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We derive a sphere-packing error exponent for coded transmission over discrete memoryless channels with a fixed decoding metric. By studying the error probability of the code over an auxiliary channel, we find a lower bound to the probability of error of mismatched decoding. The bound is shown to decay exponentially for coding rates smaller than a new upper bound to the mismatch capacity which is established in this paper. For rates higher than the new upper bound, the error probability is shown to be bounded away from zero. The new upper bound is shown to improve over previous upper bounds to the mismatch capacity.

To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division duplex (FDD) systems, the CSI is acquired through a feedback signal from the user equipment (UE). However, this may lead to an important overhead in FDD massive MIMO systems. Focusing on these systems, in this study, we propose a novel strategy to design the CSI feedback. Our strategy allows to optimally design variable length feedback, that is promising compared to fixed feedback since users experience channel matrices differently sparse. Specifically, principal component analysis (PCA) is used to compress the channel into a latent space with adaptive dimensionality. To quantize this compressed channel, the feedback bits are smartly allocated to the latent space dimensions by minimizing the normalized mean squared error (NMSE) distortion. Finally, the quantization codebook is determined with k-means clustering. Numerical simulations show that our strategy improves the zero-forcing beamforming sum rate by 17%, compared to CsiNetPro. The number of model parameters is reduced by 23.4 times, thus causing a significantly smaller offloading overhead. At the same time, PCA is characterized by a lightweight unsupervised training, requiring eight times fewer training samples than CsiNetPro., Accepted by IEEE for publication

This letter considers the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication systems in urban areas under the general Rician fading channel. A hybrid offline-online design is proposed to improve the system performance by leveraging both the statistical channel state information (S-CSI) and instantaneous channel state information (I-CSI). For the offline phase, we aim to maximize the expected average achievable rate based on the S-CSI by jointly optimizing the RIS's phase-shift and UAV trajectory. The formulated stochastic optimization problem is difficult to solve due to its non-convexity. To tackle this problem, we propose an efficient algorithm by leveraging the stochastic successive convex approximation (SSCA) techniques. For the online phase, the UAV adaptively adjusts the transmit beamforming and user scheduling according to the effective I-CSI. Numerical results verify that the proposed hybrid design performs better than various bechmark schemes, and also demonstrate a favorable trade-off between system performance and CSI overhead.

This work gives a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the received signal-to-noise ratio (SNR) by coordinating phase shifts across reflective elements in the absence of channel information. While the existing methods of IRS beamforming typically first estimate channels and then optimize phase shifts, we propose a conditional sample mean based statistical approach that explores the wireless environment via random sampling without performing any channel estimation. Remarkably, the new method just requires a polynomial number of random samples to yield an SNR boost that is quadratic in the number of reflective elements, whereas the standard random-max sampling algorithm can only achieve a linear boost under the same condition. Moreover, we gain additional insight into blind beamforming by interpreting it as a least squares problem. Field tests demonstrate the significant advantages of the proposed blind beamforming algorithm over the benchmark algorithms in enhancing wireless transmission., Comment: 16 pages, 15 figures

In this paper, we propose two reconfigurable intelligent surface-aided $M$-ary frequency-modulated differential chaos shift keying (RIS-$M$-FM-DCSK) schemes. In scheme I, the RIS is regarded as a transmitter at the source to incorporate the $M$-ary phase-shift-keying ($M$-PSK) symbols into the FM chaotic signal and to reflect the resultant $M$-ary FM chaotic signal toward the destination. The information bits of the source are carried by both the positive/negative state of the FM chaotic signal and the $M$-PSK symbols. In scheme II, the RIS is treated as a relay so that both the source and relay can simultaneously transmit their information bits to the destination. The information bits of the source and relay are carried by the positive/negative state of the FM chaotic signal and $M$-PSK symbols generated by the RIS, respectively. The proposed RIS-$M$-FM-DCSK system has an attractive advantage that it does not require channel state information for detection, thus avoiding complex channel estimation. Moreover, we derive the theoretical expressions for bit error rates (BERs) of the proposed RIS-$M$-FM-DCSK system with both scheme I and scheme II over multipath Rayleigh fading channels. Simulations results not only verify the accuracy of the theoretical derivations, but also demonstrate the superiority of the proposed system. The proposed RIS-$M$-FM-DCSK system is a promising low-cost, low-power, and high-reliability alternative for wireless communication networks.

This paper studies the feasibility of deploying intelligent reflecting surfaces (IRSs) in massive MIMO (multiple-input multiple-output) systems to improve the performance of users in the service dead zone. To reduce the channel training overhead, we advocate a novel protocol for the uplink communication in the IRS-assisted massive MIMO systems. Under this protocol, the IRS reflection coefficients are optimized based on the channel covariance matrices, which are generally fixed for many coherence blocks, to boost the long-term performance. Then, given the IRS reflecting coefficients, the BS beamforming vectors are designed in each coherence block based on the effective channel of each user, which is the superposition of its direct and reflected user-IRS-BS channels, to improve the instantaneous performance. Since merely the user effective channels are estimated in each coherence block, the training overhead of this protocol is the same as that in the legacy wireless systems without IRSs. Moreover, in the asymptotic regime that the numbers of IRS elements and BS antennas both go to infinity with a fixed ratio, we manage to first characterize the minimum mean-squared error (MMSE) estimators of the user effective channels and then quantify the closed-form user achievable rates as functions of channel covariance matrices with channel training overhead and estimation error taken into account. Interestingly, it is shown that the properties of channel hardening and favorable propagation still hold for the user effective channels, and satisfactory user rates are thus achievable even if simple BS beamforming solutions, e.g., maximal-ratio combining, are employed. Finally, thanks to the rate characterization, we design a low-complexity algorithm to optimize the IRS reflection coefficients based on channel covariance matrices., Comment: submitted to IEEE journal for possible publication

