Cooperative broadcast is an efficient approach to improve Wi-Fi broadcast performance in crowded scenarios with densely deployed access points (APs). However, existing concurrent transmission MAC protocols cannot perfectly synchronize APs for the receiving user, and the superimposed channels at users vary over time due to multi-path effects with different carrier frequency offsets (CFOs) from the APs. Traditional channel estimation methods, which treat the superimposed channels as a whole and use a portion of the superimposed channels to derive the rest, are unsuitable. To solve the problem, we propose a fine-grained channel estimation approach that first estimates channel taps and CFOs of each AP, and then reconstructs the superimposed channels. We first study a benchmark channel estimation algorithm that utilizes a widely adopted compressed sensing (CS) technique. However, through analysis and simulations, we show that the CS-based algorithm suffers from high correlation problems in the constructed sensing matrix and the non-sparse channel problem in practice, leading to an estimation error floor at high SNRs. To solve these problems, we present a two-stage channel estimation algorithm. It first estimates the CFOs by identifying the most likely CFO combination matching the received signals, and then estimates the time-domain channel taps. Simulation and experimental results show that the two-stage channel estimation algorithm achieves much lower bit error rate (BER) and packet error rate (PER) than the traditional IEEE 802.11 approach, and the two-stage algorithm outperforms the CS-based algorithm, especially at high SNRs. The network-layer simulation results further demonstrate that, empowered by the proposed two-stage channel estimation algorithm, the cooperative broadcast scheme improves throughput by at least 1.4× (up to 46.8×) compared with the unicast-based broadcast schemes, and by approximately 0.6× to 0.8× compared with the simple uncooperative broadcast scheme.

Due to circuit failures, defective elements that cannot adaptively adjust the phase shifts of their impinging signals in a desired manner may exist on an intelligent reflecting surface (IRS). Traditional way to locate these defective IRS elements requires a thorough diagnosis of all the circuits belonging to a huge number of IRS elements, which is practically challenging. In this paper, we will devise novel approaches under which a transmitter sends known pilot signals and a receiver localizes all the defective IRS elements just based on its over-the-air measurements reflected from the IRS. Specifically, given any set of IRS elements, we propose an efficient method to process the received signals to determine whether this cluster contains defective elements or not with a very high accuracy probability. Based on this method, we show that the over-the-air diagnosis problem belongs to the 20 questions problem, where we can adaptively change the query set at the IRS so as to localize all the defective elements as quickly as possible. Along this line, we first propose a sorted posterior matching (sortPM) based method according to the noisy 20 questions technique, which enables accurate diagnosis even if the answers about the existence of defective elements in some sets of interest are wrong at certain question and answer (Q&A) rounds due to the noisy received signals. Next, to reduce the complexity, we propose a bisection based method according to the noiseless 20 questions technique, which totally trusts the answer at each Q&A round and keeps removing half of the remaining region based on such answers. Via numerical results, we show that our proposed methods can exploit the over-the-air measurements to localize all the defective IRS elements quickly and accurately.

Uncoordinated random-access protocols are attractive for underwater acoustic (UWA) networks due to their simplicity and low overhead, especially for data collection applications in scuba diving. However, the performance is limited by severe collisions and the challenging UWA channel, including rich multipath and time-varying channel (caused by Doppler effects and user movements). Existing UWA physical-layer waveforms struggle to resolve collisions while maintaining high data rates. This paper presents ZCMod, a Zadoff-Chu (ZC) sequence-based modulation that assigns unique ZC sequences to users to mitigate interference and encodes multiple bits via cyclic shifts for high data rates. To address UWA-specific challenges, ZCMod introduces two key designs: 1) shape-based demodulation, which tracks channel response shifts to combat multipath effects; 2) auxiliary modulation, where each symbol is modulated with two ZC sequences—one for channel estimation and the other for data transmission—to handle fast time-varying channels. Experiments and simulations demonstrate that a) ZCMod achieves more robust BER performance and eliminates error floors compared to state-of-the-art (SOTA) methods in slight time-varying channels; and b) ZCMod maintains stable throughput in fast time-varying channels, while SOTA approaches suffer significant degradation.

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays.

