflection beamforming at the aSTAR-RIS. To solve this non-convex problem, we propose an effective alternating optimization algorithm with semidefinite relaxation and fractional programming techniques. Numerical results demonstrate that the proposed aSTAR-RIS scheme can achieve up to 640.68% performance gains compared to the passive STAR-RIS scheme in our settings.

Intelligent reflecting surface (IRS) has been recently exploited as a symbiotic radio (SR) technology to improve energy and spectral efficiencies in wireless systems. In this article, we consider a symbiotic IRS-assisted mobile-edge computing (MEC) system that allows edge users to first harvest RF power from a hybrid access point (HAP) and then offload its computational workload to the MEC server associated with the HAP. We aim to minimize the HAP’s energy consumption by jointly optimizing the users’ offloading schemes, the HAP’s active beamforming, and the IRS’s passive beamforming strategies. We propose an optimization-driven hierarchical deep deterministic policy gradient (OH-DDPG) framework to decompose the energy minimization problem into the optimization and the learning subproblems, respectively. The outer loop DDPG learning method adapts the IRS’s passive beamforming strategy, while the inner loop optimization deals with the other control variables with reduced dimensionality. Moreover, to improve the learning efficiency, we extend OH-DDPG to the multiagent scenario. In particular, the HAP first estimates the users’ offloading strategy by the inner-loop optimization and shares it with all user agents. Then, each user agent refines its offloading decision using the DDPG algorithm independently. This can avoid signaling overhead among users and improve the multiuser learning efficiency. Simulation results show that the proposed OH-DDPG and the multiuser extension can achieve significant performance gains compared to the conventional model-free learning algorithms.

In this paper, we focus on a wireless-powered sensor network coordinated by a multi-antenna access point (AP). Each node can generate sensing information and report the latest information to the AP using the energy harvested from the AP’s signal beamforming. We aim to minimize the average age-of-information (AoI) by adapting the nodes’ scheduling and the transmission control strategies jointly. To reduce the transmission delay, an intelligent reflecting surface (IRS) is used to enhance the channel conditions by controlling the AP’s beamforming strategy and the IRS’s phase shifting matrix. Considering dynamic data arrivals at different sensing nodes, we propose a hierarchical deep reinforcement learning (DRL) framework for AoI minimization in two steps. The users’ transmission scheduling is firstly determined by the outer-loop DRL approach, e.g. the DQN or PPO algorithm, and then the inner-loop optimization is used to adapt either the uplink information transmission or downlink energy transfer to all nodes. A simple and efficient approximation is also proposed to reduce the inner-loop rum time overhead. Numerical results verify that the hierarchical learning framework outperforms typical baselines in terms of the average AoI and proportional fairness among different nodes.

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

In this paper, we propose a reconfigurable intelligent surface (RIS) assisted secure transmission scheme for a symbiotic radio multicast system, where the RIS not only assists the confidential information multicasting from a primary transmitter (PT) to multiple primary users (PUs) to against the information interception by eavesdroppers, but also delivers its own signal to a secondary user (SU) by passive reflections. We formulate a signal-to-noise ratio (SNR) maximization problem for the SU by jointly optimizing the active beamforming at the PT, amplitude reflection coefficients and phase shifts of the RIS. To address the non-convexity of the formulated problem, we propose to decompose the original problem into two sub-problems and solve them independently in an iteratively alternating manner. For the first sub-problem, we adopt the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to design the active beamforming by proving the tightness of SDR. For the second sub-problem, the sequential rank-one constraint relaxation (SROCR) technique is adopted to handle the rank-one constraint for reflection coefficients optimization. Numerical results show that compared to the benchmark schemes, the proposed scheme can achieve up to 68.3% performance gain in terms of SNR.

Pattern recognition based on computer-assisted seismic classification accelerates traditional seismic interpretation and can automatically extract seismic patterns of interest on large data volumes that would take considerable effort by a human interpreter. Seismic facies such as salt domes, mass transport complexes, and karst collapse features, can often be differentiated from surrounding conformal sediments by lateral and vertical changes in dip, energy, and continuity. To better exploit the difference between conformal and more chaotic seismic features, we introduce 3-D geometric nonparallelism attributes to statistically quantify lateral and vertical changes in both dip and amplitude gradients. When examining the attribute response of salt and karst features, coherent noise or small internal reflector blocks in an otherwise chaotic background give rise to a “salt and pepper” pattern on coherence, reflector parallelism, and texture attributes. Whereas human interpreters see the larger patterns and can put them in the proper geological context, simple attribute classification techniques cannot and see such embedded coherent anomalies as separate facies. To address this issue, we apply a 3-D structure-oriented adaptive Kuwahara filtering technique to precondition the seismic attribute volumes to smooth the internal attribute response while sharpening the facies edges prior to subsequent machine learning classification. We validate our workflow by applying a semisupervised generative topographic mapping machine learning-based multiattribute classification algorithm of five seismic attribute volumes to mapping karst collapse facies.

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the transmit beamforming and the relays’ radio modes and operating parameters. Due to the non-convex and combinatorial problem structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep$Q$-network (DQN) algorithm and then optimizes the continuous beamforming and relaying strategies by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the inner-loop DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning performance and achieve a higher reward, up to 20%, compared to the conventional model-free DDPG approach.

