Knowledge Of Channel State Information Research Articles

Optimizing beamforming in IoT-WET: A symbiotic-based approach under imperfect channel state information

This study delves into the energy optimization problem in low-power Internet of Things (IoT) networks utilizing wireless energy transfer (WET) technology. In such IoT systems, conventional optimization schemes relying on complete knowledge of channel state information (CSI) are often irrelevant for practical implementation due to their high complexity and energy consumption. To address this challenge and enhance the fairness of energy harvesting among IoT devices with imperfect CSI, we propose a novel beamforming design based on the symbiotic organisms search (SOS) algorithm. Through comprehensive numerical analysis, we demonstrate the effectiveness of our proposed scheme in terms of computational complexity, convergence speed, and energy harvested at each IoT device. The results highlight the significant advantages of our approach compared to conventional methods, paving the way for practical and efficient energy optimization in IoT networks.

Robust Beamforming for Downlink Multi-Cell Systems: A Bilevel Optimization Perspective

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming technique that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties.

Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attack

Due to the open communications environment in wireless channels, wireless networks are vulnerable to jamming attacks. However, existing approaches for jamming rely on knowledge of the legitimate users’ (LUs’) channels, extra jamming power, or both. To raise concerns about the potential threats posed by illegitimate intelligent reflecting surfaces (IRSs), we propose an alternative method to launch jamming attacks on LUs without either LU channel state information (CSI) or jamming power. The proposed approach employs an adversarial IRS with random phase shifts, referred to as a “disco” IRS (DIRS), that acts like a “disco ball” to actively age the LUs’ channels. Such active channel aging (ACA) interference can be used to launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems. The proposed DIRS-based fully-passive jammer (FPJ) can jam LUs with no additional jamming power or knowledge of the LU CSI, and it can not be mitigated by classical anti-jamming approaches. A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived. Based on this detailed theoretical analysis, some unique properties of the proposed DIRS-based FPJ can be obtained. Furthermore, a design example of the proposed DIRS-based FPJ based on one-bit quantization of the IRS phases is demonstrated to be sufficient for implementing the jamming attack. In addition, numerical results are provided to show the effectiveness of the derived theoretical analysis and the jamming impact of the proposed DIRS-based FPJ.

Two-way differential strategy for wireless sensor networks

In this paper, a novel optimal two-way amplify and forward (AF) differential beamforming method for wireless sensor network is proposed. The proposed method is an advanced signal processing technique used to enhance the performance and reliability of the communication link by exploiting the diversity provided by multiple antennas. Unlike current state-of-the-art methods which require the knowledge of channel state information (CSI) at both transmitting and receiving antennas or at least at the receiving antennas, the suggested method does not need CSI at any transmitting or receiving antenna. Moreover, the proposed method enjoys high error performance with high diversity and coding gain and has a very low encoding and decoding complexity. Through our simulations, the proposed method is proved to outperform the best known non-coherent multi-antenna methods.

Complexity-Efficient Sidelink Synchronization Signal Detection Scheme for Cellular Vehicle-to-Everything Communication Systems

Synchronization is a challenging issue in vehicle-to-everything (V2X) cellular communication, especially when V2X devices need to directly communicate with each other outside the network coverage area. By adopting the maximum likelihood principle, we propose joint detection of the sidelink secondary synchronization signal (SL-SSS) and carrier frequency offset (CFO) in a V2X system using 5G new radio sidelink. We formulate an effective joint coherent synchronization scheme for cellular V2X applications by decoupling the estimation of the sidelink identity and CFO, which requires a priori knowledge of channel state information. To verify the feasibility of the proposed detection scheme, we derive a simplified implementation of the proposed SL-SSS detector and a closed-form expression for the detection probability. Simulation results demonstrate that the proposed SL-SSS detector exhibits either comparable performance in terms of detection probability or reduced complexity when compared with conventional SL-SSS detectors. Using the proposed method in the cellular V2X system enables V2X devices to achieve robust synchronization with reduced power consumption during the initial synchronization procedure, while also offering valuable insights for designing a simple, efficient sidelink synchronization receiver.