In VANETs, it is important to support fast and reliable multi-hop broadcast for safety-related applications. The performance of multi-hop broadcast schemes is greatly affected by relay selection strategies. However, the relationship between the relay selection strategies and the expected broadcast performance has not been fully characterized yet. Furthermore, conventional broadcast schemes usually attempt to minimize the waiting time difference between adjacent relay candidates to reduce the waiting time overhead, which makes the relay selection process vulnerable to internal interference, occurring due to retransmissions from previous forwarders and transmissions from redundant relays. In this paper, we jointly take both of the relay selection and the internal interference mitigation into account and propose a fast, reliable, opportunistic multi-hop broadcast scheme, in which we utilize a novel metric called the expected broadcast speed in relay selection and propose a delayed retransmission mechanism to mitigate the adverse effect of retransmissions from previous forwarders and an expected redundancy probability based mechanism to mitigate the adverse effect of redundant relays. The performance evaluation results show that the proposed scheme yields the best broadcast performance among the four schemes in terms of the broadcast coverage ratio and the end-to-end delivery latency.

In this paper, we compute the expected number of vehicles with at least one two-hop path to a fixed roadside unit (RSU) in a multi-hop, one-dimensional vehicular ad hoc network (VANET) where other cars can act as relays. The pairwise channels experience Rayleigh fading in the random connection model, and so exist, with a probability given by a function of the mutual distance between the cars, or between the cars and the RSU. We derive exact expressions for the expected number of cars with a two-hop connection to the RSU when the car density $\rho$ tends to zero and infinity, and determine its behaviour using an infinite oscillating power series in $\rho$, which is accurate for all regimes of traffic density. We also corroborate those findings with a realistic scenario, using snapshots of actual traffic data. Finally, a normal approximation is discussed for the probability mass function of the number of cars with a two-hop connection to the RSU., Comment: 5 pages, 5 figures

Currently, orthogonal time frequency space (OTFS) modulation has drawn much attention to reliable communications in high-mobility scenarios. This paper proposes a doubly-iterative sparsified minimum mean square error (DI-S-MMSE) turbo equalizer, which iteratively exchanges the extrinsic information between a soft-input-soft-input (SISO) MMSE estimator and a SISO decoder. Our proposed equalizer does not suffer from short loops and approaches the performance of the near-optimal symbol-wise maximum a posteriori (MAP) algorithm. To exploit the inherent sparsity of OTFS system, we resort to graph theory to investigate the sparsity pattern of the channel matrix, and propose two sparsification guidelines to reduce the complexity of calculating the matrix inverse at the MMSE estimator. Then, we apply two iterative algorithms to MMSE estimation, i.e., the Generalized Minimal Residual (GMRES) and Factorized Sparse Approximate Inverse (FSPAI) algorithms. The former is used at the initial turbo iteration, whose global convergence is proven in our equalizer, while the latter is used at the subsequent turbo iterations with the help of our proposed guidelines. Simulation results demonstrate that our equalizer has a linear order of complexity while the performance loss incurred by the sparsification is only 0.2 dB at $10^{-4}$ bit error rate., 30 pages, 7 figures

Ultrasonic signal acquisition platforms generate considerable amounts of data to be stored and processed, especially when multichannel scanning or beamforming is employed. Reducing the mass storage and allowing high-speed data transmissions necessitate the compression of ultrasonic data into a representation with fewer bits. High compression accuracy is crucial in many applications, such as ultrasonic medical imaging and nondestructive testing (NDT). In this study, we present learning models for massive ultrasonic data compression on the order of megabytes. A common and highly efficient compression method for ultrasonic data is signal decomposition and subband elimination using wavelet packet transformation (WPT). We designed an algorithm for finding the wavelet kernel that provides maximum energy compaction and the optimal subband decomposition tree structure for a given ultrasonic signal. Furthermore, the WPT convolutional autoencoder (WPTCAE) compression algorithm is proposed based on the WPT compression tree structure and the use of machine learning for estimating the optimal kernel. To further improve the compression accuracy, an autoencoder (AE) is incorporated into the WPTCAE model to build a hybrid model. The performance of the WPTCAE compression model is examined and benchmarked against other compression algorithms using ultrasonic radio frequency (RF) datasets acquired in NDT and medical imaging applications. The experimental results clearly show that the WPTCAE compression model provides improved compression ratios while maintaining high signal fidelity. The proposed learning models can achieve a compression accuracy of 98% by using only 6% of the original data.

Decoding sequences that stem from multiple transmissions of a codeword over an insertion, deletion, and substitution channel is a critical component of efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we consider a concatenated coding scheme with an outer nonbinary low-density parity-check code or a polar code and either an inner convolutional code or a time-varying block code. We propose two novel decoding algorithms for inference from multiple received sequences, both combining the inner code and channel to a joint hidden Markov model to infer symbolwise a posteriori probabilities (APPs). The first decoder computes the exact APPs by jointly decoding the received sequences, whereas the second decoder approximates the APPs by combining the results of separately decoded received sequences and has a complexity that is linear with the number of sequences. Using the proposed algorithms, we evaluate the performance of decoding multiple received sequences by means of achievable information rates and Monte-Carlo simulations. We show significant performance gains compared to a single received sequence. In addition, we succeed in improving the performance of the aforementioned coding scheme by optimizing both the inner and outer codes., This paper has been accepted for publication in the IEEE Transactions on Information Theory

Scientific simulations on high-performance computing (HPC) systems can generate large amounts of floating-point data per run. As compared to lossless compressors, lossy compressors, such as SZ and ZFP, can reduce data volume more aggressively while maintaining the usefulness of the data. However, a reduction ratio of more than two orders of magnitude is almost impossible without seriously distorting the data. In deep learning, the autoencoder has shown potential for data compression. Whether the autoencoder can deliver similar performance on scientific data, however, is unknown. In this paper, we for the first time conduct a comprehensive study on the use of autoencoders to compress real-world scientific data and illustrate several key findings on using autoencoders for scientific data reduction. We implement an autoencoder-based compression prototype to reduce floating-point data. Our study shows that the out-of-the-box implementation needs to be further tuned in order to achieve high compression ratios and satisfactory error bounds. Our evaluation results show that, for most test datasets, the tuned autoencoder outperforms SZ by 2 to 4X, and ZFP by 10 to 50X in compression ratios, respectively. Our practices and lessons learned in this work can direct future optimizations for using autoencoders to compress scientific data.

