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It is well-known that dirty paper coding (DPC)-based transceiver can achieve the sum capacity of a multi-input multi-output (MIMO) broadcast channel. In this paper, we study DPC-based hybrid precoding to maximize the sum-rate of a massive MIMO (mMIMO) downlink broadcast channel, subject to the constant modulus constraint of the analog phase shifter network (PSN). Using the duality between a massive MIMO broadcast channel (mMIMO-BC) and a massive MIMO multiple access channel (mMIMO-MAC), we propose to first optimize the transmitters and the hybrid receiver for the mMIMO-MAC, before obtaining the hybrid precoder of mMIMO-BC according to the MAC-BC duality. The algorithm is also extended for mMIMO orthogonal frequency division multiplexing (OFDM) systems over broadband frequency-selectivity channels. Numerical results show that the proposed DPC-based hybrid precoding can significantly outperform the existing linear counterpart, especially in the case of numerous users. In simulations based on the 5G new radio (5G-NR) standard, the relative gain of the proposed scheme appears prominent, which suggests that the DPC can be a worthy candidate for the future generation of wireless communications.

This paper presents a coding approach for achieving omnidirectional transmission of certain common signals in massive multi-input multi-output (MIMO) networks such that the received power at any direction in a cell remains constant for any given distance. Specifically, two-dimensional (2D) Golay complementary array set (GCAS) can be used to design the massive MIMO precoding matrix so as to achieve omnidirectional transmission due to its complementary autocorrelation property. In this paper, novel constructions of new 2D GCASs with arbitrary array lengths are proposed. Our key idea is to carefully truncate the columns of certain larger arrays generated by 2D generalized Boolean functions. Finally, the power radiation patterns and numerical results are provided to verify the omnidirectional property of the GCAS-based precoding. The error performances of the proposed precoding scheme are presented to validate its superiority over the existing alternatives.

This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-to-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-to-outdoor mmWave communications. By jointly optimizing the TAIS’s phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS’s amplification process. The key enabler is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important enabler is that we design a low-complexity algorithm to solve the optimization problem using the difference of convex programming. Moreover, the asymptotic spectral efficiency under nonlinear amplification and power scaling law with infinitely large elements under both the sparse and rich scattering channel models are analyzed. Simulation results show that our proposed TAIS can increase the SE by up to 24.7% as compared to its alternative methods.

The aim of this paper is to reveal the importance of hybrid successive interference cancellation (SIC) and power adaptation (PA) for improving transmission robustness of uplink non-orthogonal multiple access (NOMA). Particularly, a cognitive radio inspired uplink NOMA communication scenario is considered, where one primary user is allocated one dedicated resource block, while $M$ secondary users compete with each other to be opportunistically served by using the same resource block of the primary user. Two novel schemes are proposed for the considered scenario, namely hybrid SIC with PA (HSIC-PA) scheme and fixed SIC with PA (FSIC-PA) scheme. Both schemes can ensure that the secondary users are served without degrading the transmission reliability of the primary user compared to conventional orthogonal multiple access (OMA) based schemes. The novelty of the proposed schemes compared to those existing schemes is the introduction of power adaptation. This paper presents rigorous analytical results to show that both schemes can avoid outage probability error floors without any constraints on users’ target rates in the high SNR regime, which cannot be achieved by the existing schemes. Furthermore, it is shown that the diversity gain achieved by the HSIC-PA scheme is $M$ , while that of the FISC-PA scheme is only 1. Numerical results are provided to verify the developed analytical results and also demonstrate the superior performance achieved by the proposed schemes by comparing with the existing HSIC without PA (HSIC-NPA) scheme. The presented simulation results also show that HSIC-PA scheme performs the best among the three schemes, which indicates the importance of the combination of HSIC and PA for improving transmission robustness.

Recently, neural networks have improved MinSum message-passing decoders for low-density parity-check (LDPC) codes by multiplying or adding weights to the messages, where the weights are determined by a neural network. The neural network complexity to determine distinct weights for each edge is high, often limiting the application to relatively short LDPC codes. Furthermore, storing separate weights for every edge and every iteration can be a burden for hardware implementations. To reduce neural network complexity and storage requirements, this paper proposes a family of weight-sharing schemes that use the same weight for edges that have the same check node degree and/or variable node degree. Our simulation results show that node-degree-based weight-sharing can deliver the same performance requiring distinct weights for each node. This paper also combines these degree-specific neural weights with a reconstruction-computation-quantization (RCQ) decoder to produce a weighted RCQ (W-RCQ) decoder. The W-RCQ decoder with node-degree-based weight sharing has a reduced hardware requirement compared with the original RCQ decoder. As an additional contribution, this paper identifies and resolves a gradient explosion issue that can arise when training neural LDPC decoders.

In a recent paper it has been shown that to model a diffusive molecular communication (MC) channel with multiple fully absorbing (FA) receivers, these can be interpreted as sources of negative particles from the other receivers’ perspective. The barycenter point is introduced as the best position where to place the negative sources. The barycenter is obtained from the spatial mean of the molecules impinging on the surface of each FA receiver. This paper derives an expression that captures the position of the barycenter in a diffusive MC channel with multiple FA receivers. In this work, a heuristic model inspired by Newton’s law of gravitation is found to describe the barycenter, and the result is compared with particle-based simulation (PBS) data. Since the barycenter depends on the distance between the transmitter and receiver and the observation time, the condition that the barycenter can be assumed to be at the center of the receiver is discussed. This assumption simplifies further modeling of any diffusive MC system containing multiple FA receivers. The resulting position of the barycenter is used in channel models to calculate the cumulative number of absorbed molecules and it has been verified with PBS.

Recently, rate- $1/n$ zero-terminated (ZT) and tail-biting (TB) convolutional codes (CCs) with cyclic redundancy check (CRC)-aided list decoding have been shown to closely approach the random-coding union (RCU) bound for short blocklengths. This paper designs CRC polynomials for rate- $(n-1)/n$ ZT and TB CCs with short blocklengths. This paper considers both standard rate- $(n-1)/n$ CC polynomials and rate- $(n-1)/n$ designs resulting from puncturing a rate- $1/2$ code. The CRC polynomials are chosen to maximize the minimum distance $d_{\min }$ and minimize the number of nearest neighbors $A_{d_{\min }}$ . For the standard rate- $(n-1)/n$ codes, utilization of the dual trellis proposed by Yamada et al. lowers the complexity of CRC-aided serial list Viterbi decoding (SLVD). CRC-aided SLVD of the TBCCs closely approaches the RCU bound at a blocklength of 128. This paper compares the FER performance (gap to the RCU bound) and complexity of the CRC-aided standard and punctured ZTCCs and TBCCs. This paper also explores the complexity-performance trade-off for three TBCC decoders: a single-trellis approach, a multi-trellis approach, and a modified single-trellis approach with pre-processing using the wrap around Viterbi algorithm.