Modified gate activation functions of Bi-LSTM-based SC-FDMA channel equalization

Abstract In recent years, artificial neural networks (ANNs) have grown a lot and helped solve numerous problems in wireless communication systems. We have evaluated the performance of the Bidirectional-Long-Short-Term-Memory (Bi-LSTM) recurrent neural networks (RNNs) for joint blind channel equalization and symbol detection using a variety of activation functions (Afs) for the gate units (sigmoid) of Bi-LSTMs without requiring any prior knowledge of channel state information (CSI). The performance of Bi-LSTM networks with different AFs found in the literature is compared. This comparison was carried out with the assistance of three different learning algorithms, namely Adam, rmsprop, and SGdm. The research findings clearly show that performance, as measured by equalization accuracy, can be improved. Furthermore, demonstrate that the sigmoid gate activation function (GAF), which is commonly used in Bi-LSTMs, does not significantly contribute to optimal network behavior. In contrast, there are a great many less well-known AFs that are capable of outperforming the ones that are most frequently utilized.

Outage and DMT Analysis of Partition-Based Schemes for RIS-Aided MIMO Fading Channels

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) fading channels assisted by a reconfigurable intelligent surface (RIS), through the employment of partition-based RIS schemes. The proposed schemes are implemented without requiring any channel state information knowledge at the transmitter side; this characteristic makes them attractive for practical applications. In particular, the RIS elements are partitioned into sub-surfaces, which are periodically modified in an efficient way to assist the communication. Under this framework, we propose two low-complexity partition-based schemes, where each sub-surface is adjusted by following an amplitude-based or a phase-based approach. Specifically, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">activate-reflect</i> (AR) scheme activates each sub-surface consecutively, by changing the reflection amplitude of the corresponding elements. On the other hand, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flip-reflect</i> (FR) scheme adjusts periodically the phase shift of the elements at each sub-surface. Through the sequential reconfiguration of each sub-surface, an equivalent parallel channel in the time domain is produced. We analyze the performance of each scheme in terms of outage probability and provide expressions for the achieved diversity-multiplexing tradeoff. Our results show that the asymptotic performance of the considered network under the partition-based schemes can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, the FR scheme always achieves the maximum multiplexing gain, while for the AR scheme this maximum gain can be achieved only under certain conditions with respect to the number of elements in each sub-surface.

On Outage-Based Beamforming Design for Dual-Functional Radar-Communication 6G Systems

This article studies and derives beamforming design in a dual-functional radar-communication (DFRC) multiple-input-multiple-output system. We focus on a scenario, where the DFRC base station communicates with downlink communication users, with imperfect channel state information knowledge, and performs target detection, all via the same transmit signal. Through careful relaxation procedures, we arrive at a suitable and novel optimization problem, which maximizes the radar output power in the Bartlett sense, under probabilistic outage signal-to-interference-and-noise ratio constraints. Theoretical analysis proves optimality of the solution given by the relaxed version of the problem, as well as closed-form solutions in certain scenarios. Finally, the achieved performances and trade-offs of the proposed beamforming design are demonstrated through numerical simulations.

Self-Pilot Tone Based Adaptive Threshold RZ-OOK Decision for Free-Space Optical Communications

This paper studies a novel self-pilot tone based adaptive threshold return-to-zero on-off keying (RZ-OOK) decision for free-space optical (FSO) communications. RZ-OOK has the characteristics of impulse series in the spectrum. Therefore, these impulses can be utilized as the pilot tones of the transmitted signal to convey the channel state information (CSI) of FSO links. Then, the CSI signal is extracted using a local oscillator (LO) with the frequencies of the impulse series and low pass filter. Finally, the adaptive threshold decision (ATD) is realized by assigning optimized weight factors into the extracted CSI signal. The proposed adaptive threshold RZ-OOK decision was studied in simulation under various pulse durations of RZ-OOK signal and frequencies of LO. Simulation results demonstrated that RZ-OOK with the proposed impulse tone-extracted CSI signal under optimized weight factor performs close to the conventional ATD under precise CSI knowledge.

Deep Learning-Based Soft Iterative-Detection of Channel-Coded Compressed Sensing-Aided Multi-Dimensional Index Modulation