Jamming attacks on wireless networked control systems are investigated for the scenarios where the system dynamics face exogenous disturbance. In particular, the control input packets are assumed to be transmitted from a controller to a remotely located linear plant over an insecure wireless communication channel that is subject to jamming attacks. The time-varying likelihood of transmission failures on this channel depends on the power of the jamming interference signal emitted by an attacker. We show that jamming attacks can prevent stability when the system faces disturbance, even if the attacked system without disturbance is stable. We also show that stability under jamming and disturbance can be achieved if the average jamming interference power is restricted in a certain way that we characterize in the paper. We illustrate our results on an example networked control system with a fading wireless channel, where the outage probability is affected by jamming attacks., Comment: Improve wording; Correct typos

Attribute-based encryption (ABE) has been widely used to provide fine-grained access control to encrypted data in cloud computing. However, in practice, since the attributes are shared by multiple users in the ABE system, it is difficult to identify malicious users which intentionally but implicitly reveal their own ABE secret keys for some purpose. In recent years, a substantial amount of work focused on designing efficient tracing mechanisms to identify such misbehaving users on ABE. In this paper, we propose an efficient generic construction of black-box traceable ABE to identify the traitors. Our construction supports (public) adaptive black-box traceability and has short keys and short ciphertexts that are independent of the number of users. Technically, our construction relies on a generic transformation from any attribute-based inner-product functional encryption (ABIPFE) scheme to black-box traceable ABE system. Furthermore, to instantiate our generic construction from standard lattices, we propose the first lattice-based ABIPFE scheme, which is proven weakly selective secure in the standard model.

An upper bound on the capacity of multiple-input multiple-output (MIMO) Gaussian fading channels is derived under peak amplitude constraints. The upper bound is obtained borrowing concepts from convex geometry and it extends to MIMO channels notable results from the geometric analysis on the capacity of scalar Gaussian channels. Relying on a sphere packing argument and on the renowned Steiner's formula, the proposed upper bound depends on the intrinsic volumes of the constraint region, i.e., functionals defining a measure of the geometric features of a convex body. The tightness of the bound is investigated at high signal-to-noise ratio (SNR) for any arbitrary convex amplitude constraint region, for any channel matrix realization, and any dimension of the MIMO system. In addition, two variants of the upper bound are proposed: one is useful to ensure the feasibility in the evaluation of the bound and the other to improve the bound's performance in the low SNR regime. Finally, the upper bound is specialized for two practical transmitter configurations, either employing a single power amplifier for all transmitting antennas or a power amplifier for each antenna., Comment: Submitted to IEEE Transactions on Information Theory. Part of this work was presented at the 2020 IEEE Information Theory Workshop

In this paper, we utilize a concatenation scheme to construct new families of quantum error correction codes achieving the quantum Gilbert-Varshamov (GV) bound asymptotically. We concatenate alternant codes with any linear code achieving the classical GV bound to construct Calderbank-Shor-Steane (CSS) codes. We show that the concatenated code can achieve the quantum GV bound asymptotically and can approach the Hashing bound for asymmetric Pauli channels. By combing Steane's enlargement construction of CSS codes, we derive a family of enlarged stabilizer codes achieving the quantum GV bound for enlarged CSS codes asymptotically. As applications, we derive two families of fast encodable and decodable CSS codes with parameters $\mathscr{Q}_1=[[N,\Omega(\sqrt{N}),\Omega( \sqrt{N})]],$ and $\mathscr{Q}_2=[[N,\Omega(N/\log N),\Omega(N/\log N)/\Omega(\log N)]].$ We show that $\mathscr{Q}_1$ can be encoded very efficiently by circuits of size $O(N)$ and depth $O(\sqrt{N})$. For an input error syndrome, $\mathscr{Q}_1$ can correct any adversarial error of weight up to half the minimum distance bound in $O(N)$ time. $\mathscr{Q}_1$ can also be decoded in parallel in $O(\sqrt{N})$ time by using $O(\sqrt{N})$ classical processors. For an input error syndrome, we proved that $\mathscr{Q}_2$ can correct a linear number of ${X}$-errors with high probability and an almost linear number of ${Z}$-errors in $O(N )$ time. Moreover, $\mathscr{Q}_2$ can be decoded in parallel in $O(\log(N))$ time by using $O(N)$ classical processors., Comment: 11 pages,2 figures

Massive MIMO opens up attractive possibilities for next generation wireless systems with its large number of antennas offering spatial diversity and multiplexing gain. However, the fronthaul link that connects a massive MIMO Remote Radio Head (RRH) and carries IQ samples to the Baseband Unit (BBU) of the base station can throttle the network capacity/speed if appropriate data compression techniques are not applied. In this paper, we propose an iterative technique for fronthaul load reduction in the uplink for massive MIMO systems that utilizes the convolution structure of the received signals. We use an alternating minimisation algorithm for blind deconvolution of the received data matrix that provides compression ratios of 30-50. In addition, the technique presented here can be used for blind decoding of OFDM signals in massive MIMO systems., Comment: 7 pages, 3 figures