The reconfigurable intelligent surface (RIS) is useful to effectively improve the coverage and data rate of end-to-end communications. In contrast to the well-studied coverage-extension use case, in this paper, multiple RIS panels are introduced, aiming to enhance the data rate of multi-input multi-output (MIMO) channels in presence of insufficient scattering. Specifically, via the operator-valued free probability theory, the asymptotic mutual information of the large-dimensional RIS-assisted MIMO channel is obtained under the Rician fading with Weichselberger’s correlation structure, in presence of both the direct and the reflected links. Although the mutual information of Rician MIMO channels scales linearly as the number of antennas and the signal-to-noise ratio (SNR) in decibels, numerical results show that it requires sufficiently large SNR, proportional to the Rician factor, in order to obtain the theoretically guaranteed linear improvement. This paper shows that the proposed multi-RIS deployment is especially effective to improve the mutual information of MIMO channels under the large Rician factor conditions. When the reflected links have similar arriving and departing angles across the RIS panels, a small number of RIS panels are sufficient to harness the spatial degree of freedom of the multi-RIS assisted MIMO channels.

Beneš networks are well-known rearrangeable nonblocking (RNB) multistage networks. The so-called conventional Beneš networks are based on $2\times 2$ switches. In this paper, Beneš network is a general term used to refer to an $N\times {N}~n$ -nary Beneš network. Such networks, denoted by B( $n$ , $t)$ , where $N = n^{t}$ and $t \ge2$ , are based on regular $n\times n$ switches, and are RNB as well. A Beneš network is constructed recursively from a 3-stage Clos network. For an $N\times N$ RNB Clos network C( $n$ , $m$ , $r)$ , where $N = nr$ and $m \ge n$ , the maximum allowable number of non-contact faults in each single shell for realizing any permutation has been investigated in an earlier study, where shell $k$ in a network consists of both the $k$ th and the $k$ th-to-last node stages. That study showed that, for a given integer $N$ , an RNB C( $n$ , $n$ , $r)$ network with larger $n\times n$ switches leads to tolerance of more non-contact faults in shell 1. In this paper, for an $N\times N$ B( $n$ , $t)$ network, we study the maximum allowable number, say $f_{k}$ , of non-contact faults for any permutation not only in each single shell $k$ , but also in all shells simultaneously under the fault condition that at most $f_{k}$ non-contact faults are arbitrarily located in the switches in each shell $k$ . We call the former the fault tolerance capability in a single shell, and the latter the fault tolerance capability of the network. We show that a larger switch size, i.e., $n\times n$ , in an ${N} \times N$ B( $n$ , $t)$ network leads to a higher fault tolerance capability of the network and a higher fault tolerance capability in each non-middle shell. An $N\times N$ Beneš network B( $n$ , $t)$ considers only the value of $N$ which is a power of $n$ . To consider a flexible $N$ with $N = n^{s}\cdot \textit {q}$ , where $s\ge2$ , $1 < {q} < n$ and ${q}\vert n$ , which means that $n$ is divisible by ${q}$ , we propose in this paper an $N\times N$ RNB Beneš-type network using regular $n\times n$ switches, which is called an extended Beneš and denoted by B( $n$ , $s$ , ${q})$ . Both Beneš and extended Beneš networks are based on regular switches, and they have better scalabilities than a Clos network. We define a network’s fault tolerance rate as the ratio of the fault tolerance capability to the total crosspoints in the network. For given integers $N$ and $n$ , we derive that the fault tolerance capability and fault tolerance rate of an ${N} \times N$ Beneš (or extended Beneš) network are higher than or equal to those of an $N\times N$ RNB C( $n$ , $n$ , $r)$ network, and the former outperforms the latter in most cases.

Cell-free massive multiple-input multiple-output (mMIMO) has recently been proposed to improve cell edge performance. However, most prior works consider perfect hardware impairments (HIs), which are difficult to be achieved in practical systems. This paper studies the impact of HI in an uplink cell-free mMIMO system with both multi-antenna access points (APs) and multi-antenna user terminals (UTs) under the Weichselberger channel model.Firstly, we study a two-layer decoding scheme with local minimum mean-squared error or maximum ratio combining at the AP side and with optimal large-scale fading decoding in the central processing unit. We derive novel closed-form SE expressions and prove that the effect of HI can be mitigated in the case of UTs with multiple antennas. However, the achievable SE is constrained by the pilot contamination and pilot overhead. To this end, the superimposed pilot (SP) transmission method is considered in this paper, where all the coherence intervals are used for both pilot and data symbols transmission. Finally, numerical results verify our derived expressions and reveal the relationship between HI and the number of antennas per UT for different pilot schemes. Note that the advantages of SP over regular pilots disappear when the hardware quality decreases with multi-antenna UTs.

Orthogonal time frequency space (OTFS) modulation has recently emerged as a potential 6G candidate waveform which provides improved performance in high-mobility scenarios. In this paper we investigate the combination of OTFS with non-orthogonal multiple access (NOMA). Existing equalization and detection methods for OTFS-NOMA, such as minimum-mean-squared error with successive interference cancellation (MMSE-SIC), suffer from poor performance. Additionally, existing iterative methods for single-user OTFS based on low-complexity iterative least-squares solvers are not directly applicable to the NOMA scenario due to the presence of multi-user interference (MUI). Motivated by this, in this paper we propose a low-complexity method for equalization and detection for OTFS-NOMA. The proposed method uses a novel reliability zone (RZ) detection scheme which estimates the reliable symbols of the users and then uses interference cancellation to remove MUI. The thresholds for the RZ detector are optimized in a greedy manner to further improve detection performance. In order to optimize these thresholds, we modify the least squares with QR-factorization (LSQR) algorithm used for channel equalization to compute the post-equalization mean-squared error (MSE), and track the evolution of this MSE throughout the iterative detection process. Numerical results demonstrate the superiority of the proposed equalization and detection technique to the existing MMSE-SIC benchmark in terms of symbol error rate (SER).

