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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.

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.

Millimeter-Wave Massive MIMO is important for beyond 5G or 6G wireless communication networks. The goal of this paper is to establish successful communication between the cellular base stations and devices, focusing on the problem of joint user activity detection and channel estimation. Different from traditional compressed sensing (CS) methods that only use the sparsity of user activities, we develop several Approximate Message Passing (AMP) based CS algorithms by exploiting the sparsity of user activities and mmWave channels. First, a group soft-thresholding AMP is presented to utilize only the user activity sparsity. Second, a hard-thresholding AMP is proposed based on the on-grid CS approach. Third, a super-resolution AMP algorithm is proposed based on atomic norm, in which a greedy method is proposed as a super-resolution denoiser. And we smooth the denoiser based on Monte Carlo sampling to have Lipschitz continuity and present state evolution results. Extensive simulation results show that the proposed method outperforms the previous state-of-the-art methods.

In this paper, for a general full-duplex wireless MapReduce distributed computing network, we investigate the minimization of the communication overhead for a given computation overhead. The wireless MapReduce framework consists of three phases: Map phase, Shuffle phase and Reduce phase. Specifically, we model the Shuffle phase into a cooperative X network based on a more general file assignment strategy. Furthermore, for this cooperative X network, we derive an information-theoretic upper bound on the sum degree of freedom (SDoF). Moreover, we propose a joint interference alignment and neutralization (IAN) scheme to characterize the achievable SDoF. Especially, in some cases, the achievable SDoF coincides with the upper bound on the SDoF, hence, the IAN scheme provides the optimal SDoF. Finally, based on the SDoF, we present an information-theoretic lower bound on the normalized delivery time (NDT) and achievable NDT of the wireless distributed computing network, which are less than or equal to those of the existing networks. The lower bound on the NDT shows that 1) there is a tradeoff between the computation load and the NDT; 2) the achievable NDT is optimal in some cases, hence, the proposed IAN scheme can reduce the communication overhead effectively.

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

This paper investigates a location-based downlink transmission scheme to provide diverse services for different users in an integrated satellite-terrestrial network (ISTN). Specifically, the satellite network employs multicast communication to disseminate information to multiple satellite users, while the terrestrial network incorporates non-orthogonal multiple access (NOMA) with intelligent reflecting surface (IRS) technology to serve terrestrial users. Given that the location information-based channel state information (LoI-CSI) of each user is available, we formulate an optimization problem to minimize the outage probability (OP) of the terrestrial network by optimizing the transmit power and beamforming (BF) weight vector at the base station, the IRS phase shift vector, and the power allocation factor, while meeting the quality-of-service (QoS) requirement of the satellite network. To make the optimization problem tractable, we first propose a low-complexity BF algorithm based on the LoI-CSI, which simplifies the optimization problem while guaranteeing the QoS requirement of the satellite network. Then, assuming that terrestrial links experience Rician fading, we derive an approximate yet accurate OP of the terrestrial network, which is explored to calculate the phase shift vector. Furthermore, we propose a novel power allocation method that employs an exponential-type approximation of the first-order Marcum Q-function, to obtain the power allocation coefficient. Finally, simulation results confirm the theoretical formulas’ validity and reveal the proposed algorithms’ superiority in system performance.

Mainstream wireless communication networks mostly adopt the layered protocol encapsulation mechanism, which inevitably introduces natural redundancy into protocols. To fully exploit the abundant natural redundancy in protocols (NRP), this paper provides a novel cross-frame and cross-layer perspective for channel coding and decoding (CCD). First, a generalized CCD architecture is developed for the first time to describe the NRP and artificial redundancy in channel codes (ARC) uniformly. The concept of equivalent code rate is proposed correspondingly to evaluate the potential gain of NRP. Second, we summarize the types of NRP, and for each of them, factor graphs and sum-product algorithms (SPAs) are adopted to model and solve the correlation between it and ARC. On this basis, the joint decoding of multi-class NRP and ARC is further considered, and a general SPA-based joint protocol-channel decoding (JPCD) scheme is presented. Finally, tests on the 802.11n TCP/IP/MAC protocol stack demonstrate that the JPCD scheme can significantly improve the decoding performance of both the packet header and payload with a relatively small computational cost. Our work shows great promise in reducing error retransmission, bandwidth overhead, and the difficulty of source recovery.