Information compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks. This paper proposes a communication-efficient linearly convergent distributed (COLD) algorithm to solve strongly convex optimization problems. By compressing innovation vectors, which are the differences between decision vectors and their estimates, COLD is able to achieve linear convergence for a class of $\delta$-contracted compressors. We explicitly quantify how the compression affects the convergence rate and show that COLD matches the same rate of its uncompressed version. To accommodate a wider class of compressors that includes the binary quantizer, we further design a novel dynamical scaling mechanism and obtain the linearly convergent Dyna-COLD. Importantly, our results strictly improve existing results for the quantized consensus problem. Numerical experiments demonstrate the advantages of both algorithms under different compressors., Comment: 14 pages

This article focuses on the development of integrated sensing and communications (ISAC) from a multiple access (MA) perspective, where the idea of non-orthogonal multiple access (NOMA) is exploited for harmoniously accommodating the sensing and communication functionalities. We first reveal that the developing trend of ISAC is from \emph{orthogonality} to \emph{non-orthogonality}, and introduce the fundamental models of the downlink and uplink ISAC while identifying the design challenges from the MA perspective. (1) For the downlink ISAC, we propose two novel designs, namely \emph{NOMA-empowered} downlink ISAC and \emph{NOMA-inspired} downlink ISAC to effectively coordinate the inter-user interference and the sensing-to-communication interference, respectively. (2) For the uplink ISAC, we first propose a \emph{pure-NOMA-based} uplink ISAC design, where a fixed communication-to-sensing successive interference cancellation order is employed for distinguishing the mixed sensing-communication signal received over the fully shared radio resources. Then, we propose a general \emph{semi-NOMA-based} uplink ISAC design, which includes the conventional orthogonal multiple access-based and pure-NOMA-based uplink ISAC as special cases, thus being capable of providing flexible resource allocation strategies between sensing and communication. Along each proposed NOMA-ISAC design, numerical results are provided for showing the superiority over conventional ISAC designs., 7 pages, 5 figures

In the wideband regime, the performance of many of the popular modulation schemes such as code division multiple access and orthogonal frequency division multiplexing falls quickly without channel state information. Obtaining the amount of channel information required for these techniques to work is costly and difficult, which suggests the need for schemes which can perform well without channel state information. In this work, we present one such scheme, called wideband time frequency coding, which achieves rates on the order of the additive white Gaussian noise capacity without requiring any channel state information. Wideband time frequency coding combines impulsive frequency shift keying with pulse position modulation, which allows for information to be encoded in both the transmitted frequency and the transmission time period. On the detection side, we propose a non-coherent decoder based on a square-law detector, akin to the optimal decoder for frequency shift keying based signals. The impacts of various parameters on the symbol error probability and capacity of wideband time frequency coding are investigated, and the results show that it is robust to shadowing and highly fading channels. When compared to other modulation schemes such as code division multiple access, orthogonal frequency division multiplexing, pulse position modulation, and impulsive frequency shift keying without channel state information, wideband time frequency coding achieves higher rates in the wideband regime, and performs comparably in smaller bandwidths.

Achieving reliable acoustic wireless video transmissions in the extreme and uncertain underwater environment is a challenge due to the limited bandwidth and the error-prone nature of the channel. Aiming at optimizing the received video quality and the user's experience, an adaptive solution for underwater video transmissions is proposed that is specifically designed for Multi-Input Multi-Output (MIMO -based Software-Defined Acoustic Modems (SDAMs . To keep the video distortion under an acceptable threshold and to keep the Physical-Layer Throughput (PLT high, cross-layer techniques utilizing diversity-spatial multiplexing and Unequal Error Protection (UEP are presented along with the scalable video compression at the application layer. Specifically, the scalability of the utilized SDAM with high processing capabilities is exploited in the proposed structure along with the temporal, spatial, and quality scalability of the Scalable Video Coding (SVC H.264/MPEG-4 AVC compression standard. The transmitter broadcasts one video stream and realizes multicasting at different users. Experimental results at the Sonny Werblin Recreation Center, Rutgers University-NJ, are presented. Several scenarios for unknown channels at the transmitter are experimentally considered when the hydrophones are placed in different locations in the pool to achieve the required SVC-based video Quality of Service (QoS and Quality of Experience (QoE given the channel state information and the robustness of different SVC scalability. The video quality level is determined by the best communication link while the transmission scheme is decided based on the worst communication link, which guarantees that each user is able to receive the video with appropriate quality.

Intelligent reflecting surface (IRS) has received widespread attention because it can significantly improve the performance of wireless networks. The X-duplex is a more flexible duplex mode, which can decrease a small sum rate in exchange for a higher quality-of-service performance. In this article, an IRS transmission scheme based on X-duplex is investigated to enhance the flexibility and freedom of communication design. We present the X-duplex IRS with the simultaneous wireless information and power transfer, and establish a power consumption model based on real application scenarios. An alternating optimization algorithm is proposed to maximize the energy efficiency (EE) of a multi-input single-output (MIMO) network assisted by the X-duplex IRS. As for the X-duplex relay, we explore an algorithm based on the golden section to optimize the EE. Simulation results comprehensively evaluate the performance of the two auxiliary transmission methods. The X-duplex IRS has higher EE and weighted sum rate (WSR) than the X-duplex relay when sufficient transmission power is available. However, the IRS cannot entirely replace the traditional relay. The X-duplex relay with self-interference energy harvesting achieves a higher WSR and EE when the access point energy is limited or the transmission distance is short.

We consider a coded compressed sensing approach for the unsourced random access and replace the outer tree code proposed by Amalladinne et al. with the list recoverable code capable of correcting t errors. A finite-length random coding bound for such codes is derived. The numerical experiments in the single antenna quasi-static Rayleigh fading MAC show that transition to list recoverable codes correcting t errors improves the performance of coded compressed sensing scheme by 7-10 dB compared to the tree code-based scheme. We propose two practical constructions of outer codes. The first is a modification of the tree code. It utilizes the same code structure, and a key difference is a decoder capable of correcting up to t errors. The second is based on the Reed-Solomon codes and Guruswami-Sudan list decoding algorithm. The first scheme provides an energy efficiency very close to the random coding bound when the decoding complexity is unbounded. But for the practical parameters, the second scheme is better and improves the performance of a tree code-based scheme when the number of active users is less than 200.