In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming. Considering channel estimation errors, we model both the AP-IRS channel and the AP-IRS-receiver as a cascaded channel by norm-based uncertainty sets. This allows us to formulate a robust optimization problem to minimize the AP's transmit power, subject to the receiver's worst-case data rate requirement and the IRS's worst-case power budget constraint. Instead of using the alternating optimization (AO) method, we firstly propose a heuristic scheme to decompose the IRS's phase shift optimization and the AP's active beamforming. Based on semidefinite relaxations of the worst-case constraints, we further devise an iterative algorithm to optimize the AP's transmit beamforming and the magnitude of the IRS's reflecting coefficients efficiently by solving a set of semidefinite programs. Simulation results reveal that the AP requires a higher transmit power to deal with the channel uncertainty. Moreover, the negative effect of channel uncertainty can be alleviated by using a larger-size IRS.

This paper proposes a hybrid-relaying scheme empowered by a self-sustainable intelligent reflecting surface (IRS) in a wireless powered communication network (WPCN), to simultaneously improve the performance of downlink energy transfer (ET) from a hybrid access point (HAP) to multiple users and uplink information transmission (IT) from users to the HAP. We propose time-switching (TS) and power-splitting (PS) schemes for the IRS, where the IRS can harvest energy from the HAP's signals by switching between energy harvesting and signal reflection in the TS scheme or adjusting its reflection amplitude in the PS scheme. For both the TS and PS schemes, we formulate the sum-rate maximization problems by jointly optimizing the IRS's phase shifts for both ET and IT and network resource allocation. To address each problem's non-convexity, we propose a two-step algorithm to obtain the near-optimal solution with high accuracy. To show the structure of resource allocation, we also investigate the optimal solutions for the schemes with random phase shifts. Through numerical results, we show that our proposed schemes can achieve significant system sum-rate gain compared to the baseline scheme without IRS.

With the dramatic growth of seismic data volume, efficient and accurate seismic source location has become a significant challenge to seismologists. Recently, time reversal imaging (TRI) has been widely applied in automatic seismic source location for its robustness and accuracy, but its wave-equation-based implementation is usually computationally expensive. To achieve an efficient in situ and real-time source location, the emerging sensor network is a good option. In this article, we propose a simplified Gaussian beam TRI (SGTRI) method to implement the seismic source location in a distributed sensor network. Gaussian beam (GB) is a high-frequency asymptotic solution of the wave equation, which can help reduce the computation costs of the wavefield extrapolation in conventional TRI. Traditionally, the GB construction for reflection seismic imaging covers the entire subsurface space. However, for certain source localization, only limited areas contribute. Thus, we propose a beamforming-technique-based simplified GB construction to further boost efficiency. Then, we propose an imaging condition for the SGTRI to construct the final source location map. Using synthetic experiments, we demonstrate the accuracy, robustness, and efficiency of the proposed method compared with conventional TRI. In the end, a field application also shows promising results.

In this paper, we propose a relay cooperation scheme for backscatter communication systems for performance enhancement, in which one user backscatters incident signals from a power beacon (PB) to a relay and a receiver simultaneously, and then the relay decodes the received signals and forwards the decoded signals to the receiver. We consider two cases that the relay is with/without an embedded energy source. In particular, if the relay does not have an energy source, an energy harvesting phase is required, during which the relay harvests energy from the PB while the user backscatters information to the receiver. We first formulate system throughput maximization problems for both cases by finding the optimal time allocation schemes, from which some useful insights are provided. Then, with a given amount of information required to be delivered, the transmission time minimization problems for both cases are also formulated, and the optimal solutions are derived in closed-form. Numerical results reveal the proposed scheme can significantly enhance the system throughput and transmission time.

In this paper, we consider a relay assisted backscatter communication (RaBackCom) system, where a user backscatters incident signals from a carrier emitter (CE) to a relay and a receiver simultaneously, and then the relay forwards the user's information to the receiver for throughput improvement. We consider two cases that the relay is with/without an embedded energy source. Specifically, if the relay does not have an energy source, it first harvests energy from the signals from the CE and then uses its harvested energy for information forwarding. For both cases, we formulate time allocation problems on the user's information backscattering, the user's information forwarding, or the relay's energy harvesting to maximize the system throughput, and then derive closed-form solutions. Simulation results demonstrate the advantages of the proposed relay cooperation scheme with the optimal time allocation in terms of system throughput.

In this paper, we consider a cognitive wireless powered communication network for Internet of Things applications, which consists of a primary communication pair and a secondary communication system. We propose a novel hybrid harvest-then-transmit (HTT) and backscatter communication (BackCom) mode for the information transmission of the secondary communication system. When the primary channel is busy, cognitive users (CUs) backscatter the incident signal from the primary transmitter to the information receiver in the ambient backscatter (AB) mode or harvest energy for the future information transmission. When the primary channel is idle, CUs backscatter the incident signal from the power beacon in the bistatic scatter (BS) mode or work in the HTT mode to transmit information. We further investigate the optimal time allocation between the AB mode and energy harvesting and that between the BS mode and the HTT mode for the sake of maximizing the throughput of the secondary communication system, and derive the numerical solutions. To be specific, we derive the closed-form optimal solution for a single CU case, and moreover, obtain the optimal combination of the working modes. Numerical results demonstrate the advantage of our proposed hybrid HTT and BackCom mode over the benchmark mode in terms of system throughput.