In classical coding theory, it is common to construct new codes via propagation rules. There are various propagation rules to construct classical block codes. However, propagation rules have not been extensively explored for locally repairable codes. In this paper, we systematically study some of propagation rules to construct optimal and asymptotically good locally repairable codes. To our surprise, these simple propagation rules produce interesting results. Firstly, by a lengthening propagation rule that adds some rows and columns to a parity-check matrix of a given linear code, we are able to convert a classical maximum distance separable (MDS) code into a Singleton-optimal locally repairable code and provide a simplified proof of the asymptotic Tafasman-Vlăduţ-Zink bound which exceeds the asymptotic Gilbert-Varshamov bound of locally repairable codes. Secondly, by concatenating a locally repairable code as an inner code with a classical block code as an outer code, we obtain a family of dimension-optimal locally repairable codes. Thirdly, we can make use of the shortening technique to produce more dimension-optimal locally repairable codes. Finally, one of phenomena that we observe in this paper is that some trivial propagation rules in classical block codes do not hold anymore for locally repairable codes.

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually contains two types of propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature and dominant propagation-path feature, especially in channel environments with rich scatterers. To fully extract the both propagation features and learn a dual-feature representation for CSI, this paper proposes a dual-feature-fusion neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a parallel structure with a convolutional neural network (CNN) and an attention-empowered neural network (ANN) to respectively extract different features in the CSI, and then explores their interplay by a fusion NN. Built upon this proposed DuffinNet, a new encoder-decoder framework is developed, referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI compression and reconstruction. To facilitate the application of Duffin-CsiNet in practice, this paper also presents a two-stage approach for codeword quantization of the CSI feedback. Besides, a transfer learning-based strategy is introduced to improve the generalization of Duffin-CsiNet, which enables the network to be applied to new propagation environments. Simulation results illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing DL-based methods in terms of reconstruction performance, encoder complexity, and network convergence, validating the effectiveness of the proposed dual-feature fusion design.

In this paper, we derive a theoretical upper bound on the generalization error of reservoir computing (RC), a special category of recurrent neural networks (RNNs). The specific RC implementation considered in this paper is the echo state network (ESN), and an upper bound on its generalization error is derived via the empirical Rademacher complexity (ERC) approach. While recent work in deriving risk bounds for RC frameworks makes use of a non-standard ERC measure and a direct application of its definition, our work uses the standard ERC measure and tools allowing fair comparison with conventional RNNs. The derived result shows that the generalization error bound obtained for ESNs is tighter than the existing bound for vanilla RNNs, suggesting easier generalization for ESNs. With the ESN applied to symbol detection in MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) systems, we show how the derived generalization error bound can guide underlying system design. Specifically, the derived bound together with the empirically characterized training loss is utilized to identify the optimum reservoir size in neurons for the ESN-based symbol detector. Finally, we corroborate our theoretical findings with results from simulations that employ 3GPP standards-compliant wireless channels, signifying the practical relevance of our work.

Reverse analysis of the intercepted signal to reconstruct the communication scheme used by the transmitter is a key problem in the non-cooperative context. In this paper, we propose a new algorithm for the joint blind reconstruction of the cyclic codes and self-synchronous scramblers directly using soft-decision sequences. First, we derive the conclusion that if the parity polynomial of the factor of the generator polynomial of the cyclic code and the feedback polynomial of the self-synchronous scrambler are multiplied, then the vector corresponding to the inverse polynomial of this product forms a parity-check relation with the row vector of the self-synchronously scrambled cyclic code bit matrix, which is constructed by the sequence of self-synchronously scrambled cyclic codes. To detect this parity-check relation, the concept of average parity-check probability (APCP) is defined, which uses the intercepted soft-decision sequence to measure the reliability of the parity-check relation. An optimal threshold based on the minimum error criterion is then derived. When the APCP is greater than this optimal threshold, the parity-check relation is considered to be detected, i.e., the corresponding irreducible polynomial is a factor of the generator polynomial of the cyclic code and the corresponding candidate feedback polynomial is the feedback polynomial of the self-synchronous scrambler. Therefore, the blind reconstruction problem in this paper is exactly equivalent to the hypothesis-testing problem. Simulation results show that the algorithm is more fault-tolerant than existing algorithms in noisy environments.

In this paper, the paradigm of thermal noise communication (TherCom) is put forward for future wired/wireless networks with extremely low power consumption. Taking backscatter communication (BackCom) and reconfigurable intelligent surface (RIS)-based radio frequency chain-free transmitters one step further, a thermal noise-driven transmitter might enable zero-signal-power transmission by simply indexing resistors or other noise sources according to information bits. This preliminary paper aims to shed light on the theoretical foundations, transceiver designs, and error performance derivations as well as optimizations of two emerging TherCom solutions: Kirchhoff-law-Johnson-noise (KLJN) secure bit exchange and wireless thermal noise modulation (TherMod) schemes. Our theoretical and computer simulation findings reveal that noise variance detection, supported by sample variance estimation with carefully optimized decision thresholds, is a reliable way of extracting the embedded information from noise modulated signals, even with limited number of noise samples.

This paper aims to present novel constructions of Golay-ZCZ sequence sets with various set sizes and flexible lengths. A Golay-ZCZ sequence set is not only a Golay complementary set (GCS) but also a zero correlation zone (ZCZ) sequence set. Golay-ZCZ sequence sets have been employed in OFDM systems due to their low peak-to-average power ratios (PAPRs) which are upper bounded by the set sizes. Although several constructions of Golay-ZCZ sequence sets have been proposed in the literature, the known Golay-ZCZ sets cannot attain the theoretical upper bound on the ZCZ width for non-binary cases. Also, for the Golay-ZCZ sets constructed by generalized Boolean functions, parameters are limited to powers of two, such as set sizes, sequence lengths, and ZCZ widths. In this paper, constructions of Golay-ZCZ sequence sets based on the extended generalized Boolean functions are proposed, where set sizes, sequence lengths, and ZCZ widths are all flexible and not necessary to be power-of-two. Since the constructed Golay-ZCZ sets have various set sizes, their PAPR upper bounds can be tighter. In addition, the proposed Golay-ZCZ sequence sets can achieve the upper bound on the ZCZ width. Therefore, optimal and almost-optimal Golay-ZCZ sequence sets can be obtained by the proposed methods.