An improved Singleton-type upper bound is presented for the list decoding radius of linear codes, in terms of the code parameters [n,k,d] and the list size L. L-MDS codes are then defined as codes that attain this bound (under a slightly stronger notion of list decodability), with 1-MDS codes corresponding to ordinary linear MDS codes. Several properties of such codes are presented; in particular, it is shown that the 2-MDS property is preserved under duality. Finally, explicit constructions for 2-MDS codes are presented through generalized Reed-Solomon (GRS) codes., Comment: Main changes from v1: replaced Theorem 4 by a stronger result and added Corollary 5, Lemma 8, and Corollary 9

In a time-varying massive multiple-input multipleoutput (MIMO) system, the acquisition of the downlink channel state information at the base station (BS) is a very challenging task due to the prohibitively high overheads associated with downlink training and uplink feedback. In this paper, we consider the hybrid precoding structure at BS and examine the antennatime domain channel extrapolation. We design a latent ordinary differential equation (ODE)-based network under the variational auto-encoder (VAE) framework to learn the mapping function from the partial uplink channels to the full downlink ones at the BS side. Specifically, the gated recurrent unit is adopted for the encoder and the fully-connected neural network is used for the decoder. The end-to-end learning is utilized to optimize the network parameters. Simulation results show that the designed network can efficiently infer the full downlink channels from the partial uplink ones, which can significantly reduce the channel training overhead., Comment: 5 pages, 5 figures

This correspondence paper studies a network integrated sensing and communication (ISAC) system that unifies the interference channel for communication and distributed radar sensing. In this system, a set of distributed ISAC transmitters send individual messages to their respective communication users (CUs), and at the same time cooperate with multiple sensing receivers to estimate the location of one target. We exploit the coordinated power control among ISAC transmitters to minimize their total transmit power while ensuring the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs and the maximum Cram\'{e}r-Rao lower bound (CRLB) requirement for target location estimation. Although the formulated coordinated power control problem is non-convex and difficult to solve in general, we propose two efficient algorithms to obtain high-quality solutions based on the semi-definite relaxation (SDR) and CRLB approximation, respectively. Numerical results show that the proposed designs achieve substantial performance gains in terms of power reduction, as compared to the benchmark with a heuristic separate communication-sensing design., Comment: 5 pages, 4 figures

In this paper, we propose a wideband Full Duplex (FD) Multiple-Input Multiple-Output (MIMO) communication system comprising of an FD MIMO node simultaneously communicating with two multi-antenna UpLink (UL) and DownLink (DL) nodes utilizing the same time and frequency resources. To suppress the strong Self-Interference (SI) signal due to simultaneous transmission and reception in FD MIMO systems, we propose a joint design of Analog and Digital (A/D) cancellation as well as transmit and receive beamforming capitalizing on baseband Orthogonal Frequency-Division Multiplexing (OFDM) signal modeling. Considering practical transmitter impairments, we present a multi-tap wideband analog canceller architecture whose number of taps does not scale with the number of transceiver antennas and multipath SI components. We also propose a novel adaptive digital cancellation based on truncated singular value decomposition that reduces the residual SI signal estimation parameters. To maximize the FD sum rate, a joint optimization framework is presented for A/D cancellation and digital beamforming. Finally, our extensive waveform simulation results demonstrate that the proposed wideband FD MIMO design exhibits higher SI cancellation capability with reduced complexity compared to existing cancellation techniques, resulting in improved achievable rate performance., 32 pages, 10 figures, Under Review for Publication in the IEEE Transactions on Wireless Communications

A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed decoder initializes $L$ $(L\ge1)$ active decoding paths with $L$ random codeword permutations sampled from the full symmetry group of the codes. The path extension in the permutation domain is carried out until the first constituent RM code of order $1$ is visited. Conventional path extension of the successive-cancellation list decoder is then utilized in the information bit domain. The simulation results show that for a RM code of length $512$ with $46$ information bits, by running $20$ parallel permuted FHT-FSCL decoders with $L=4$, we reduce $72\%$ of the computational complexity, $22\%$ of the decoding latency, and $84\%$ of the memory consumption of the state-of-the-art simplified successive-cancellation decoder that uses $512$ permutations sampled from the full symmetry group of the code, with similar error-correction performance at the target frame error rate of $10^{-4}$., Submitted to an IEEE journal for possible publication

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the potent encryption paradigms in protecting data confidentiality in the cloud data sharing scenario. However, the access policy of the traditional CP-ABE is in plaintext form that reveals significant sensitive information of data owners and data visitors. To mitigate this problem, two approaches have been proposed in the literature. One is partially hidden, where the attributes in the access policy are divided into two parts: the plaintext attribute names and the hidden attribute values. The other approach fully hides the attributes in the access policy which, unfortunately, hinders efficient and correct decryption as well as dynamic policy-updating. In this paper, we design a security-enhanced Attribute Cuckoo Filter (se-ACF) to hide the access policy and propose a new CP-ABE system, called Privacy-Preserving Policy Updating ABE (3PU-ABE), which effectively integrates policy hiding and policy updating. We conduct rigorous security analysis and performance evaluation of 3PU-ABE. The results indicate that 3PU-ABE completely hides the access policy without affecting the decryption, and entails better policy-updating efficiency than similar works.