Uncoordinated random-access protocols are well-suited for underwater acoustic (UWA) networks due to their simplicity and low overhead. However, their performance is hindered by severe collisions and the challenging characteristics of UWA channels such as rich multipath and Doppler effect. Existing UWA physical layer waveforms struggle to resolve collisions while maintaining high data rates. This paper introduces ZCMod, a high-rate waveform allowing uncoordinated concurrent random access in UWA networks. ZCMod employs a Zadoff–Chu (ZC) sequence-based modulation that assigns unique ZC sequences to users to minimize inter-user interference and encodes multiple bits through cyclic shifts of the sequences to improve data rates. ZCMod further addresses the unique challenges of UWA channels via two new designs: 1) a shape-based demodulation approach that estimates the data-induced shift of channel response shape between the preamble and data symbols to handle rich multipath, and 2) an auxiliary modulation approach that modulates each data symbol with two ZC sequences, one for extracting current channel response shape and the other for data modulation, to handle the fast time-varying channel. Experimental results in a lake and a swimming pool and extensive simulation results show that a) ZCMod achieves around 100% higher throughput compared with the state-of-the-art (SOTA) approaches in quasi-static channels, and b) ZCMod maintains comparable throughput in fast time-varying channels as in quasi-static conditions, where the SOTA approaches experience significant degradation.

Channel state information (CSI) acquisition is essential for the base station (BS) to fully reap the beamforming gain in intelligent reflecting surface (IRS)-aided downlink communication systems. Recently, Wang et al. (2020) revealed a strong correlation in different users’ cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication. In this paper, we aim to exploit the above channel property to reduce the overhead for both pilot and feedback transmission in IRS-aided downlink communication. Note that in the downlink, the distributed users merely receive the pilot signals containing their own CSI and cannot leverage the correlation in different users’ channels, which is in sharp contrast to the uplink counterpart considered in Wang et al. (2020). To tackle this challenge, this paper proposes a novel “quantize-then-estimate” protocol in frequency division duplex (FDD) IRS-aided downlink communication. Specifically, the users quantize and feed back their received pilot signals, instead of the estimated channels, to the BS. After de-quantizing the pilot signals received by all the users, the BS estimates all the cascaded channels by leveraging their correlation, similar to the uplink scenario. Under this protocol, we manage to propose efficient user-side quantization and BS-side channel estimation methods. Moreover, we analytically quantify the pilot and feedback transmission overhead to reveal the significant performance gain of our proposed scheme over the conventional “estimate-then-quantize” scheme.

Due to circuit failures, defective elements that cannot adaptively adjust the phase shifts of their impinging signals in a desired manner may exist on an intelligent reflecting surface (IRS). Traditional way to find these defective IRS elements requires a thorough diagnosis of all the circuits belonging to a huge number of IRS elements, which is practically challenging. In this paper, we will devise a novel approach under which a transmitter sends known pilot signals and a receiver localizes all the defective IRS elements just based on its over-the-air measurements reflected from the IRS. The key lies in the fact that the over-the-air measurements at the receiver side are functions of the set of defective IRS elements. Based on this observation, we propose a bisection based method to localize all the defective IRS elements. Specifically, at each time slot, we properly control the desired phase shifts of all the IRS elements such that half of the considered regime that is not useful to localize the defective elements can be found based on the received signals and removed. Via numerical results, it is shown that our proposed bisection method can exploit the over-the-air measurements to localize all the defective IRS elements quickly and accurately.

This paper revisits the identity detection problem under the current grant-free protocol in massive machine-type communications (mMTC) by asking the following question: for stable identity detection performance, is it enough to permit active devices to transmit preambles without any handshaking with the base station (BS)? Specifically, in the current grant-free protocol, the BS blindly allocates a fixed length of preamble to devices for identity detection as it lacks the prior information on the number of active devices K. However, in practice, K varies dynamically over time, resulting in degraded identity detection performance especially when K is large. Consequently, the current grant-free protocol fails to ensure stable identity detection performance. To address this issue, we propose a two-stage communication protocol which consists of estimation of K in Phase I and detection of identities of active devices in Phase II. The preamble length for identity detection in Phase II is dynamically allocated based on the estimated K in Phase I through a table lookup manner such that the identity detection performance could always be better than a predefined threshold. In addition, we design an algorithm for estimating K in Phase I, and exploit the estimated K to reduce the computational complexity of the identity detector in Phase II. Numerical results demonstrate the effectiveness of the proposed two-stage communication protocol and algorithms.