This paper investigates how to apply non-orthogonal multiple access (NOMA) as an add-on in terahertz (THz) networks. In particular, prior to the implementation of NOMA, it is assumed that there exists a legacy THz system, where spatial beams have already been configured to serve legacy primary users. The aim of this paper is to study how these pre-configured spatial beams can be used as a type of bandwidth resources, on which additional secondary users are served without degrading the performance of the legacy primary users. A joint beam management and power allocation problem is first formulated as a mixed combinatorial non-convex optimization problem, and then solved by two methods with different performance-complexity tradeoffs, one based on the branch and bound method and the other based on successive convex approximation. Both analytical and simulation results are presented to illustrate the new features of beam-based resource allocation in THz-NOMA networks and also demonstrate that those pre-configured spatial beams can be employed to improve the system throughput and connectivity in a spectrally efficient manner.

This paper proposes a beamforming method under a per-antenna power constraint (PAPC). Although many beamformer designs with the PAPC need to solve complex optimization problems, the proposed complete power reallocation (CPR) method can generate beamformers with excellent performance only with linear operations. CPR is designed to have a simple structure, making it highly flexible and practical. In this paper, three CPR variations considering algorithm convergence speed, sum-rate maximization, and robustness to the channel uncertainty are developed. Simulation results verify that CPR and its variations satisfy their design criteria, and, hence, CPR can be readily utilized for various purposes.

This paper studies packet repetition strategies over erasure channels with memory and a long feedback delay. The problem is initially formulated as a communications problem where a source wishes to transmit one message packet to a destination while minimizing both the delay and the number of transmissions. At each time instant, the sender is provided a delayed acknowledgement feedback about past attempts, and must decide whether to attempt a new transmission or not. This problem is then re-formulated as an episodic reinforcement learning problem, where an agent attempts to learn the optimal transmission policy, provided delayed feedback about past transmission attempts. The agent is helped by a channel estimator, which attempts to capture the channel memory and use that to predict probabilities of erasures in a future window. This channel estimator is also data-driven and learns the channel model without any a priori channel knowledge. The paper presents a lower bound on the achievable trade-off between delay and number of transmissions for any channel modeled as a Markov process. Experimental results show that the combination of the proposed channel estimator and the agent can noticeably outperform naive strategies for channels with memory, and achieves results close to the lower bound.

Approximate Message Passing (AMP) is an efficient iterative parameter-estimation technique for certain high-dimensional linear systems with non-Gaussian distributions, such as sparse systems. In AMP, a so-called Onsager term is added to keep estimation errors approximately Gaussian. Orthogonal AMP (OAMP) does not require this Onsager term, relying instead on an orthogonalization procedure to keep the current errors uncorrelated with (i.e., orthogonal to) past errors. In this paper, we show the generality and significance of the orthogonality in ensuring that errors are “asymptotically independently and identically distributed Gaussian” (AIIDG). This AIIDG property, which is essential for the attractive performance of OAMP, holds for separable functions. We present a simple and versatile procedure to establish the orthogonality through Gram-Schmidt (GS) orthogonalization, which is applicable to any prototype. We show that different AMP-type algorithms, such as expectation propagation (EP), turbo, AMP and OAMP, can be unified under the orthogonal principle. The simplicity and generality of OAMP provide efficient solutions for estimation problems beyond the classical linear models. As an example, we study the optimization of OAMP via the GS model and GS orthogonalization. More related applications will be discussed in a companion paper where new algorithms are developed for problems with multiple constraints and multiple measurement variables.

The paper provides a new perspective on peak- and average-constrained Gaussian channels. Such channels model optical wireless communication (OWC) systems which employ intensity-modulation with direct detection (IM/DD). First, the paper proposes a new, capacity-preserving vector binary channel (VBC) model, consisting of dependent binary noisy bit-pipes. Then, to simplify coding over this VBC, the paper proposes coding schemes with varying levels of complexity, building on the capacity of binary-symmetric channels (BSC) and channels with state. The achievable rates are compared to capacity and capacity bounds, showing that coding for the BSC with state over the VBC achieves rates close to capacity at moderate to high signal-to-noise ratio (SNR), whereas simpler schemes achieve lower rates at lower complexity. The presented coding schemes are realizable using capacity-achieving codes for binary-input channels, such as polar codes. Numerical results are provided to validate the theoretical results and demonstrate the applicability of the proposed schemes.

This paper deals with scenario-aware, uncoordinated, and distributed signaling techniques in the context of feedforward opportunistic communications, that is, when the opportunistic transmitting node does not cooperate with any other node in a heterogeneous communication context. In this signaling technique, each network node individually follows a transmission strategy based on the locally sensed occupied and unused physical-layer network resources to minimize the induced interference onto other coexisting networks, taking into account the impact of the sensing errors and the locality of the sensing information. The paper identifies and characterizes critical invariance properties of the transmitted pulse shaping waveforms that guarantee the detectability of the feedforward transmitted signal by the uncoordinated receiving nodes, irrespective of the sensing signal space basis. The paper also shows that, under mild operating conditions, the proposed transmission scheme asymptotically defines efficient alternatives in the frequency domain, such as the circulant-shaping TDMA (CS-TDMA) modulation, and all of them admit a direct adaptation to frequency-selective channels. Numerical evaluation of the proposed schemes validates the provided theoretical models.

Fluid antennas offer a novel way to achieve massive connectivity by enabling each user to find a ‘port’ in space where the instantaneous interference undergoes a deep null for multiple access. While this unprecedented capability permits hundreds of users to share the same radio channel, each user needs to switch its best port on a symbol-by-symbol basis, which is impractical. Motivated by this, this paper considers the scenario in which the fluid antenna of each user updates its best port only if the fading channel changes. We refer to this approach as slow fluid antenna multiple access ( $s$ -FAMA). In this paper, we first investigate the interference immunity of $s$ -FAMA through analyzing the outage probability. Then an outage probability upper bound is obtained, from which we shed light on the achievable multiplexing gain of the system and unpack the impacts of various system parameters on the performance. Numerical results reveal that despite having a weaker multiplexing power than the symbol-based, fast FAMA (i.e., $f$ -FAMA), spatial multiplexing of 4 users or more is possible if the users’ fluid antennas have large numbers of ports.

