Automotive Millimeter-wave (mmWave) radar is becoming an essential modality for autonomous vehicles to enable all-weather perception, especially when LiDAR and camera fail in foggy, rainy, or snowy conditions. It is expected that the mutual interference among multiple radars becomes a critical issue in dense traffic scenarios, which can severely degrade the radar performance and lead to accidents. Despite extensive interference mitigation techniques, none can meet the less valid signal distortion while high robustness requirements for automotive radar perception in dense traffic scenarios. To overcome this predicament, we propose mmMic, a novel multiple mutual interference mitigation system that can accurately separate interference and recover valid signals to maintain the reliability of the radar measurements. The key insight is to design an interference estimator that can accurately localize the interference signal according to its linear frequency modulation features in the time-frequency (TF) domain. In addition, mmMic also fully exploits undisturbed valid signal information within an extended time-frequency domain to reconstruct the damaged signal. Our experiments on a real testbed show that mmMic can improve SINR to interference-free levels from multiple radars, achieving an average SINR improvement of 17% compared to the best-performing baseline.

Searchable symmetric encryption (SSE) enables the storage and retrieval of encrypted data on untrusted cloud servers, while dynamic searchable symmetric encryption (DSSE) further supports updating encrypted data. To date, in multi-user environments, most DSSE schemes cannot achieve simultaneous access control for both keyword retrieval and data updates. To address this issue, we propose a new DSSE scheme with efficient and complete(keyword retrieval and update)access control for multi-user environments, named EFCAM. Our work has simultaneously achieved efficient, flexible, and fine-grained access control for keyword retrieval and updating, this is extremely rare in existing research. For update operations, we combine file index encoding and homomorphic encryption (HE) technology, so that EFCAM optimizes the calculation; to achieve flexible access control, we adopt an equality test scheme that can supports three types of update authorization. For retrieval operations, users do not need to share keys. By executing a single query, the users can effectively retrieve all the data that they have permission to access. To enhance system security and operational efficiency, we have extended EFCAM with a dynamic policy update mechanism for flexible and real-time adjustment of access control policies. We formally analyze the security of EFCAM to prove that our scheme has forward security (FS) and backward security (BS). Experimental results show that, EFCAM maintains outstanding efficiency in encrypted data retrieval and update operations within multi-user environments, while also exhibiting strong scalability.

Cloud computing, as a novel paradigm for resource utilization and service deployment, virtualizes computing, storage, and network resources to form a logical resource pool. It provides users with an on-demand, self-service resource acquisition model, significantly lowering the barrier to resource access and offering strong elasticity in scaling applications. Traditional cloud data center networks are typically built on Ethernet and IP technologies. Most cloud data centers adopt a Spine-Leaf architecture, where the underlying physical network is interconnected via Ethernet links, and data transmission is realized through dynamic routing protocols and equal-cost multipath (ECMP) forwarding at the IP layer. Network virtualization functions are commonly achieved through tunneling technologies such as VXLAN, NVGRE, and UDP tunnels. However, this approach presents notable limitations. Firstly, conventional Ethernet and IP technologies were not originally designed for the specific requirements of modern cloud data center scenarios. As a result, they fail to address the particular technical demands and service characteristics of cloud computing networks. Secondly, tunneling technologies—especially those based on transport-layer tunneling—operate at higher layers of the protocol stack. When physical servers send and receive virtual network traffic, a considerable amount of computational resources (e.g., CPU and memory) is consumed, leading to both increased overhead and reduced network throughput. As a foundational infrastructure tailored for virtualized workloads, cloud data centers represent a typical vertically specialized application scenario. Therefore, it is feasible to design a novel cloud data center network architecture specifically optimized for this context. This paper proposes a new implementation approach for cloud data center networks, introducing a novel addressing, routing, and forwarding mechanism that natively integrates network virtualization functions into the network substrate. By embedding virtualization at the architectural layer, this design fundamentally addresses the limitations of conventional network virtualization techniques and enhances overall performance.