To achieve multi-Gb/s data rates in 6G optical wireless access networks based on narrow infrared (IR) laser beams, a high-speed receiver with two key specifications is needed: a sufficiently large aperture to collect the required optical power and a wide field-of-view (FOV) to avoid strict alignment issues. This paper puts forward the systematic design and optimisation of multi-tier non-imaging angle diversity receivers (ADRs) composed of compound parabolic concentrators (CPCs) coupled with photodiode (PD) arrays for laser-based optical wireless communication (OWC) links. Design tradeoffs include the gain-FOV tradeoff for each receiver element and the area-bandwidth tradeoff for each PD array. The rate maximisation is formulated as a non-convex optimisation problem under the constraints on the minimum required FOV and the overall ADR dimensions to find the optimum configuration of the receiver bandwidth and FOV, and a low-complexity optimal solution is proposed. The ADR performance is studied using computer simulations and insightful design guidelines are provided through various numerical examples. An efficient technique is also proposed to reduce the ADR dimensions based on CPC length truncation. It is shown that a compact ADR with a height of $\leq 0.5$ cm and an effective area of $\leq 0.5$ cm2 reaches a data rate of 12 Gb/s with a half-angle FOV of 30° over a 3 m link distance.

In this paper, in order to improve the throughput of wireless communications, we investigate the variability in both the number of active transmit antennas (TAs) and the combination degree of multiple constellations in a multiple-input multiple-output (MIMO) system. By jointing the use of an arbitrary number of active TAs and the construction of one or two signal constellations, a new design, called as joint multiple constellations and variable active antennas selection for signal spaces design (SSD-MCVA), is proposed to exploit the spatial domain and then extend the signal spaces. Specifically, according to the dimensions of the spatial symbols that are constructed by three types of constellations (i.e., conventional QAM, the first secondary QAM (SQAM) and the second SQAM ( $\text{S}'$ QAM)) and the aid of the sign $j$ , four antenna index (AI) vector subsets are designed to extend the number of signal space vectors for enhancing the SE as well as the reliability of transmission. In addition, permutating signal spatial vectors with MCVA (PSSV-MCVA) is proposed to exploit the permutation index domain and then greatly improve the throughput for the app- lication of the large-scale MIMO systems. Furthermore, the spatial index bits, the detection complexity comparisons and the average bit error probability are provided. Simulation results show that the proposed systems achieve more spatial index bits and a lower bit error rate than existing index modulation systems.

We consider the problem of cache-aided multi-user private information retrieval (MuPIR). This problem consists of $N$ independent files that are replicated across $S \geq 2$ non-colluding servers. There are $K$ cache-equipped users having caches of size equivalent to $M$ files. Every user aims to retrieve a file from the servers, but servers should not obtain any information about users’ demands. The user caches are filled with some arbitrary function of the files before the users decide their demands, known as the placement phase. After deciding their demands, users cooperatively send queries to the servers to retrieve their desired files privately. Upon receiving the queries, servers broadcast coded transmissions based on these queries and the files, known as the delivery phase. Conveying queries to the servers incurs an upload cost for the users, and downloading the answers broadcasted by the servers incurs a download cost. Each file has to be split into $F$ packets to implement cache-aided MuPIR schemes. In this paper, we propose MuPIR schemes that utilize placement delivery arrays (PDAs) to characterize placement and delivery. Proposed MuPIR schemes significantly reduce subpacketization levels while slightly increasing the download cost. The proposed scheme also substantially reduces the upload cost for the users. We show that our scheme is order optimal in terms of download cost for PDAs based on Ali-Niesen scheme for centralised coded caching. We recover the optimal single-user PIR scheme presented by Tian et al. in [“Capacity-Achieving Private Information Retrieval Codes With Optimal Message Size and Upload Cost,” IEEE Trans. Inform. Theory, 2019] as a special case. Our scheme also achieves optimal rate for single-user cache-aided PIR setup reported in [R. Tandon, “The capacity of cache aided private information retrieval,” Annual Allerton Conference on Communication, Control and Computing, 2017.]

Extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems introduce a new communication paradigm called near-field communications, which identifies users’ location within the near-field (Fresnel’s region). In the near-field, beams can be steered in the angle and distance dimensions, resulting in an enormous codebook and a prolonged two-dimensional beam alignment (BA) process. To keep a low BA overhead while achieving low BA error, in this paper, we design a novel hierarchical codebook and a BA scheme for near-field XL-MIMO systems. Specifically, we first propose a novel spatial partition where the angle-offset effect is revealed and leveraged to improve the beam gain inside the coverage area. Based on the partition, we design distance-coarse and focusing beams. Distance-coarse beams are leveraged to construct the high level of the codebook for angle dimension alignment. In contrast, focusing beams construct the last level codebook for distance dimension alignment. Corresponding to the proposed codebook structure, our BA scheme is a tree search consisting of two stages: the angle aligning stage and the distance aligning stage. Next, we formulate the desired codebook design problem as difference convex optimization problems, where three beam design guidelines are considered to minimize the BA error rate raised by the near-field angle-offset effect. After that, the proposed optimization problem is solved by the constrained concave-convex procedure. Numerical simulations verify the angle offset effect and our designed near-field beam. Furthermore, we show that our BA scheme only utilizes one percent of overhead but achieves a lower BA error rate than exhaustive searching.

In this paper, we optimize a Wireless Information and Power Transfer (WIPT) system including multiple pairs of users, where transmitters employ a single-antenna to transmit their information and power to their receivers with the help of one multiple-antenna Amplify-and-Forward (AF) relay or an active Intelligent Reflecting Surface (IRS). We propose a joint Time Switching (TS) scheme in which transmitters, receivers, and the relay/IRS are either in their energy or information transmission/reception modes. The transmitted multi-carrier unmodulated and modulated waveforms are used for Energy Harvesting (EH) and Information Decoding (ID) modes, respectively. In order to design an optimal fair system, we maximize the minimum rate of all pairs for both relay and IRS systems through a unified framework. This framework allows us to simultaneously design energy waveforms, find optimal relay/IRS amplification/reflection matrices, allocate powers for information waveforms, and allocate time durations for various phases. This problem turns out to be non-convex. Thus, we propose an iterative algorithm by using the Minorization-Maximization (MM) technique, which efficiently converges to the optimized solution, i.e., stationary point of the problem (under mild conditions). Numerical examples show that the proposed method improves the performance of the multi-pair WIPT relay/IRS system under various setups.

Efficient implementation of Tomlinson-Harashima precoding (THP) is crucial in massive multiple-input-multiple-output (MIMO) systems with a large number of antennas at the base station (BS) serving many user equipments (UEs). To address the high computational complexity of THP, in this paper, we first propose novel THP update algorithms that can avoid recomputing the THP filters when a new UE arrives or departs. Specifically, by using the Gram-Schmidt process and a series of Givens matrices, the THP filters are computed without full matrix operations. Then we extend the THP update algorithms to a more general scenario when multiple multi-antenna UEs arrive or depart. In this case, the proposed algorithms use both direct and iterative approaches. Moreover, the computational complexity of the proposed algorithms is derived and compared with that of the conventional THP. Finally, to further align with the practical scenario, we analyze and derive the approximate close-form expressions for the sum achievable rate of the proposed algorithms under imperfect channel state information (CSI). Simulation results are provided to illustrate the effectiveness of the proposed algorithms. The impact of quasi-static fading and slow time-varying scenarios with imperfect CSI on the communication performance of the proposed algorithms is also evaluated.

An integrated sensing and communication (ISAC) system assisted by distributed passive intelligent reflecting surfaces (IRSs) is proposed in this paper, which enables simultaneous signal demodulation and location sensing, without channel state information (CSI). A detailed workflow of the proposed IRS-based ISAC system is designed, including transmission protocol, joint location sensing and demodulation, as well as beamforming optimization. Specifically, each coherent block consists of two stages, and each stage is divided into two time blocks, where the signal demodulation, channel estimation, and location sensing are conducted by the proposed integrated localization and demodulation (I-LAD) algorithm, simultaneously. Simulation results show that the signal demodulation performance of the proposed I-LAD algorithm is close to that of the benchmark scheme assuming perfect CSI. At the same time, the channel estimation performance is comparable to that of the parallel factor decomposition (PAPRFAC) method or the compressive sensing-based channel estimation (CS-EST) method. In addition, the proposed location sensing scheme almost achieves the Cramér-Rao lower bound (CRLB) for distributed IRS-assisted localization-only systems, confirming the effectiveness of the proposed I-LAD algorithm.