This paper applies probabilistic amplitude shaping (PAS) to cyclic redundancy check (CRC)-aided tail-biting trellis-coded modulation (TCM). CRC-TCM-PAS produces practical codes for short block lengths on the additive white Gaussian noise (AWGN) channel. In the transmitter, equally likely message bits are encoded by a distribution matcher (DM) generating amplitude symbols with a desired distribution. A CRC is appended to the sequence of amplitude symbols, and this sequence is then encoded and modulated by TCM to produce real-valued channel input signals. This paper proves that the sign values produced by the TCM are asymptotically equally likely to be positive or negative. The CRC-TCM-PAS scheme can thus generate channel input symbols with a symmetric capacity-approaching probability mass function. The paper provides an analytical upper bound on the frame error rate of the CRC-TCM-PAS system over the AWGN channel. This FER upper bound is the objective function used for jointly optimizing the CRC and convolutional code. Additionally, this paper proposes a multi-composition DM, which is a collection of multiple constant-composition DMs. The optimized CRC-TCM-PAS systems achieve frame error rates below the random coding union (RCU) bound in AWGN and outperform the short-blocklength PAS systems with various other forward error correction codes studied in Coşkun et al. (2019).

Semantic communication has been regarded as a promising technology to serve upcoming intelligent applications. However, few studies have addressed the problem of resource allocation in semantic communication networks. Most resource allocation mechanisms act fairly to all original data, ignoring the meaning behind the transmitted bits. In this paper, a dynamic resource allocation scheme for the task-oriented semantic communication network (TOSCN) based on deep reinforcement learning (DRL) is proposed, which allows data with richer semantic information to preferentially occupy limited communication resources. This paper aims to design a deep deterministic policy gradient (DDPG) agent at the micro base station to maximize the long-term transmission efficiency of tasks. Firstly, the relationship between semantic information and task performance is investigated. Subsequently, a novel wireless resource allocation model for TOSCN is proposed by taking the image classification task as an example. Then, a joint optimization problem of the semantic compression ratio, transmit power, and bandwidth of each user is formulated. The agent is trained in an interactive learning environment to obtain a decent trade-off between the amount of data delivered to the receiver and the accuracy of intelligent tasks. Simulation results demonstrate that the proposed scheme achieves significant advantages in relieving communication pressure and improving task performance in resource-constrained wireless networks.

In this paper, simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) is investigated in the multi-user mobile edge computing (MEC) system to improve the computation rate. Compared with traditional RIS-aided MEC, STAR-RIS extends the service coverage from half-space to full-space and provides new flexibility for improving the computation rate for end users. However, the STAR-RIS-aided MEC system design is a challenging problem due to the non-smooth and non-convex binary amplitude coefficients with coupled phase shifters. To fill this gap, this paper formulates a computation rate maximization problem via the joint design of the STAR-RIS phase shifts, reflection and transmission amplitude coefficients, the receive beamforming vectors, and energy partition strategies for local computing and offloading. To tackle the discontinuity caused by binary variables, we propose an efficient smoothing-based method to decrease convergence error, in contrast to the conventional penalty-based method, which brings many undesired stationary points and local optima. Furthermore, a fast iterative algorithm is proposed to obtain a stationary point for the joint optimization problem, with each subproblem solved by a low-complexity algorithm, making the proposed design scalable to a massive number of users and STAR-RIS elements. Simulation results validate the strength of the proposed smoothing-based method and show that the proposed fast iterative algorithm achieves a higher computation rate than the conventional method while saving the computation time by at least an order of magnitude. Moreover, the resultant STAR-RIS-aided MEC system significantly improves the computation rate compared to other baseline schemes with conventional reflect-only/transmit-only RIS.

Accurate channel model and channel estimation are essential to empower extremely large-scale MIMO (XL-MIMO) in 6G networks with ultra-high spectral efficiency. With the sharp increase in the antenna array aperture of the XL-MIMO scenario, the electromagnetic propagation field will change from far-field to near-field. Unfortunately, due to the near-field effect, most of the existing XL-MIMO channel models fail to describe mixed line-of-sight (LoS) and non-line-of-sight (NLoS) path components simultaneously. In this paper, a mixed LoS/NLoS near-field XL-MIMO channel model is proposed to match the practical near-field XL-MIMO scenario, where the LoS path component is modeled by the geometric free space propagation assumption while NLoS path components are modeled by the near-field array response vectors. Then, to define the range of near-field for XL-MIMO, the MIMO Rayleigh distance (MIMO-RD) and MIMO advanced RD (MIMO-ARD) is derived. Next, a two stage channel estimation algorithm is proposed, where the LoS path component and NLoS path components are estimated separately. Moreover, the Cramér-Rao lower bound (CRLB) of the proposed algorithm is derived in this paper. Numerical simulation results demonstrate that, the proposed two stage scheme is able to outperform the existing methods in both the theoretical channel model and the QuaDRiGa channel emulation platform.

Most IoT systems use random access protocols for wireless communication. This paper considers an IoT node that generates periodic traffic to be delivered to a destination over a random access network; each packet of the node is expected to be delivered before its deadline. We say that the node is in a stressed period if, within a time interval, its successive packets miss their deadlines. In many systems, the worst-case performance is significantly affected by stressed periods. Characterizing the stochastic properties of stressed periods is thus of fundamental importance. In this paper, we use a fluid flow model to approximate the evolution of the buffer occupancy (i.e., backlog) at the transmitting node. We derive a relationship between buffer occupancy and delay and formally define a stressed period via a time interval in which the buffer occupancy exceeds a certain threshold. With this model, we analyze the dynamics of the buffer occupancy evolution and obtain the probability distributions of stressed period duration and delay. Real network experiments show that our model can well approximate the distributions of stressed period duration and delay in practical WiFi networks. The theoretical results of this paper can be used to analyze the robustness and worst-case performance of IoT monitoring and control systems built on random access networks.

This paper considers the problem of broadcasting with side information (BWSI), where a central server broadcasts encoded transmissions using $M$ -ary modulation to a set of caching receivers over an additive white Gaussian noise channel. For this problem of noisy index coding with $M$ -ary modulated transmission, the ML decoder at any receiver does not involve demodulation to the complex signal point and then index code decoding to the requested message bit. Instead, it decodes directly to the requested message bit, raising the question of whether the central server’s encoding scheme is required to correspond to an index code or if any set of transmissions encoding across the entire library of messages is sufficient. This paper proves that index codes are necessary for solving noisy BWSI problems even with $M$ -ary modulated transmission. Further, we look at index-coded QAM for prioritized receivers. An algorithm for mapping index-coded vectors to signal points on a square QAM constellation is presented, which gives mappings that achieve the best ML decoding performance for the prioritized receivers. Expressions for the ML metric seen by the highest priority receiver while using $M$ -PSK and $M$ -QAM for transmission are derived. For the highest priority receiver, $M$ -PSK is shown to outperform $M$ -QAM.