The concept of Index Modulation (IM) has been actively researched as a benefit of its flexible trade-off between performance, achievable rate, energy efficiency, hardware cost and complexity. In order to fully exploit its the degrees of freedom, the concept of Multi-dimensional IM (MIM) has been developed in literature, where Compressed Sensing (CS) is often utilized to exploit the sparsity of the multi-dimensional transmitted signals. However, this flexibility and performance gains are attained at the cost of a substantially increased detection complexity. In this paper, we propose Deep Learning (DL) based detection for CS-aided MIM (CS-MIM), where both Hard-Decision (HD) as well as Soft-Decision (SD) detection combined with iterative decoding are conceived. More explicitly, firstly, we propose learning aided hard and soft detection for CS-MIM. Secondly, two novel neural network aided methods are proposed for Iterative Soft Detection (ISD), where iterations are carried out between the CS-MIM detector and a channel decoder. In contrast to the conventional detection of CS-MIM system, which critically relies on the knowledge of Channel State Information (CSI) at the receiver, the proposed learning-aided methods are capable of eliminating the overhead and complexity of Channel Estimation (CE), which results in an improved transmission rate. Explicitly, we develop an advanced DL architecture for blind-detection-aided MIM for the first time in the open literature, where the HD and SD CS algorithms are implemented by learning without the need for CE. Our simulation results demonstrate that the proposed learning techniques conceived for SD CS-MIM combined with iterative detection are capable of achieving near-capacity performance at a reduced complexity compared to the conventional model-based SD relying on CSI acquisition.

A New Transceiver Design for Protograph LDPC-Coded LACO-OFDM VLC Systems With Deep Learning

As a spectral-efficient and transmission-reliable technique, channel coded layered asymmetrically clipped optical orthogonal frequency-division multiplexing (LACO-OFDM) has attracted considerable interest in visible light communications. In this letter, we investigate the protograph low-density parity-check (LDPC)-coded LACO-OFDM VLC systems. Specially, we design a novel type of interleaving scheme, referred to as “variable-node subcarrier matched mapping (VNSMM)” to optimize the performance of protograph-coded LACO-OFDM VLC systems. In such scenario, the conventional iterative detection framework cannot completely eliminate the inter-layer interference (ILI), which degrades the system performance. To further suppress the ILI at the receiver, we propose a deep learning (DL)-based detection framework, called “DeepLACO”, to simultaneously obtain the log-likelihood ratios (LLRs) used for the protograph decoders in all layers without the knowledge of channel state information (CSI). Analyses and simulations demonstrate that the protograph-coded LACO-OFDM exploiting the proposed interleaving scheme and detection framework can achieve excellent performance in VLC systems.

Artificial Intelligence Augmentation for Channel State Information in 5G and 6G

In this article, we present an artificial intelligence (AI) augmentation framework for physical layer communication applicable to both 5G and future 6G networks. The framework classifies the channel state information (CSI), and uses the classified CSI knowledge to adapt transmission configurations, perform resource optimization, and improve essential signal processing modules such as channel estimation. We demonstrate feasibility and benefits of the proposed AI augmentation in different use cases in the 5G NR context, such as beamforming mode adaptation, reference signal resource optimization, and link adaptation, as well as channel estimation. The framework also allows extension to resolve future 6G challenges.

Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. In traditional communication systems, the compressed CSI bits is treated equally and expected to be transmitted accurately over the noisy channel. While the errors occur due to the limited bandwidth or low signal-to-noise ratios (SNRs), the reconstruction performance of the CSI degrades drastically. As a branch of semantic communications, deep joint source-channel coding (DJSCC) scheme performs better than the separate source-channel coding (SSCC) scheme—the cornerstone of traditional communication systems—in the limited bandwidth and low SNRs. In this paper, we propose a DJSCC based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

On the Coexistence of eMBB and URLLC in Multi-Cell Massive MIMO

The non-orthogonal coexistence between the enhanced mobile broadband (eMBB) and the ultra-reliable low-latency communication (URLLC) in the downlink of a multi-cell massive MIMO system is rigorously analyzed in this work. We provide a unified information-theoretic framework blending an infinite-blocklength analysis of the eMBB spectral efficiency (SE) in the ergodic regime with a finite-blocklength analysis of the URLLC error probability relying on the use of mismatched decoding, and of the so-called saddlepoint approximation. Puncturing (PUNC) and superposition coding (SPC) are considered as alternative downlink coexistence strategies to deal with the inter-service interference, under the assumption of only statistical channel state information (CSI) knowledge at the users. eMBB and URLLC performances are then evaluated over different precoding techniques and power control schemes, by accounting for imperfect CSI knowledge at the base stations, pilot-based estimation overhead, pilot contamination, spatially correlated channels, the structure of the radio frame, and the characteristics of the URLLC activation pattern. Simulation results reveal that SPC is, in many operating regimes, superior to PUNC in providing higher SE for the eMBB yet achieving the target reliability for the URLLC with high probability. Moreover, PUNC might cause eMBB service outage in presence of high URLLC traffic loads. However, PUNC turns to be necessary to preserve the URLLC performance in scenarios where the multi-user interference cannot be satisfactorily alleviated.