Fuzz testing is an dynamic program analysis technique designed for discovering vulnerabilities in IoT systems. The core goal is to deliberately feed maliciously crafted inputs into an IoT device or service, triggering vulnerabilities such as system crashes, buffer overflow exploits, and memory corruption, etc. Efficiently generating malicious inputs remains challenging, with leading methods often relying on randomly mutating existing valid inputs. In this work, we propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks to guide future fuzzing explorations. Specifically, we develop a framework that leverages code LLMs to guide the mutation process to perform meaningful input mutations. We formulate the mutation process as the sequenceto-sequence modeling, where LLM receives a sequence of bytes and outputs the mutated byte sequence. FuzzCoder is fine-tuned on our created instruction dataset (FuzzInstruct), where the successful fuzzing history is collected from the heuristic fuzzing tool. FuzzCoder can predict mutation positions and strategies for input files to trigger abnormal behaviors of the program. Most importantly, the experiment reveals results that FuzzCoder achieves better fuzzing performance compared to traditional and other AFL-based fuzzers, such as AFL, AFL++, AFLSmart, etc. On average, FuzzCoder achieves an improvement in code coverage of more than 20%, along with a significant increase in the number of crashes. 1

American fuzzy lop (AFL), as a representative tool for fuzzing, is capable of uncovering security vulnerabilities in industrial systems. It suffers from consuming a large amount of computational resources during the mutation. To improve the performance of AFL, researchers adopt algorithms, such as particle swarm optimization and long short-term memory, to optimize mutation operator selection. However, challenges persist in these approaches integrated with AFL, including optimization model complexity, insufficient accuracy, and poor generalization scalability. To address these issues, the article proposes a new fuzzer calledDPRFuzzto optimize AFL’s mutation phases. First, in the deterministic mutation strategy mutation phase, deep Q network and trust region policy optimization are leveraged to precisely generate effective mutated samples through perceiving mutation process in a relatively short time. Then, to boost the efficiency of the Havoc random mutation phase, we improve the Thompson sampling algorithm based on a multiagent strategy to generate an overall optimal mutation strategy chain. Finally, the approach is tested on eight programs, such asreadelf,tcpdump,andnm, and the advantages ofDPRFuzzare analyzed. Most importantly, the experiment reveals results thatDPRFuzzachieves better fuzzing performance compared to the traditional and other AFL-based fuzzers, such as AFL, AFL++, AFLSmart, etc. On average,DPRFuzzachieves an improvement in code coverage of over 10%, along with a significant increase in the number of crashes.

In this paper, we employ massive multiple-input and multiple-output (MIMO) technology to support multiple Internet-of-Things devices with ultra-reliability and low-latency communication (URLLC) industrial applications. Specifically, we first derive lower bounds (LBs) on the achievable uplink (UL) and downlink (DL) data rates under the finite blocklength (FBL) and pilot contamination, where each base station (BS) employs maximum-ratio transmission (MRT) in the DL and maximum-ratio combining (MRC) in the UL detection. In addition, the LB rates are derived for two types of pilot of the regular pilot (RP) and superimposed pilot (SP). We study joint UL-DL power allocation optimization where the objective is to maximize the UL-DL overall average weighted sum rate (WSR) for the systems individually with RP and SP schemes. We propose to employ successive convex approximation to transform the original problems into a series of geometric program problems. Then, an iterative algorithm is proposed to jointly optimize the UL and DL pilot and data payload power allocation. Simulation results are shown to compare the performances of the systems with RP and SP schemes for different settings. Simulation results also verify that the derived LB rates tightly match the corresponding ergodic rates and confirm the rapid convergence speed of the proposed iterative algorithms.