The ever-growing demands on wireless connectivity, especially with the emergence of various data-intensive low-latency applications, require novel multiplexing solutions capable of reliably supporting high rates at low latency. Non-orthogonal time division duplex (TDD) coupled with multiuser detection can meet these emerging needs, provided that accurate channel state information is available. This paper proposes a new pilot-free TDD frame structure that allows designing highly effective multiuser decoders and precoders for uplink and downlink multicell systems, in an unsupervised manner. The key idea is that each user repeats and permutes its uplink data using a pre-assigned permutation code. Invoking canonical correlation analysis (CCA) at the serving BS on the two deinterleaved uplink blocks yields high quality CCA-based beamformers capable of both recovering the uplink and precoding the downlink user signals in a way that effectively mitigates interference. The paper includes a pilotless synchronization framework that leverages CCA to recover the timing and frequency offsets in an asynchronous multiuser MIMO setup, without using pilots. Simulations are used to study the performance of the proposed approach on a large-scale network with multiple users and cells, while laboratory experiments with a small-scale network of software radios are used to demonstrate that the approach works well in practice under common hardware imperfections.

In an adaptive or non-cooperative communication system, the blind identification of channel coding is an indispensable procedure for recovering the message from the intercepted coding data. Due to the wide applications of convolutional codes, the blind identification problem of convolutional codes has also received extensive studies. In this paper, we consider the blind identification problem of convolution encoder parameters in the general $k/n$ rate case. To improve the blind identification accuracy of existing hard decision based solutions, a novel soft information based blind identification algorithm is designed in this paper. Specifically, a Soft Gaussian-Jordan Elimination Through Pivoting (Soft-GJETP) algorithm is firstly proposed to calculate the rank of the received data matrix. In contrast to the existing hard decision based GJETP algorithm, this Soft-GJETP algorithm formulates the soft information of the received data, then defines three reliability metrics and derives updating rules for this soft information. In this way, the diagonals of the received data matrix have fewer errors in Soft-GJETP algorithm. Furthermore, a soft decision strategy is proposed with a weight-dependent threshold. Employing this strategy, the dependent columns can be distinguished with higher accuracy. Finally, simulation results and comparisons are given to illustrate the performances of our proposed methods.

In this paper, we investigate the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. Besides, artificial noise (AN) is exploited by the BS for joint sensing and physical layer security provisioning. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers’ small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution. Simulation results illustrate that the proposed scheme can significantly enhance the physical layer security of ISAC systems compared to three baseline schemes. Moreover, compared to the conventional multi-stage approach for ISAC system design, the proposed approach based on variable-length snapshots not only facilitates a highly-directional offline sensing beam design but also allows us to flexibly prioritize communication or sensing depending on the application scenario.

Unmanned Aerial Vehicles (UAVs) hold great potential to support a wide range of applications due to the high maneuverability and flexibility. Compared with single UAV, UAV swarm carries out tasks efficiently in harsh environment, where the network resilience is of vital importance to UAV swarm. The network topology has a fundamental impact on the resilience of UAV network. It is discovered that scale-free network topology, as a topology that exists widely in nature, has the ability to enhance the network resilience. Besides, increasing network throughput can enhance the efficiency of information interaction, improving the network resilience. Facing these facts, this paper studies the throughput of UAV network with scale-free topology. Introducing the hybrid network structure combining both ad hoc transmission mode and cellular transmission mode into UAV network, the throughput of UAV network is improved compared with that of pure ad hoc UAV network. Furthermore, this work also investigates the optimal setting of the hop threshold for the selection of ad hoc or cellular transmission mode. It is discovered that the optimal hop threshold is related with the number of UAVs and the parameters of scale-free topology. This paper may motivate the application of hybrid network structure into UAV network.

Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user equipment) and feeding back to the transmitter. The overhead of CSI feedback occupies substantial uplink bandwidth resources, especially when the number of transmit antennas is large. Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. In this paper, a comprehensive overview of state-of-the-art research on this topic is provided, beginning with basic DL concepts widely used in CSI feedback and then categorizing and describing some existing DL-based feedback works. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy. Works on joint design of CSI feedback with other communication modules are also introduced, and some practical issues, including bitstream generation, multirate feedback, imperfect feedback, NN complexity, training dataset collection, online training, and standardization effect, are discussed. At the end of the paper, some challenges and potential research directions associated with DL-based CSI feedback in future wireless communication systems are identified.

There is an increasing demand of massive machine-type communication (mMTC) to provide scalable access for a large number of devices, which has prompted extensive investigation on grant-free massive random access (RA) in 5G and beyond wireless networks. Although many efficient signal processing algorithms have been developed, the limited radio resource for pilot transmission in grant-free massive RA systems makes accurate user activity detection and channel estimation challenging, which thereby compromises the communication reliability. In this paper, we adopt retransmission as a means to improve the quality of service (QoS) for grant-free massive RA. Specifically, by jointly leveraging the user activity correlation between adjacent transmission blocks and the historical channel estimation results, we first develop an activity-correlation-aware receiver for grant-free massive RA systems with retransmission based on the correlated approximate message passing (AMP) algorithm. Then, we analyze the performance of the proposed receiver, including the user activity detection, channel estimation, and data error, by resorting to the state evolution of the correlated AMP algorithm and the random matrix theory (RMT). Our analysis admits a tight closed-form approximation for frame error rate (FER) evaluation. Simulation results corroborate our theoretical analysis and demonstrate the effectiveness of the proposed receiver for grant-free massive RA with retransmission, compared with a conventional design that disregards the critical user activity correlation.