The existing relay-assisted terahertz (THz) wireless system is limited to dual-hop transmission with pointing errors and short-term fading without considering the shadowing effect. This paper analyzes the performance of a multihop-assisted backhaul communication mixed with an access link under the shadowed fading with antenna misalignment errors. We derive statistical results of the signal-to-noise ratio (SNR) of the multihop link by considering independent but not identically distributed (i.ni.d) $\alpha$-$\mu$ fading channel with pointing errors employing channel-assisted (CA) and fixed-gain (FG) amplify-and-forward (AF) relaying for each hop. We analyze the outage probability, average BER, and ergodic capacity performance of the mixed system considering the generalized-$K$ shadowed fading model with AF and decode-and-forward (DF) protocols employed for the access link. We derive exact expressions of the performance metrics for the CA-multihop system with the DF relaying for the last hop and upper bound of the performance for the FG-multihop system using FG and DF relaying at the last relay. We also develop asymptotic analysis in the high SNR to derive the diversity order of the system and use computer simulations to provide design and deployment aspects of multiple relays in the backhaul link to extend the communication range for THz wireless transmissions., Comment: This work has been submitted to the IEEE for possible publication

Several information-theoretic studies on channels with output quantization have identified the capacity-achieving input distributions for different fading channels with 1-bit in-phase and quadrature (I/Q) output quantization. However, an exact characterization of the capacity-achieving input distribution for channels with multi-bit phase quantization has not been provided. In this paper, we consider four different channel models with multi-bit phase quantization at the output and identify the optimal input distribution for each channel model. We first consider a complex Gaussian channel with $b$-bit phase-quantized output and prove that the capacity-achieving distribution is a rotated $2^b$-phase shift keying (PSK). The analysis is then extended to multiple fading scenarios. We show that the optimality of rotated $2^b$-PSK continues to hold under noncoherent fast fading Rician channels with $b$-bit phase quantization when line-of-sight (LoS) is present. When channel state information (CSI) is available at the receiver, we identify $\frac{2\pi}{2^b}$-symmetry and constant amplitude as the necessary and sufficient conditions for the ergodic capacity-achieving input distribution; which a $2^b$-PSK satisfies. Finally, an optimum power control scheme is presented which achieves ergodic capacity when CSI is also available at the transmitter., Comment: 23 pages, 4 figures, Accepted for publication in IEEE Transactions on Information Theory

Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. Then, we present a modeling framework that exploits the aforementioned coding architecture. The proposed mathematical problem minimizes the power consumption by jointly determining the encoding rate and multi-path rate allocation subject to distortion and energy constraints. Extensive performance evaluation results show that the proposed framework is able to transmit multi-view streams with guaranteed video quality at lower power consumption.

In this article, we consider the millimeter-wave simultaneous wireless information and power transfer nonorthogonal multiple access (NOMA) relay networks in the presence of multiple passive eavesdroppers, where the near and far Internet of Things (IoT) devices with different communication requirements are served by one source with the help of the relays, and stochastic geometry is used to characterize the distribution of multiple nodes. First, a NOMA pairing and relay selection scheme is designed. Then, the closed-form expressions for the energy-information coverage probability (EICP) and the effective secrecy throughput of the near IoT devices are derived. Finally, the EICPs of the far IoT device under random relay selection and opportunistic relay selection schemes are obtained. Moreover, the asymptotic expressions of the EICP for both the near and far IoT devices are derived, which show that the asymptotic EICP when the number of antennas at the source or relay tends to infinity is only dependent on the location distribution of nodes and blockage environment. The simulation results show that NOMA can outperform orthogonal multiple access, and the advantage of the opportunistic relay selection scheme over the random relay selection scheme is more obvious when the density of the relay is relatively large.

In this article, the effects of multiantenna users and low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) on multigroup multicast downlink cell-free massive multi-input multi-output (MIMO) systems are explored. Leveraging on the distributed conjugate beamforming receiver and the generalized additive quantization noise model, we first derive a closed-form expression for the achievable sum spectral efficiency (SE). The resultant expression enables us to precisely quantify the effects of key parameters, such as the transmit power of access points (APs), the numbers of groups, users’ antennas, APs’ antennas, and quantization bits. Due to the joint influence of channel hardening property, multiantenna gain, and intergroup interference, the research becomes sophisticated particularly. The result shows that when the sum-SE is small, the multicast is better than the unicast and vice versa. Typically, it reveals that the full-resolution DAC can be replaced by 5-bit low-resolution DAC to achieve the same performance, and the additional antenna at users can effectively enhance the system performance. Then, according to a generated energy cost model, the formula of the energy efficiency (EE) is obtained. When the DAC quantization is equal to 2-bit, it can achieve the maximum sum EE. Finally, the simulation results are provided to verify the derived results.

Cloud-based VANETs are designed to enable communication between high-speed vehicles. In such a highly dynamic environment, how to provide secure and anonymous communication service is a challenge. In this paper, we affirmatively address the challenge by proposing a traitor-resistant and dynamic anonymous communication framework (TD-ACF) for cloud-based VANETs. In TD-ACF, each vehicle is represented by a set of attributes and the driving data is transmitted in encrypted form. Therefore, the anonymous authentication and the confidentiality of driving data are achieved. Meanwhile, TD-ACF supports two practical requirements in cloud-based VANETs: the revocation and the traceability of traitor. For the former, TD-ACF can force a vehicle to exit the communication network at any moment. We employ an efficient binary tree algorithm to reduce the size of key updates for revocation from the traditional linear to the logarithmic level. For the latter, we overcome the barrier of the one-to-many relationship between a vehicle and the shared set of attributes to support traitor tracing. In TD-ACF, the Semi-Trusted Cloud (STC) can directly capture and punish a traitor instead of querying all the records in the list of unrevoked vehicles. The theoretical analysis and experimental simulation show that our scheme is feasible and effective.