Linear chirp-based underwater acoustic communication has been widely used due to its reliability and long-range transmission capability. However, unlike the counterpart chirp technology in wireless - LoRa, its throughput is severely limited by the number of modulated chirps in a symbol. The fundamental challenge lies in the underwater multi-path channel, where the delayed signal may cause inter-symbol and intra-symbol interfere. In this paper, we present UWLoRa+, a system that realizes the same chirp modulation as LoRa with higher data rate, and address the multi-path challenge via the following new designs: a) we replace the linear chirp used by LoRa with the non-linear chirp to reduce the signal interference range and the collision probability; b) we design an algorithm that first demodulates each path and then combines the demodulation results of detected paths; and c) we replace the Hamming codes used by LoRa with the non-binary LDPC codes to mitigate the impact of the inevitable collision. Experiment results show that the new designs improve the bit error rate (BER) by 3 times, and the packet error rate (PER) significantly, compared with the LoRa's naive design. Compared with an state-of-the-art system for decoding underwater LoRa chirp signal, UWLoRa+ improves the throughput by up to 50 times.

Uncoordinated random-access protocols are especially suitable for underwater acoustic networks with long propagation delays due to their simplicity. However, their performance is limited by severe collisions caused by uncoordinated access, and the current modulations cannot handle the collisions under the multipath environment. In this paper, we propose a new modulation and a new demodulation algorithm to resolve collisions. In particular, we adopt a Zadoff-Chu (ZC) sequence with cyclic shifts as the modulation, and assign users with different ZC sequences to minimize inter-user interference. To combat the multipath challenge, we leverage the insight that the multipath interference pattern is almost constant within the same packet and the modulated data only shifts the pattern, and develop a pattern-based demodulation algorithm. Trace-driven simulation results show that our new approach allows at least five users, and outperforms the existing approach by at least 8dB. In the future, we intend to develop a real-time system in a realistic environment.

Cooperative broadcast is an efficient approach to improve Wi-Fi broadcast performance in a crowded scenario with densely deployed access points (APs). However, the current concurrent transmission MAC protocols cannot synchronize multi-APs’ signals perfectly for all users. As a result, the superimposed signal from APs is time-varying at the users due to the multiple time-domain channels and carrier frequency offsets (CFOs) from multiple APs. The traditional channel estimation approach that estimates the superimposed channel as a whole is ill-suited for the superimposed signal. In this paper, we propose a fine-grained channel estimation approach to first estimate these channel parameters for each AP, and then reconstruct the superimposed channel. Specifically, we present a two-stage channel estimation algorithm that first estimates the CFOs by discretizing the CFO range and matching the most possible CFOs, and then computes the time-domain channels. Experiment and simulation results show the new channel estimation approach achieves much lower bit error rate (BER) and packet error rate (PER) than the traditional IEEE 802.11 approach. In addition, we propose a distributed mechanism to choose the master AP that initializes multi-APs’ simultaneous transmission, which the current concurrent transmission MAC protocols lack. Network-layer simulation results show that the proposed cooperative broadcast scheme improves the throughput by 64% to 82% compared with the traditional uncooperative broadcast scheme.

This paper focuses on the covariance-based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system. In this system, active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. While the scaling law for the covariance-based activity detection in the single-cell scenario has been extensively analyzed in the literature, this paper aims to analyze the scaling law for the covariance-based activity detection in the multi-cell massive MIMO system. Specifically, this paper demonstrates a quadratic scaling law in the multi-cell system, under the assumption that the path-loss exponent of the fading channel$\gamma \gt 2$. This finding shows that, in the multi-cell massive MIMO system, the maximum number of active devices that can be correctly detected in each cell increases quadratically with the length of the signature sequence and decreases logarithmically with the number of cells (as the number of antennas tends to infinity). Moreover, in addition to analyzing the scaling law for the signature sequences randomly and uniformly distributed on a sphere, the paper also establishes the scaling law for signature sequences based on a finite alphabet, which are easier to generate and store. Finally, this paper proposes two efficient accelerated coordinate descent (CD) algorithms with a convergence guarantee for solving the device activity detection problem. The first algorithm reduces the complexity of CD by using an inexact coordinate update strategy. The second algorithm avoids unnecessary computations of CD by using an active set selection strategy. Simulation results show that the proposed algorithms exhibit excellent performance in terms of computational efficiency and detection error probability.