Great concerns on the Internet of Things (IoT) security are raised as IoT becomes an emerging paradigm to achieve ubiquitous connectivity. This article studies low-complexity secure transceiver and feedback design with the assistance of a jammer for physical-layer security in multiantenna IoT systems. We consider the general setting where a legitimate multiantenna controller broadcasts confidential messages to multiple multiantenna IoT devices in the presence of a passive external multiantenna eavesdropper. Moreover, there is only quantized downlink channel state information (CSI) at the controller and the jammer through feedback channels. We introduce several secure transceivers for different system setups, all of which employ block-diagonal precoding at the controller and null-space beamforming at the jammer but are with the different receivers at each IoT device. Considering the practical setup of IoT and for the tractability of analysis, we study the secrecy performance of the transceiver with an arbitrarily selected receive matrix independent of the channels of all devices. We derive an approximate lower bound on the ergodic secrecy rate (ESR) of each devicewithoutassuming any asymptotes for system parameters. The obtained result can also be viewed as a lower bound on the ESR performance of any other transceivers. We also optimize this bound to find an adaptive feedback bit allocation to the two feedback channels of each legitimate device. Numerical results are shown to illustrate the obtained analytical ESR lower bound, the feedback bit allocation algorithm, and significant ESR performance gain that results from the proposed feedback bit allocation.

This paper comprehensively studies the physical layer security in frequency division duplex multi-antenna cellular systems, where the multi-antenna base stations (BSs), legitimate users (LUs), and eavesdroppers are all randomly located. Each BS employs artificial-noise (AN)-aided multi-user linear beamforming with limited channel state information feedback. Based on the stochastic geometry theory, we first derive an analytical expression of a lower bound on the ergodic secrecy rate (ESR) of the typical LU without assuming asymptotes for any system parameter. We then develop a tight closed-form approximation on the optimal number of feedback bits to maximize a lower bound on the per-user net ESR, which takes into consideration the cost of uplink spectral efficiency for limited feedback. Moreover, the power allocation coefficient between message-bearing signals and AN can be optimized by using a bisection search method. Our main finding is that, the optimum number of feedback bits scales linearly with the path-loss exponent and the number of antennas, and scales logarithmically with the channel coherence time. The derived analytical results can also provide system-level insights into the ESR performance of the multi-antenna random cellular networks with limited feedback and the optimal system design. Numerical results are also presented to verify the obtained results.

This paper proposes a multiple-antenna non-orthogonal multiple access scheme including channel state information (CSI) quantization and feedback, user clustering, signal superposition coding, transmit beamforming, and successive interference cancellation at receivers under a general limited CSI feedback framework for frequency duplex division systems. Given a combination of system parameters, we conduct a mathematically strict performance analysis of the considered system, and obtain a closed-form lower bound on the ergodic rate of each user without assuming any extreme for system parameters, which has never been obtained before. Then, we jointly optimize two key parameters, i.e., transmit power and the number of feedback bits allocated to each user, and propose low-complexity closed-form solutions. Finally, numerical results are presented to verify our theoretical results and to illustrate the advantages in accuracy and performance over some existing related results under practical conditions.

We present a secure multi-user multi-antenna transmission framework based on artificial-noise-aided linear zero-forcing beamforming, with limited channel state information feedback from multiple distributed legitimate users (LUs). The secrecy performance of the proposed scheme is analytically investigated and optimized. We develop a new accurate closed-form expression of a lower bound on the ergodic secrecy rate (ESR) of each LU without assuming asymptotes for any system parameter. To make system design tractable, we develop another lower bound on ESR which is so analytically amenable that it enables one to not only extend the results of the previous related works but also explore some untouched aspects of the well known artificial-noise-aided scheme. We derive the optimized power allocation coefficient to message-bearing signals which maximizes the latter ESR lower bound. Furthermore, we theoretically study respectively the impacts of the two main parameters, i.e., transmit power P and the number of feedback bits of each LU B, on the power allocation coefficient, and show the asymptotic results for the high-power and high-quantization-resolution systems. We also develop a sufficient condition on P and B under which a positive ESR of each LU can be achieved. We study some important parameters called the minimum required transmit power (MRTP) and the minimum required number of feedback bits (MRFBs) for each LU to achieve a positive ESR or to achieve a target ESR, which have rarely been touched before. Besides, we propose the algorithms to obtain the MRTP and MRFBs. Numerical results are also provided to verify our theoretical results.