This paper proposes a novel broadband transmission technology, termed delay alignment modulation (DAM), which enables the low-complexity equalization-free single-carrier communication, yet without suffering from inter-symbol interference (ISI). The key idea of DAM is to deliberately introduce appropriate delays for information-bearing symbols at the transmitter side, so that after propagating over the time-dispersive channel, all multi-path signal components will arrive at the receiver simultaneously and constructively. We first show that by applying DAM for the basic multiple-input single-output (MISO) communication system, an ISI-free additive white Gaussian noise (AWGN) system can be obtained with the simple zero-forcing (ZF) beamforming. Furthermore, the more general DAM scheme is studied with the ISI-maximal-ratio transmission (MRT) and the ISI-minimum mean-square error (MMSE) beamforming. Simulation results are provided to show that when the channel is sparse and/or the antenna dimension is large, DAM not only resolves the notorious practical issues suffered by orthogonal frequency-division multiplexing (OFDM) such as high peak-to-average-power ratio (PAPR), severe out-of-band (OOB) emission, and vulnerability to carrier frequency offset (CFO), with low complexity, but also achieves higher spectral efficiency due to the saving of guard interval overhead., Comment: 5 pages, 6 figures

In this article, a two-hop simultaneous wireless information and power transfer relay network using a decode-and-forward strategy is considered. In order to improve the spectral efficiency, the relay node performs in full-duplex (FD) mode, and each node is equipped with multiple antennas. First, the end-to-end achievable rate is formulated for FD and half-duplex (HD) modes under imperfect channel state information (ICSI). Then, the optimal closed-form expressions for power allocation at the source and relay, as well as the energy harvesting ratios, are derived for primal variables associated with the dual variables through analytical solutions. We propose an iterative primal-dual algorithm based on the ellipsoid method so as to obtain final answers. Numerical results demonstrate the FD achievable rate superiority over the HD mode under proper self-interference cancellation (SIC). According to the existing results, there is an immense discrepancy between the achievable rates in perfect CSI and ICSI cases. Also, the impact of the relay distance from the source node, the number of antennas, and different SIC modes are investigated on the end-to-end achievable rate.

Lately, many cloud-based applications proposed Attribute-Based Encryption (ABE) as an all-in-one solution for achieving confidentiality and access control. Within this paradigm, data producers store the encrypted data on a semi-trusted cloud server, and users, holding decryption keys issued by a key authority, can decrypt data according to some access control policy. To be used in practical cases, any ABE scheme should implement a key revocation mechanism which assures that a compromised decryption key cannot be used anymore to decrypt data. Yu et al. (INFOCOM 2010) introduced an ABE scheme with revocation capabilities that enjoys several unique advantages, such as reactivity and efficiency. In the scheme, the cloud server is entitled to update keys and ciphertexts in order to achieve revocation. Unfortunately, the cloud server retains the power to undo the revocation of a key (revocation undoing attack) so endangering confidentiality. In this paper, we propose a revocable ABE scheme that still ensures the advantages of Yu et al.'s scheme, but it also resists to the revocation undoing attack. We formally prove the security of our scheme and show through simulations that the user experiences a slightly higher computational cost with respect to Yu et al.'s scheme.

There is an urgent need to solve the problems of secure storage, reliable sharing, access control and privacy protection in medical industry. In this paper, we propose EHRChain, a blockchain-based EHR system using attribute-based and homomorphic cryptosystem to solve the above problems. First, we design a medical record storage scheme to realize secure high capacity medical data storage and reliable sharing based on blockchain technology and IPFS. Second, we propose an improved cryptographic primitive called SHDPCPC-CP-ABE. Our SHDPCPC-CP-ABE realizes the functions of semi-policy hiding and dynamic permission changing based on partial ciphertext simultaneously. Furthermore, our program achieves the neutrality of the subject of judicial identification in medical disputes and fine-grained access control of medical data. Third, our system applies an additive homomorphic cryptosystem, Paillier cryptosystem with optimized parameters on patients privacy protection during the process of the medical insurance claim. After analysis and experiment, we have proved that the SHDPCPC-CP-ABE is indistinguishable under chosen plaintext attack and takes one third of the time of CP-ABE when changing access policy. Our system has higher performance than other EHR systems based on blockchain.

Previous neural network accelerators tailored to video analysis only accept data of RGB/YUV domain, requiring decompressing the video that are often compressed before transmitted from the edge sensors. A compressed video processing accelerator can alleviate the decoding overhead, and gain performance speedup by operating on more compact input data. This work proposes a novel deep learning accelerator architecture, Alchemist, which is able to predict results directly from the compressed video bitstream instead of reconstructing the full RGB images. By utilizing the metadata of motion vector and critical blocks extracted from bitstreams, Alchemist contributes to remarkable performance speedup of 5x with negligible accuracy loss. Nevertheless, we still find that the original compressed video coded by standard algorithms such as H.264 is not suitable to be directly manipulated, due to diverse compressed structures. Although obviating the requirement to recover all RGB frames, the accelerator must parse the entire compressed video bitstream to locate reference frames and extract useful metadata. If we combine the video codec with the proposed compressed video analysis, additional optimizations can be obtained. Therefore, to cope with the mismatch between current video coding algorithms such as H.264 and neural network-based video analysis, we propose a specialized coding strategy to generate compressed video bitstreams more suitable for transmission and analysis, which further simplifies the decoding stage of video analysis and is capable of achieving significant storage reduction.

Recently, semantic communication has been brought to the forefront because of its great success in deep learning (DL), especially Transformer. Even if semantic communication has been successfully applied in the sentence transmission to reduce semantic errors, existing architecture is usually fixed in the codeword length and is inefficient and inflexible for the varying sentence length. In this paper, we exploit hybrid automatic repeat request (HARQ) to reduce semantic transmission error further. We first combine semantic coding (SC) with Reed Solomon (RS) channel coding and HARQ, called SC-RS-HARQ, which exploits the superiority of the SC and the reliability of the conventional methods successfully. Although the SC-RS-HARQ is easily applied in the existing HARQ systems, we also develop an end-to-end architecture, called SCHARQ, to pursue the performance further. Numerical results demonstrate that SCHARQ significantly reduces the required number of bits for sentence semantic transmission and sentence error rate. Finally, we attempt to replace error detection from cyclic redundancy check to a similarity detection network called Sim32 to allow the receiver to reserve the wrong sentences with similar semantic information and to save transmission resources., Comment: IEEE Transactions on Communications, 2022

In this work, we study probabilistic shaping (PS) for optical wireless communications and apply the technique to wavelength division multiplexing (WDM) based visible light communication (VLC). The performance of the proposed scheme is validated with an experimental demonstration. The experimental set up uses lenses to collimate the light beam for triple LEDs. The system parameters of the WDM based VLC system are then optimised, the channel response measured, and PS based symbols are allocated to the individual orthogonal frequency division multiplexing (OFDM) subcarriers. For the channel conditions under consideration, PS resulted in a near Shannon capacity transmission rate of 10.81 Gb/s. Comparatively, the PS resulted in 25 % higher transmission rate than the widely used adaptive bit-power loading algorithm under the same channel conditions.