In this paper, we propose a robust secure communication scheme for a two-Unmanned Aerial Vehicle (UAV) scenario with the assistance of Intelligent Reflecting Surface (IRS), in which one legitimate user and one eavesdropper are considered. In the proposed scheme, the information is transmitted from one UAV named information UAV to the user via the assistance of the IRS, while the jamming information is transmitted by the other UAV named jamming UAV equipped with multi-antenna to interfere with the eavesdropper and safeguard the communication between information UAV and legitimate receiver. For the considered communication system, we assume that the Channel State Information (CSI) of the eavesdropping channels are imperfect. In this imperfect CSI case, we formulate a robust non-convex optimization problem. By jointly optimizing UAV transmit power, trajectory, and IRS phase shifters, the suboptimal average secrecy rate of the proposed scheme can be obtained. Specifically, Lagrangian dual transform and quaduratic transform are introduced to convert the objective function into more tractable form; S-Procedure is used for handling CSI uncertainty; and a two-stage penalty function algorithm is applied to obtain rank-one phase shifters solution. Numerical simulation results validate some interesting insights: (1) The IRS can bring strong robustness to the two-UAV security communication system; (2) Deploying jamming UAV in the IRS-UAV security communication system can greatly improve security performance, especially employing more antennas at the jamming UAV.

This paper investigates the covert communications via cooperative jamming and relay selection in a wireless relay system, where a source intends to transmit a message to its destination with the help of a selected relay, and a warden attempts to detect the existence of wireless transmissions from both the source and relay, while friendly jammers send jamming signals to prevent warden from detecting the transmission process. To this end, we first propose two relay selection schemes, namely random relay selection (RRS) and max-min relay selection (MMRS), as well as their corresponding cooperative jamming (CJ) schemes for ensuring covertness in the system. We then provide theoretical modeling for the covert rate performance under each relay selection scheme and its CJ scheme and further explore the optimal transmit power controls of both the source and relay for covert rate maximization. Finally, extensive simulation/numerical results are presented to validate our theoretical models and also to illustrate the covert rate performance of the relay system under cooperative jamming and relay selection.

In federated learning (FL), the communication constraint between the remote clients and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be compressed so as to minimize the loss in accuracy resulting from the communication constraint. This paper proposes “M-magnitude weighted L2 distortion + 2 degrees of freedom” (M22) algorithm, a rate-distortion inspired approach to gradient compression for federated training of deep neural networks (DNNs). In particular, we propose a family of distortion measures between the original gradient and the reconstruction we referred to as “ $M$ -magnitude weighted $L_{2}$ ” distortion, and we assume that gradient updates follow an i.i.d. distribution – generalized normal or Weibull, which have two degrees of freedom. In both the distortion measure and the gradient distribution, there is one free parameter for each that can be fitted as a function of the iteration number. Given a choice of gradient distribution and distortion measure, we design the quantizer to minimize the expected distortion in gradient reconstruction. To measure the gradient compression performance under a communication constraint, we define the per-bit accuracy as the optimal improvement in accuracy that one bit of communication brings to the centralized model over the training period. Using this performance measure, we systematically benchmark the choice of gradient distribution and distortion measure. We provide substantial insights on the role of these choices and argue that significant performance improvements can be attained using such a rate-distortion inspired compressor.

A deep autoencoder (DAE)-based structure for end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimizes the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes. This brings further gain compared to standard uniform constellations such as quadrature amplitude modulation. The proposed structure is then extended to work with imperfect channel state information (CSI). The CSI imperfection due to both the estimation and quantization errors are examined. The performance of the DAE-ZIC is compared with two baseline methods, i.e., standard and rotated constellations. The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI. Simulation results show that the improvement is achieved in all interference regimes (weak, moderate, and strong) and consistently increases with the signal-to-noise ratio (SNR). For instance, more than an order of magnitude BER reduction is obtained with respect to the most competitive conventional method at weak interference when $\rm SNR > 15 dB$ and two bits per symbol are transmitted. The improvements reach about two orders of magnitude when quantization error exists, indicating that the DAE-ZIC is more robust to the interference compared to the conventional methods.

We consider a service cost minimization problem for resource-constrained small-cell networks with caching, where the challenge mainly stems from (i) the insufficient backhaul capacity and limited network bandwidth and (ii) the limited storing capacity of small-cell base stations (SBSs). Besides, the optimization problem is NP-hard since both the users’ mobility patterns and content preferences are unknown. In this paper, we develop a novel mobility-aware joint routing and caching strategy to address the challenges. The designed framework divides the entire geographical area into small sections containing one SBS and several mobile users (MUs). Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs and make a caching decision. The FRPL method completes multiple tasks in one shot, thus reducing the average processing time per global aggregation of learning. By exploiting the outcomes of FRPL together with the estimated service edge of SBSs, the proposed cache placement solution greedily approximates the minimizer of the challenging service cost optimization problem. Theoretical and numerical analyses show the effectiveness of our proposed approaches.

For mission-critical URLLC applications, timely status updates are essential. This paper investigates the age of information (AoI) of the three HARQ schemes specified in 5G R16, targeting to provide guidelines for future grant-free access design in 5G-Advanced and beyond. Specifically, we analyze two packet management policies: First-come-first-serve (FCFS) and preemption policy where new packets always preempt the buffer. We also study the AoI in a latency-sensitive scenario where expired packets are discarded. We derive exact expressions of AoI and peak AoI for all schemes and their lower bounds, revealing that the number of the maximum consecutive transmissions is critical for information freshness. Simulation results validate the theoretical analysis and show that Proactive HARQ scheme outperforms K-repetition HARQ scheme unconditionally and Reactive HARQ scheme with moderate system load or above. And discarding expired packets enhances system robustness for overload systems but yields larger AoI.

In the ongoing 5G and upcoming 6G eras, the intelligent Internet of Things (IoT) network will take increasingly important responsibility for industrial production, daily life and so on. The IoT devices with limited battery size and computing ability cannot meet many applications brought out by the data-driven artificial intelligence technique. The combination of wireless power transfer (WPT) and edge computing is regarded as an effective solution to this dilemma. IoT devices can collect radio frequency energy provided by hybrid access points (HAPs) to process data locally or offload data to the edge servers of HAPs. However, how to efficiently make offloading decisions and allocate resource is challenging, especially for the networks with multiple HAPs. In this paper, we consider the sum computation rate maximization problem for a WPT empowered IoT network with multiple HAPs and IoT devices. The problem is formulated as a mixed-integer nonlinear programming problem. To solve this problem efficiently, we decompose it into a top-problem of optimizing offloading decisions and a sub-problem of optimizing time allocation under the given offloading decisions. We propose a deep reinforcement learning (DRL) based algorithm to output the near-optimal offloading decision and design an efficient algorithm based on Lagrangian duality method to obtain the consequent optimal time allocation. Simulations verified that the proposed DRL-based algorithm can achieve more than 95 percent of the maximal computation rate with low complexity. Compared with the common actor-critic algorithm, the proposed algorithm has the substantial advantage in convergence speed, achieved computation rate and running time.