We provide in this paper a comprehensive solution to the design, performance analysis, and optimization of a multi-antenna non-orthogonal multiple access (NOMA) system for multiuser downlink communications under a general limited channel state information (CSI) feedback framework for frequency division duplex mode. We design a general framework including user clustering, joint power and bits allocation, CSI quantization and feedback, signal superposition coding, transmit beamforming, and successive interference cancellation at receivers. Then, we conduct a mathematically strict performance analysis of the considered system, and obtain a closed-form lower bound on the ergodic rate of each user in terms of transmit power, CSI quantization accuracy and channel conditions. For exploiting the potentials of multiple-antenna techniques in NOMA systems, we jointly optimize two key parameters, i.e., transmit power and the number of feedback bits allocated to each user, and propose low-complexity closed-form solutions. Moreover, through asymptotic analysis, we reveal the interactions between the main system parameters and their impacts on the joint power and feedback bits allocation result, and hence show some guidelines on the system design. Finally, numerical results validate the correctness of our theoretical analysis and demonstrate the advantages of the proposed algorithms over the most related state of the art.

Although there have been extensive works on artificial-noise-aided (AN-aided) secure transmission schemes for multi-antenna systems, there is still lack of study on the AN-aided scheme employing the widely used linear zero-forcing beamforming (ZFBF) for systems with multiple distributed users. Particularly, there is no analytical secrecy performance for general system settings with neither perfect nor imperfect channel state information of the legitimate users at the transmitter (CSIT). This paper considers the AN-aided ZFBF based on the quantized CSIT for secure communication in the downlink multiuser multi-antenna systems with an external multi-antenna eavesdropper. We develop an approximated closed-form lower bound on the ergodic rate of each legitimate user (LU), and also a closed-form upper bound on the maximum achievable ergodic rate for each LU's messages over the eavesdropper's channel without assuming any asymptotes for system parameters. Then, an approximated closed-form lower bound on the ergodic secrecy rate of each LU follows. Simulation results validate our analytical secrecy performance results and also the effectiveness of AN in enhancing the secrecy performance of linear ZFBF.

We consider a low-complexity transceiver design for secure communications of a multiuser multi-antenna system with an external multi-antenna eavesdropper. We propose to employ Tomlinson–Harashima precoder (THP) to simultaneously transmit message-bearing signals and artificial noise (AN) according to the quantized channel state information (CSI). Based on random vector quantization of the channel vectors, we obtain analytical approximations of the ergodic secrecy rate (ESR) of each legitimate receiver (LR) and the ergodic secrecy sum rate of the system with arbitrary system parameters. Based on the obtained analytical results, the near-optimal power allocation to the information signals and AN can be obtained using a numerical method. We show using an information-theoretical method that, besides the advantage in the ESR over the linear precoding scheme, THP can reduce the supported rate of the eavesdropper’s channel by preventing the eavesdropper from obtaining the legitimate channels’ (quantized) CSI. The ESR loss of each LR compared with the perfect CSI case will increase without bound for a fixed number of feedback bits. We also derive a feedback bit scaling law to solve this problem. Finally, numerical results are provided to verify our analytical results and also the advantage of the proposed secure nonlinear transceiver over the corresponding linear scheme.

Recent years have seen major innovations in developing energy-efficient wireless technologies, such as the Bluetooth low energy (BLE) for Internet of Things (IoT). Despite demonstrating significant benefits in providing low power transmission and massive connectivity, very few of these technologies directly connect to the Internet. Recent advances demonstrate the viability of direct communication among heterogeneous IoT devices with incompatible physical layers. These techniques, however, require modifications in transmission power or time, which may affect the media access control layer behaviors in legacy networks. In this paper, we argue that the frequency domain can serve as a free side channel with minimal interruptions to legacy networks. To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying demodulator and channel state information. The artificial Doppler shifts can be compensated for by the inherent frequency synchronization module and thus have a negligible impact on legacy communications. Our evaluation using commercial off-the-shelf BLE chips and 802.11-compliant testbeds has demonstrated that DopplerFi can achieve a throughput of up to 6.5 Kb/s at the cost of merely less than 0.8% throughput loss.