In this paper, we consider a status updating system where updates are generated at a constant rate at $K$ sources and sent to the corresponding recipients through a noise-free broadcast channel. We assume that perfect channel state information (CSI) is available at the transmitter before each transmission, and the transmitter is able to utilize the CSI information to precode the updates. Our object is to design optimal precoding schemes to minimize the summed average \emph{age of information} (AoI) at the recipients. Under various assumptions on the size of each update $B$, the number of transmit antennas $M$, and the number of receive antennas $N$ at each user, this paper identifies the corresponding age-optimal precoding and transmission scheduling strategies. Specifically, for the case when $N=1$, a round-robin based updating scheme is shown to be optimal. For the two-user systems with $N>B$ or $M\notin[N:2N]$, framed updating schemes are proven to be optimal. For other cases in the two-user systems, a framed alternating updating scheme is proven to be $2$-optimal.

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the "transfer learning'' assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.

We investigate state estimation of linear systems over channels having a finite state not known by the transmitter or receiver. We show that similar to memoryless channels, zero-error capacity is the right figure of merit for achieving bounded estimation errors. We then consider finite-state, worst-case versions of the common erasure and additive noise channels models, in which the noise is governed by a finite-state machine without any statistical structure. Upper and lower bounds on their zero-error capacities are derived, revealing a connection with the topological entropy of the channel dynamics. Separate necessary and sufficient conditions for bounded linear state estimation errors via such channels are obtained. These estimation conditions bring together the topological entropies of the linear system and the discrete channel.

As a new joint source-channel coding scheme, the double polar (D-Polar) codes have been proposed recently. In this letter, a novel joint source-channel decoder, namely the joint successive cancellation list (J-SCL) decoder, is proposed to improve the decoding performance of the D-Polar codes. We merge the trellis of the source polar code and that of the channel polar code to construct a compound trellis. In this compound trellis, the joint source-channel nodes represent both of the information bits and the high-entropy bits. Based on the compound trellis, the J-SCL decoder is designed to recover the source messages by combining the source SCL decoding and channel SCL decoding. The J-SCL decoder doubles the number of the decoding paths at each decoding level and then reserves the L paths with the smallest joint path-metric (JPM). For the JSC node, the JPM is updated considering both the channel decision log-likelihood ratios (LLRs) and the source decision LLRs. Simulation results show that the J-SCL decoder outperforms the turbo-like BP (TL-BP) decoder with lower complexity.

In this work, we investigate hybrid analog-digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. {We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the AP, whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement.} Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate., 32 pages, 5 figures

This letter analyzes the performance of uplink integrated sensing and communications (ISAC) systems where communication users (CUs) and radar targets (RTs) share the same frequency band. A non-orthogonal multiple access (NOMA) protocol is adopted in the communication procedure of the ISAC system. Novel expressions are derived to characterize the outage probability, ergodic communication rate, and sensing rate. Besides, the diversity order and high signal-to-noise ratio (SNR) slope are unveiled to gain further insights. It is found that when achieving the same communication rate, the ISAC system enjoys a higher sensing rate than the conventional frequency-division sensing and communications (FDSAC) system where CUs and RTs share isolated bands. All the results are validated by numerical simulations and are in excellent agreement., 5 pages

In this paper, we address the antenna selection (AS) problem in full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) systems, where a multi-antenna FD relay bridges the connection between the multi-antenna base station and NOMA far user. Specifically, two AS schemes, namely max-$\SUu$ and max-$\SUuu$, are proposed to maximize the end-to-end signal-to-interference-plus-noise ratio at either or both near and far users, respectively. Moreover, a two-stage AS scheme, namely quality-of-service (QoS) provisioning scheme, is designed to realize a specific rate at the far user while improving the near user's rate. To enhance the performance of the QoS provisioning AS scheme, the idea of dynamic antenna clustering is applied at the relay to adaptively partition the relay's antennas into transmit and receive subsets. The proposed AS schemes' exact outage probability and achievable rate expressions are derived. To provide more insight, closed-form asymptotic outage probability expressions for the max-$\SUu$ and max-$\SUuu$ AS schemes are obtained. Our results show that while the QoS provisioning AS scheme can deliver a near-optimal performance for static antenna setup at the relay, it provides up to $12\%$ average sum rate gain over the optimum AS selection with fixed antenna setup., Comment: Submitted to IEEE Transactions on Communications

This article studies the resilient control problem for a class of unknown nonlinear systems with fading measurements under malicious denial-of-service (DoS) attacks. The system output is assumed to be transmitted through a fading channel, where the fading phenomenon is described by a Rice fading model. The strategy of the attacker is to periodically interfere with the networked channels to reduce the success rate of data transmissions. First, a dynamic linearization method along the iteration domain is introduced to convert the nonlinear system into an equivalent data-related model. Then, a model-free adaptive iterative learning control (MFAILC) scheme is presented, which is independent of model information. The convergence of the MFAILC scheme is deduced theoretically and the influence of DoS attacks and stochastic fading phenomenon on system stability are also analyzed. Finally, the effectiveness of the design is verified by a numerical simulation and a trajectory tracking example of wheeled mobile robots (WMRs).