Using synthetic DNA for data storage and for physical information encoding in labeling, tracing, and authentication applications is becoming more feasible as synthesis and reading technologies are improving. DNA in data storage applications has several advantages such as very high physical density and robustness. Some of the new synthesis technologies lead to repetition noise, consisting of sticky insertions and deletions in the resulting messages. In this paper, we address reconstruction algorithms for multiple trace communication channels with repetition (sticky insertion and deletion) noise. We prove correctness and analyze failure rates, both analytically and on simulated data. We identify a failure mechanism related to alternating stretches in the design sequence that leads to a potential bias in the data derived from reads (traces) and used for reconstruction. To minimize this effect we introduce alternating length limited codes (ALL codes) and analyze some of their properties.

The line-of-sight (LOS) requirement of free-space optical (FSO) systems can be relaxed by employing optical relays or optical intelligent reflecting surfaces (IRSs). In this paper, we show that the power reflected from FSO IRSs and collected at the receiver (Rx) lens may scale quadratically or linearly with the IRS size or may saturate at a constant value. We analyze the power scaling law for optical IRSs and unveil its dependence on the wavelength, transmitter (Tx)-to-IRS and IRS-to-Rx distances, beam waist, and Rx lens size. We also consider the impact of linear, quadratic, and focusing phase shift profiles across the IRS on the power collected at the Rx lens for different IRS sizes. Our results reveal that surprisingly the powers received for the different phase shift profiles are identical, unless the IRS operates in the saturation regime. Moreover, IRSs employing the focusing (linear) phase shift profile require the largest (smallest) size to reach the saturation regime. We also compare optical IRSs in different power scaling regimes with optical relays in terms of the outage probability, diversity and coding gains, and optimal placement. Our results show that, at the expense of a higher hardware complexity, relay-assisted FSO links yield a better outage performance at high signal-to-noise-ratios (SNRs), but optical IRSs can achieve a higher performance at low SNRs. Moreover, while it is optimal to place relays equidistant from Tx and Rx, the optimal location of optical IRSs depends on the phase shift profile and the power scaling regime they operate in.

Over-the-air computation (AirComp) is a well-known technique by which several wireless devices transmit by analog amplitude modulation to achieve a sum of their transmit signals at a common receiver. The underlying physical principle is the superposition property of the radio waves. Since such superposition is analog and in amplitude, it is natural that AirComp uses analog amplitude modulations. Unfortunately, this is impractical because most wireless devices today use digital modulations. It would be highly desirable to use digital communications because of their numerous benefits, such as error correction, synchronization, acquisition of channel state information, and widespread use. However, when we use digital modulations for AirComp, a general belief is that the superposition property of the radio waves returns a meaningless overlapping of the digital signals. In this paper, we break through such beliefs and propose an entirely new digital channel computing method named ChannelComp, which can use digital as well as analog modulations. We propose a feasibility optimization problem that ascertains the optimal modulation for computing arbitrary functions over-the-air. Additionally, we propose pre-coders to adapt existing digital modulation schemes for computing the function over the multiple access channel. The simulation results verify the superior performance of ChannelComp compared to AirComp, particularly for the product functions, with more than 10 dB improvement of the computation error.

The design and engineering of molecular communication (MC) components capable of processing chemical concentration signals is the key to unleashing the potential of MC for interdisciplinary applications. By controlling the signaling pathway and molecule exchange between cell devices, synthetic biology provides the MC community with tools and techniques to achieve various signal processing functions. In this paper, we propose a design framework to realize any order concentration shift keying (CSK) systems based on simple and reusable single-input single-output cells. The design framework also exploits the distributed computation on multicellular consortia with spatial segregation, which has advantages in system scalability, low genetic manipulation, and signal orthogonality. We also create a small library of simple logic engineered cells and apply them to implement binary CSK (BCSK) and quadruple CSK (QCSK) systems to demonstrate the feasibility of our proposed design framework. The simplicity of our engineered cells allows for their reuse in other systems beyond CSK. Importantly, we establish a mathematical framework to theoretically characterize our proposed distributed multicellular systems. Specially, we divide a system into fundamental building blocks, from which we derive the impulse response of each block and the cascade of the impulse responses leads to the end-to-end response of the system. Simulation results obtained from the agent-based simulator BSim not only validate our CSK design framework but also demonstrate the accuracy of the proposed mathematical analysis.

Reconfigurable intelligent surface (RIS) has recently gained significant interest as an emerging technology for future wireless networks thanks to its potential for improving the coverage in challenging propagation environments. This paper studies an RIS-assisted communication system, where a source transmits data to a destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, we derive closed-form expressions for the coverage probability and ergodic rate, which explicitly unveil the impact of the propagation environment and the RIS on the system performance. Besides the optimization of the RIS phase profile, we formulate an RIS placement optimization problem with the aim of maximizing the coverage probability by relying only on partial channel state information. An efficient algorithm is proposed based on the gradient ascent method. Simulation results are illustrated in order to corroborate the analytical framework and findings. The proposed RIS phase profile is shown to outperform several heuristic benchmark schemes in terms of outage probability and ergodic rate. In addition, the proposed RIS placement strategy provides an extra degree of freedom that remarkably improves the system performance.

Due to long-term reliability, wireless control systems (WCSs) have attracted significant interest recently. However, mission-critical control requires stringent ultra-reliability and low-latency communication (URLLC) with massive data delivery, which are major challenges for conventional wireless networks. This paper investigates downlink URLLC in WCS, where the semantic communication is adopted at the control center to extract task-oriented semantic information from original large-sized data. To efficiency, the control center utilizes the rate splitting policy to deliver semantic information through private messages, while the semantic knowledge is transmitted through one common message. We aim to maximize the weighted sum semantic information transmission rate by jointly optimizing the semantic information extraction, delivery duration, rate splitting, and transmit beamforming, subject to several practical constraints, including recovery accuracy, quality of service requirements, communication latency and computation delay. By the problem decomposition, two sub-problems are obtained, where the closed-form solution for the semantic information extraction is derived at each step. Due to the complexity of the multivariable coupling in the channel dispersion, we propose fractional transformation methods for rate splitting design. Numerical results confirm that the RSMA and semantic communication design can complement each other for multiplexing gains enhancement and latency reduction to achieve overloaded connections.