Information leakage rate is an intuitive metric that reflects the level of security in a wireless communication system, however, there are few studies taking it into consideration. Existing work on information leakage rate has two major limitations due to the complicated expression for the leakage rate: 1) the analytical and numerical results give few insights into the trade-off between system throughput and information leakage rate; 2) and the corresponding optimal designs of transmission rates are not analytically tractable. To overcome such limitations and obtain an in-depth understanding of information leakage rate in secure wireless communications, we propose an approximation for the average information leakage rate in the fixed-rate transmission scheme. Different from the complicated expression for information leakage rate in the literature, our proposed approximation has a low-complexity expression, and hence, it is easy for further analysis. Based on our approximation, the corresponding approximate optimal transmission rates are obtained for two transmission schemes with different design objectives. Through analytical and numerical results, we find that for the system maximizing throughput subject to information leakage rate constraint, the throughput is an upward convex non-decreasing function of the security constraint and much too loose security constraint does not contribute to higher throughput; while for the system minimizing information leakage rate subject to throughput constraint, the average information leakage rate is a lower convex increasing function of the throughput constraint.

We consider nonlinear transceiver design for downlink multiuser multi-antenna secure communications with an external multi-antenna eavesdropper. The multi-antenna transmitter simultaneously transmits confidential-message-bearing signals and artificial noise (AN) using nonlinear Tomlinson Harashima precoding based on the limited channel state information feedback. For the proposed nonlinear secure transceiver, we reveal the mechanism behind which makes this nonlinear precoding superior than the linear precoding methods in guaranteeing secrecy of wireless multiuser multi-antenna systems. We also obtain analytical bounds of the ergodic secrecy rate of each legitimate receiver and the ergodic secrecy sum rate of the system. Based on the analytical result, the near optimal power allocation to the information signals and AN can be obtained using numerical method. Numerical results are shown to verify the advantage of the proposed nonlinear method over the linear zero-forcing precoding.

We study transceiver design for secure communication of multiuser multi-antenna systems with assistant of a cooperative helper. There is an external multiple-antenna eavesdropper trying to obtain the confidential messages. Due to the finite-rate constraint of feedback channels, only quantized channel state information of the legitimate users is available at the transmitter and the helper. A nonlinear precoding strategy using Tomlinson Harashima precoding at the legitimate transmitter and a null-space beamforming scheme at the helper are proposed based on the quantized channel state information. Assuming a genie-aided perfect successive interference cancelation at Eve, we obtain closed-form expressions of the performance bounds for the achievable ergodic rate of each legitimate user and the ergodic secrecy sum rate. Numerical results illustrate that our proposed nonlinear-precoded strategy outperforms linear precoding scheme.

In this paper, we consider the problem of vehicular positioning enhancement with emerging connected vehicles (CV) technologies. In order to actually describe the scenario, the Interacting Multiple Model (IMM) filter is used for depicting varies of observation models. A CV-enhanced IMM filtering approach is proposed to locate a vehicle by data fusion from both coarse GPS data and the Doppler frequency shifts (DFS) measured from dedicated short-range communications (DSRC) radio signals. Simulation results state the effectiveness of the proposed approach.

We consider nonlinear signal processing algorithms for downlink multiple-input multiple-output (MIMO) systems with multiple-antenna users. The design goal is to improve bit error rate (BER) performance by jointly optimizing the transceiver, data streams, and user ordering, under given total transmit power. We consider a general global objective function, whose elements are Schur-convex functions of the mean square error (MSE) of each user. With nonlinear Tomlinson-Harashima precoding combined with block successive zero-forcing precoding at the transmitter, we show that the optimal nonlinear transceiver leads to favorable diagonal and parallel structures for all users with the general global performance metric. The number of data streams of each user is allowed to be an arbitrary number no more than the rank of the effective channel. We then provide closed-form expressions of the transceiver matrices and optimal power allocation of each user, and analytically characterize the optimal number of data streams of each user for the minimax and average BER metrics. The user ordering is also optimized to further improve the system performance. We also analytically investigate the impact of channel spatial correlation on the performance of our scheme and illustrate why our proposed adaptive strategy can mitigate the performance degradation caused by channel spatial correlation. The superiority of our proposed framework is demonstrated through numerical results.