One of the most important features of next-generation communication is to incorporate intelligence towards semantic communication, where highly condensed semantic information considering both source and channel features will be extracted and transmitted. The recent popular large models such as GPT4 and the boosting learning techniques are envisioned to accelerate its practical implementation in the near future. Given the characteristics of “training once and widely use” of those multimodal large language models, we argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LMaaS). However, the trading and pricing problem is quite complex with heterogeneous and dynamic customer environments, making the pricing optimization problem challenging in seeking on-hand solutions. In this paper, we optimize the profit of both the seller and customers. We formulate the LMaaS market trading as a Stackelberg game with two steps. In the first step, we optimize the seller's pricing decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes the prices of large models iteratively by reasoning customers’ future rental decisions, which achieves a near-optimal pricing solution. In the second step, we optimize customers’ selection decisions by designing a robust selecting and renting (RSR) algorithm, which is guaranteed to be optimal with rigorous theoretical proof. Extensive experiments confirm the effectiveness and robustness of our algorithms, outperforming the state-of-the-art solution by 43.96% in profit at the customer side and achieving the near-optimal profit at the seller side.

This paper presents a Long Range (LoRa) data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes’ data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3$\times$in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1$\times$for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devicesK. This is sharply different from the activity detection problem in traditional setups, in which the knowledge ofKis not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator forKand a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation ofKare closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation ofKand the design of the ADC quantizer; 2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol.

For intelligent reflecting surface (IRS) aided down-link communication in frequency division duplex (FDD) systems, the overhead for the base station (BS) to acquire channel state information (CSI) is extremely high under the conventional “estimate-then-quantize” scheme, where the users first estimate and then feed back their channels to the BS. Recently, [1] revealed a strong correlation in different users' cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication. In this paper, we aim to exploit the above channel property for reducing the overhead of both pilot transmission and feedback transmission in IRS-aided downlink communication. Different from the uplink counterpart where the BS possesses the pilot signals containing the CSI of all the users, in downlink communication, the distributed users merely receive the pilot signals containing their own CSI and cannot leverage the correlation in different users' channels revealed in [1]. To tackle this challenge, this paper proposes a novel “quantize-then-estimate” protocol in FDD IRS-aided downlink communication. Specifically, the users first quantize their received pilot signals, instead of the channels estimated from the pilot signals, and then transmit the quantization bits to the BS. After de-quantizing the pilot signals received by all the users, the BS estimates all the cascaded channels by leveraging the correlation embedded in them, similar to the uplink scenario. Under this protocol, we propose efficient methods for quantization at the user side and channel estimation at the BS side. Furthermore, we manage to show both analytically and numerically the great overhead reduction in pilot transmission and feedback transmission arising from our proposed “quantize-then-estimate” protocol.

This paper studies the covariance based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, where the active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. The scaling law of covariance based activity detection in the single-cell scenario has been thoroughly analyzed in the literature. This paper aims to analyze the scaling law of covariance based activity detection in the multi-cell massive MIMO system. In particular, this paper shows a quadratic scaling law in the multi-cell system under the assumption that the exponent in the classical path-loss model is greater than 2, which demonstrates that in the multi-cell MIMO system the maximum number of active devices that can be correctly detected in each cell increases quadratically with the length of the signature sequence and decreases logarithmically with the number of cells (as the number of antennas tends to infinity). This paper also characterizes the distribution of the estimation error in the multi-cell scenario.

In a two-way relay channel (TWRC), physical-layer network coding (PNC) doubles the system throughput by turning superimposed signals transmitted simultaneously by different end nodes into useful network-coded information (known as PNC de-coding). Prior works indicated that the PNC decoding performance is affected by the relative phase offset between the received signals from different nodes. In particular, some "bad" relative phase offsets could lead to huge performance degradation. Previous solutions to mitigate the relative phase offset effect were limited to the conventional bit-oriented communication paradigm, aiming at delivering a given information stream as quickly and reliably as possible. In contrast, this paper puts forth the first semantic communication-empowered PNC-enabled TWRC to address the relative phase offset issue, referred to as SC-PNC. Despite the bad relative phase offsets, SC-PNC directly extracts the semantic meaning of transmitted messages rather than ensuring accurate bit stream transmission. We jointly design deep neural network (DNN)-based transceivers at the end nodes and propose a semantic PNC decoder at the relay. Taking image delivery as an example, experimental results show that the SC-PNC TWRC achieves high and stable image reconstruction quality under different channel conditions and relative phase offsets, compared with the conventional bit-oriented counterparts.