In this paper, we propose a novel artificial intelligence (AI) based adaptive polar coding scheme that adapts to various channel conditions and quality of service requirements. To ensure tight adaptation, we develop a new AI-based performance prediction framework for the precoded polar codes under the successive cancellation list (SCL) decoder. This AI-based framework relies on a neural network and recent advancements in the analysis of precoded polar codes, SCL and SC decoders. Then we apply the proposed framework to optimise precoded polar codes for various target frame error rates (FER), signal-to-noise ratios (SNR) and decoding list sizes $L$ , where the code length is fixed to a power of two, but the code rate may vary. We predict the throughput and maximise it over the code rates with bit-level granularity. The proposed approach paves the way towards online adaptive polar coding with high error-correction capability. The constructed codes can be compactly specified using the reliability sequence from the 5G New Radio standard and a single parameter whose value is specific to each code. The simulation results show that the proposed codes outperform 5G polar codes with CRC11 under SCL decoding with various $L$ .

Extremely large-scale multiple-input multiple-output (XL-MIMO) communications and ultra-high frequency bands are both potential enablers for satisfying extreme performance requirements of future wireless systems. Thanks to low hardware cost and power consumption, holographic metasurface antennas (HMAs) operating at high frequencies have recently emerged as an effective realization of large-scale antenna arrays, leading to greatly enlarged near-field region. In this paper, we investigate a power-efficient HMA-based near-field downlink multiuser system, where three different HMA-based arrays are considered. Specifically, we aim to minimize the total transmit power for each HMA-based array while maintaining the signal to interference plus noise ratio (SINR) constraint of each user by jointly optimizing the digital transmit precoder and the analog HMA weighting matrix. In the special single-user scenario, we validate that the original optimization problem can be decomposed into several independent subproblems each corresponding to a single HMA microstrip, whose optimal solution can be obtained by the successive convex approximation (SCA) based method. It is also revealed that the HMA-based array is capable of achieving near-field beam focusing. In the general multiuser scenario, we develop an efficient SCA-alternating direction method of multipliers (ADMM) based alternating optimization (AO) algorithm to tackle the intractable optimization problem, where the digital precoders and the HMA weighting matrix are iteratively optimized in an alternating manner. Numerical results demonstrate the superior performance of our proposed algorithms over existing benchmark schemes. It is also shown that the HMA-based array attains lower hardware overhead and power consumption as compared to the conventional hybrid array.

Multipartite entanglement plays a crucial role for the design of the Quantum Internet, due to its peculiarities with no classical counterpart. Yet, for entanglement-based quantum networks, a key open issue is constituted by the lack of an effective entanglement access control (EAC) strategy for properly handling and coordinating the quantum nodes in accessing the entangled resource. In this paper, we design a quantum-genuine entanglement access control (EAC) to solve the contention problem arising in accessing a multipartite entangled resource. The proposed quantum-genuine EAC is able to: i) fairly select a subset of nodes granted with the access to the contended resource; ii) preserve the privacy and anonymity of the identities of the selected nodes; iii) avoid to delegate the signaling arising with entanglement access control to the classical network. We also conduct a theoretical analysis of noise effects on the proposed EAC. This theoretical analysis is able to catch the complex noise effects on the EAC through meaningful parameters.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted mobile edge computing (MEC) network aiming to maximize the energy efficiency in the finite blocklength (FBL) regime under both coding length and maximum decoding error rate constraints. We first analyze the single user equipment (UE) case and propose a three-step alternating optimization algorithm to solve the problem. Extending the system model, we subsequently investigate a network with multiple UEs, in which non-orthogonal multiple access (NOMA) transmission is adopted. In this more general setting, we also conduct a convergence analysis. Furthermore, we introduce a UE-grouping scheme for hybrid NOMA-TDMA transmission and develop a dynamic CPU frequency allocation algorithm at the mobile edge computing (MEC) server. Numerical results show that the proposed algorithms solve the problem efficiently. Via numerical results, we also identify the impact of various parameters (e.g., coding blocklength, the number of RIS elements, computational resources, number of UEs) on the energy efficiency. Furthermore, with the numerical results, we verify the validity of UE grouping method and demonstrate that the proposed dynamic CPU frequency allocation can enhance the performance substantially.