We consider the problem of secure communication in wireless fading channels in the presence of quantized version of the receivers channel sate information at transmit side. The transmitter has multiple antennas and is able to simultaneously transmits an information bearing signal and artificial noise. For general values of signal to noise ratio (SNR), we obtain the exact closed-form expression for the distribution function of output signal to interference plus noise ratio at the legitimate receiver and a lower bound of the ergodic secrecy capacity can be obtain by using simple numerical integration. For the interference-limited case, we obtained a closed-form expression for a lower bound of secrecy capacity. Our results apply to arbitrary number of antennas and number of feedback bits. The analytical and numerical results show that the ergodic secrecy capacity lower bound approaches to a constant as the SNR of system grows large. In addition, we find that, for system with reasonable number of feedback bits, the optimal power allocation strategy should allocate most of the available power to the information signal.

We propose new low complexity opportunistic relaying strategies for multiple-antenna relay networks. Assuming that a source communicates with a destination, both equipped with M antennas, assisted by K single-antenna relay terminals using an amplify-and-forward half-duplex protocol, we propose a new transmission strategy which employs linear zero-forcing transmission and reception. Integrated with this transmission strategy, we propose two low complexity opportunistic relay selection algorithms, referred to as the Maximum Sum Rate Relay Selection (MSR) and Greedy Semi-Orthogonal Relay Selection (GSO) algorithms, which select only a few very important relays to share the total power. For the GSO algorithm, we present a theoretical analysis of the sum capacity as K grows large, which is shown to be M/2 log log K + O(1). This result is also shown to coincide with a fundamental cut-set upper bound on the sum capacity of MIMO networks with opportunistic relaying which we derive; thereby establishing a new exact scaling law for such networks, as well as demonstrating the asymptotic optimality of our proposed low complexity approach. Our numerical studies also indicate that our proposed opportunistic relaying techniques yield significant capacity benefits over the conventional approach without opportunistic selection, even when the number of relays is not large.

We consider a setup where a source communicates with a destination, both equipped with M antennas, assisted by K single-antenna relays using an amplify-and-forward half-duplex protocol. In contrast to conventional approaches where all relays in the network participate, we propose a new opportunistic relaying protocol in which only a few very important relays (VIRs) are selected to share the total power. Assuming perfect channel state information (CSI) at the destination and CSI of the backward channels for the selected VIRs at the source node, we show that the network capacity scales as M/2 log log K + O(1) for fixed M, fixed signal-to-noise ratio, and K → ∞. We also propose two low complexity algorithms for selecting the VIRs, and show that our proposed opportunistic relaying protocol can achieve the optimal capacity scaling, and also yield significant capacity gains compared with the conventional approach.

This paper investigates a low complexity zero-forcing dirty paper coding based transmission approach for the MIMO broadcast channel, employing eigenmode transmission with greedy semi-orthogonal user selection (SUS). We prove that as the number of users K grows large, our scheme achieves the optimal sum rate scaling of the MIMO broadcast channel (i.e. linear scaling with the number of transmit antennas, and double-logarithmic scaling with K). In addition, we show that whilst the number of receive antennas only affects the asymptotic sum rate scaling via the second-order behavior of the multiuser diversity gain; for finite K, the benefit due to multiple receive antennas can be very significant. Finally, we show the interesting result that the semi-orthogonality constraint imposed by the SUS algorithm, whilst facilitating a very low complexity user selection procedure, does not reduce the multiuser diversity gain in either first or second-order.

This paper studies the performance of MIMO multichannel beamforming systems in the presence of unequal-power co-channel interference and noise. We present exact expressions for the symbol error rate, and investigate the high signal to noise ratio regime by deriving explicit closed-form expressions for the diversity order and array gain. Our results are based on new exact and asymptotic marginal ordered eigenvalue distributions which we derive for a certain class of finite-dimensional complex random matrices.