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

In this letter, we study the joint device activity and delay detection problem in asynchronous massive machine-type communications (mMTC), where all active devices asynchronously transmit their preassigned preamble sequences to the base station (BS) for device identification and delay detection. We first formulate this joint detection problem as a maximum likelihood estimation problem, which depends on the received signal only through its sample covariance, and then propose efficient coordinate descent type of algorithms to solve the formulated problem. Our proposed covariance-based approach is sharply different from the existing compressed sensing (CS) approach for the same problem. Numerical results show that our proposed covariance-based approach significantly outperforms the CS approach in terms of the detection performance since our proposed approach can make better use of the BS antennas than the CS approach.

This paper investigates the application of physical-layer network coding (PNC) to Industrial Internet of Things (IIoT) in which a controller and a robot are out of each other’s transmission range, and they exchange messages with the assistance of a relay. We particularly focus on a scenario where 1) the controller has more information to transmit than the robot; 2) the channel of the controller is stronger than that of the robot, and both users have nearly the same transmit power. To reduce the communication latency, we put forth an asymmetric PNC transmission scheme in which the controller transmits more information than the robot by exploiting its stronger channel gain in the uplink of PNC. However, the current channel-coded PNC requires the two users to transmit the same amount of source information in order to preserve the linearity of the two users’ channel codes at the relay for successful decoding. Therefore, a challenge in the asymmetric PNC transmission scheme is how to construct a channel decoder at the relay, considering that a superimposed symbol at the relay contains different amounts of source information from the controller and robot. To fill this gap, we propose a lattice-based encoding and decoding scheme in which the robot and controller encode and modulate their information in lattices with different lattice construction levels. The network-coded messages are decoded level-by-level in the lattice. Our design is versatile on that the controller and the robot can freely choose their modulation orders based on their channel power, and the design is applicable for arbitrary channel codes, not just for one particular channel code. The simulation results demonstrate the effectiveness of the proposed channel-coded asymmetric PNC transmission scheme.

In the intelligent reflecting surface (IRS) assisted communication systems, the acquisition of channel state information (CSI) is a crucial impediment for achieving the passive beamforming gain of IRS because of the considerable overhead required for channel estimation. Specifically, under the current beamforming design for IRS-assisted communications, KMN + KM channel coefficients should be estimated if the passive IRS cannot estimate its channels with the base station (BS) and users due to its lack of radio frequency (RF) chains, where K, N and M denote the numbers of users, reflecting elements of the IRS, and antennas at the BS, respectively. These numbers can be extremely large in practice considering the current trend of massive MIMO (multiple-input multiple-output), i.e., a large M, and massive connectivity, i.e., a large K. To accurately estimate such a large number of channel coefficients within a short time interval, we devote our endeavour in this paper to investigating the efficient pilot-based channel estimation method in IRS-assisted uplink communications. Building upon the observation that each IRS element reflects the signals from all the users to the BS via the same channel, we analytically verify that a time duration consisting of K+N+max(K-1, [(K-1)N/M)] pilot symbols is sufficient for the BS to perfectly recover all the KMN + KM channel coefficients for the case without receiver noise. In contrast to the conventional uplink communications without IRS in which the minimum pilot sequence length is independent with the number of receive antennas, our study reveals the significant role of massive MIMO in reducing the channel training time for IRS-assisted communications.

This paper investigates coherent detection for physical-layer network coding (PNC) with short packet transmissions in a two-way relay channel (TWRC). PNC turns superimposed EM waves into network-coded messages to improve throughput in a relay system. To achieve this, accurate channel information at the relay is a necessity. Much prior work applies preambles to estimate the channel. For long packets, the preamble overhead is low because of the large data payload. For short packets, that is not the case. To avoid excessive overhead, we consider a set-up in which short packets do not have preambles. A key challenge is how the relay can estimate the channel and detect the network-coded messages jointly based on the received signals from the two end users. We design a coherent detector that makes use of a belief propagation (BP) algorithm to do so. For concreteness, we focus on binary frequency-shift-keying (FSK) modulation. We show how the BP algorithm can be simplified and made practical with Gaussian-mixture passing. In addition, we demonstrate that prior knowledge on the channel distribution is not needed with our framework. Benchmarked against the detector with prior knowledge of the channel distribution, numerical results show that our detector can have nearly the same performance without such prior knowledge.