This paper investigates the capacity region of the optical intensity broadcast channels (OI-BCs), where the input is subject to a peak-intensity constraint, an average-intensity constraint, or both. By leveraging the decomposition results of several random variables, i.e., uniform, exponential, and truncated exponential random variables, and adopting a superposition coding (SC) scheme, the inner bound on the capacity region is derived. Then, the outer bound is derived by applying the conditional entropy power inequality (EPI). In the high signal-to-noise ratio (SNR) regime, the inner bound asymptotically matches the outer bound, thus characterizing the high-SNR asymptotic capacity region. The bounds are also extended to the general $K$ -user BCs without loss of high-SNR asymptotic optimality.

Precise parameter estimation is essential for developing effective channel models. However, jointly estimating the various channel parameters is a challenging problem as these parameters can only be extracted from comprehensive data collected during massive measurement activities. Traditional algorithms suffer from limited estimation accuracy due to high mobility and signal correlation. In this paper, we propose a low-complexity full-dimension parameter extraction algorithm. The method extends the forward-backward spatial smoothing technique to estimate two-dimensional (2-D) angle information in the uniform planar array (UPA), enhancing aperture size and providing higher resolution for coherent signals. Then, we provide a Doppler compensation technique to prevent the deterioration in complex amplitude estimation quality caused by large Doppler frequency in high-speed mobile environments. Moreover, the proposed algorithm is also applied to the fifth-generation new radio (5G-NR) system. To remedy power leakage in the channel impulse response (CIR), we propose an eigenvalue-assisted sampling point selection scheme. This scheme preserves sampling positions abundant in channel information to reduce computational complexity and eliminate most noise. Simulation and field tests show that the proposed algorithm achieves better performance with lower computational complexity compared to the existing algorithm.

To satisfy the heterogeneous service requirements in future internet of things (IoT), this paper investigates the novel framework for intelligent reflecting surface (IRS)-aided wireless powered over-the-air computation (AirComp) and communication networks, where the IoT devices first harvest energy from the downlink signal sent by the base station, and then conduct the information transmissions and AirComp in the uplink. In particular, the IRS is used to improve the efficiency of wireless energy transfer, and alleviate the harmful interference between the communication and AirComp signals. To balance the performance of such an integrated system, we present two joint beamforming and reflection optimization problems via minimizing the computation distortion and maximizing the sum rate, respectively. To solve the non-convex problems, we develop the alternating optimization framework with proved convergence, in which the penalty function-based method and variable substitution technique are exploited to acquire the optimal solutions of beamformers and reflection parameters. Finally, simulation results show that the proposed method realizes significantly higher computation accuracy and communication rate, in comparison with several existing benchmark methods.

This paper develops an optimal design for device scheduling, alignment coefficient, and aggregation rounds within a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system considering a constrained sum power budget. In DP-OTA-FedAvg, gradients are aligned using an alignment coefficient and then aggregated over the air, utilizing channel noise to ensure participant privacy. This study highlights two critical tradeoffs in aligned over-the-air federated learning (OTA-FL) systems with limited power and privacy budgets. Firstly, it reveals the tradeoff between the number of scheduled devices and the alignment coefficient. Secondly, it investigates the balance between aggregation distortion and local training error while adhering to the sum power constraint. Specifically, we measure privacy using differential privacy (DP) and perform convergence analyses for both convex and non-convex loss functions. These analyses provide insights into how device scheduling, the alignment coefficient, and the number of global aggregations affect both privacy preservation and the learning process. Building on these analytical results, we formulate an optimization problem aimed at minimizing the optimality gap of DP-OTA-FedAvg under power and privacy constraints. By specifying the number of aggregation rounds, we derive a closed-form expression describing the relationship between the alignment coefficient and the number of scheduled devices. We then tackle the problem through iterative optimization of scheduling and aggregation rounds. The effectiveness of the proposed policies is verified through simulations, and the performance advantage is particularly pronounced in scenarios where devices have poor channel conditions and limited sum-power budgets.