In intelligent reflecting surface (IRS) assisted communication systems, the acquisition of channel state information is a crucial impediment for achieving the beamforming gain of IRS because of the considerable overhead required for channel estimation. Specifically, under the current beamforming design for IRS-assisted communications, in total KMN+KM channel coefficients should be estimated, where K, N and M denote the numbers of users, IRS reflecting elements, and antennas at the base station (BS), respectively. For the first time in the literature, this paper points out that despite the vast number of channel coefficients that should be estimated, significant redundancy exists in the user-IRS-BS reflected channels of different users arising from the fact that each IRS element reflects the signals from all the users to the BS via the same channel. To utilize this redundancy for reducing the channel estimation time, we propose a novel three-phase pilot-based channel estimation framework for IRS-assisted uplink multiuser communications, in which the userBS direct channels and the user-IRS-BS reflected channels of a typical user are estimated in Phase I and Phase II, respectively, while the user-IRS-BS reflected channels of the other users are estimated with low overhead in Phase III via leveraging their strong correlation with those of the typical user. Under this framework, we analytically prove that a time duration consisting of K + N + max(K - 1, [(K - 1)N/M]) pilot symbols is sufficient for perfectly recovering all the KMN + KM channel coefficients under the case without receiver noise at the BS. Further, under the case with receiver noise, the user pilot sequences, IRS reflecting coefficients, and BS linear minimum mean-squared error channel estimators are characterized in closed-form.

This paper investigates noncoherent detection in a two-way relay channel operated with physical- layer network coding (PNC), assuming FSK modulation and short-packet transmissions. For noncoherent detection, the detector has access to the magnitude but not the phase of the received signal. For conventional communication in which a receiver receives the signal from a transmitter only, the phase does not affect the magnitude, hence the performance of the noncoherent detector is independent of the phase. PNC, on the other hand, is a multiuser system in which a receiver receives signals from multiple transmitters simultaneously. The relative phase of the signals from different transmitters affects the received signal magnitude through constructive-destructive interference. In particular, for good performance, the noncoherent detector of a multiuser system such as PNC must take into account the influence of the relative phase on the signal magnitude. Building on this observation, this paper delves into the fundamentals of PNC noncoherent detector design. To avoid excessive overhead, we assume a set-up in which the short packets in the PNC system do not have preambles. We show how the relative phase can be deduced directly from the magnitudes of the received data symbols, and that the knowledge of the relative phase thus deduced can in turn be used to enhance performance of noncoherent detection. We design a noncoherent detector that jointly estimates relative phase and detects data using a belief propagation algorithm. Numerical results show that our detector performs as well as a “fictitious” optimal detector that has perfect knowledge of the relative phase. Although this paper focuses on PNC with FSK modulation, we believe the insight of this paper applies generally to noncoherent detection in other multiuser systems with other modulations. Specifically, our insight is that the relative phase of overlapped signals affects the signal magnitude in multiuser systems, but fortunately the relative phase can be deduced from the magnitudes and this knowledge can be used to improve detection performance.

This paper investigates the noncoherent detection in a two-way relay channel operated with physical-layer network coding (PNC), assuming FSK modulation and short-packet transmissions. For noncoherent detection, the detector has access to the magnitude but not the phase of the received signal. For conventional communication in which a receiver receives the signal from a transmitter only, the phase does not affect the magnitude, and hence the performance of the noncoherent detector is independent of the phase. PNC, on the other hand, is a multiuser system in which a receiver receives signals from multiple transmitters simultaneously. The relative phase of the signals from different transmitters affects the received signal magnitude through constructive-destructive interference. In particular, for good performance, the noncoherent detector of a multiuser system such as PNC must take into account the influence of the relative phase on the signal magnitude. Building on this observation, this paper delves into the fundamentals of PNC noncoherent detector design. To avoid excessive overhead, we assume a set-up in which the short packets in the PNC system do not have preambles. We show how the relative phase can be deduced directly from the magnitudes of the received data symbols, and that the knowledge of the relative phase thus deduced can in turn be used to enhance performance of noncoherent detection. Our overall detector design consists of two components: 1) a channel gains estimator that estimates channel gains without preambles; and 2) a detector that builds on top of the estimated channel gains to jointly estimate relative phase and detect data using a belief propagation algorithm. Numerical results show that our detector performs nearly as well as a “fictitious” optimal detector that has perfect knowledge of the channel gains and relative phase. Although this paper focuses on PNC with FSK modulation, we believe that the insight of this paper applies generally to noncoherent detection in other multiuser systems with other modulations. Specifically, our insight is that the relative phase of overlapped signals affects the signal magnitude in multiuser systems, but fortunately the relative phase can be deduced from the magnitudes and this knowledge can be used to improve the detection performance.