Anchor-Aided Channel Estimation for RIS-Assisted Multiuser Broadband Communications

Due to the passive nature of reconfigurable intelligent surface (RIS) and a large number of users, it is very challenging to estimate the cascaded base station (BS)-RIS-user channel directly, especially for multiuser broadband communication systems. In this letter, we propose a novel two-phase method to estimate multiuser broadband cascaded channels with the assistance of two anchor nodes. Specifically, in the first phase, the cascaded channel from the two anchors to the BS and that between two anchors are estimated to acquire the BS-RIS channel state information (CSI) with sign ambiguities. It is accomplished based on cyclic-prefixed single-carrier transmission and phase configuration at the RIS, which creates multiple cyclic delayed versions of transmitted signals for isolating all broadband channel taps. In the second phase, the RIS-user channel and subsequently the cascaded BS-RIS-user channel are estimated in the frequency domain using the knowledge of the partial BS-RIS CSI. Simulation results verify the superiority of the proposed scheme over the state-of-the-art methods in terms of estimation overhead, normalized mean-squared error and bit error rate.

Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI

In this paper, the performance of a deep learning based multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Further more, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results.

Learning to Transmit Fresh Information in Energy Harvesting Networks

We study age of information (AoI) minimization in an ad hoc network consisting of energy harvesting transmitters that are scheduled to send status updates to their intended receivers. The transmission scheduling with power allocation problem over a communication session is first studied assuming apriori knowledge of channel state information, harvested energy, and update packet arrivals, i.e., the offline setting. The global optimal scheduling policy in this case is the solution of a mixed integer linear program which is known to be computationally hard. We propose a supervised-learning-based algorithm to mitigate the high computational complexity. A bidirectional recurrent neural network that interprets user scheduling as a time-series classification problem is trained and tested to achieve near-optimal AoI. Next, we consider online scheduling and power allocation with causal knowledge of the system state, which is an infinite-state Markov decision problem. In this case, the related reinforcement learning problem is solved by a model-free on-policy deep reinforcement learning, where the actor-critic algorithm with deep neural network function approximation is implemented. Comparable AoI to the optimal is demonstrated and faster runtime of learning solvers is observed, verifying the efficacy of learning in terms of both optimality and computational energy efficiency for AoI-focused scheduling and resource allocation problems in wireless networks.

Two-Timescale Optimization for Intelligent Reflecting Surface-Assisted MIMO Transmission in Fast-Changing Channels

The application of intelligent reflecting surface (IRS) depends on the knowledge of channel state information (CSI), and has been hindered by the heavy overhead of channel training, estimation, and feedback in fast-changing channels. This paper presents a new two-timescale beamforming approach to maximizing the average achievable rate (AAR) of IRS-assisted MIMO systems, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the base station precoder and power allocation are updated frequently based on quickly outdated instantaneous CSI (I-CSI). The key idea is that we first reveal the optimal small-timescale power allocation based on outdated I-CSI yields a water-filling structure. Given the optimal power allocation, a new mini-batch sampling (mbs)-based particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS configuration with reduced channel samples. Another important aspect is that we develop a model-driven PSO algorithm to optimize the IRS configuration, which maximizes a lower bound of the AAR by only using the S-CSI and eliminates the need of channel samples. The model-driven PSO serves as a dependable lower bound for the mbs-PSO. Simulations corroborate the superiority of the new two-timescale beamforming strategy to its alternatives in terms of the AAR and efficiency, with the benefits of the IRS demonstrated.

Federated Learning via Over-the-Air Computation With Statistical Channel State Information

Federated learning (FL) is a popular distributed learning paradigm, in which a global model at a server learns private data of clients without data shared among clients or the server. In this paper, we consider FL over a noisy fading multiple access channel (MAC) via over-the-air computation (AirComp). Benefiting from waveform-superposition propriety of wireless signals, AirComp is able to achieve fast aggregations in FL and improve spectral efficiency. However, most of the schemes exploiting AirComp require intensive channel estimations as the demands from precoders, which results in considerable communication overheads. For this reason, we propose two novel FL schemes with statistical channel state information (FL-SCSI-A and FL-SCSI-B) to reduce the efforts required by channel estimations. In FL-SCSI-A, precoders adjust phases of transmitted signals with phases of instant channel state information (CSI), and scale transmitted signal powers based on statistical CSI. The precoder design has the following two advantages. First, phases of instant CSI can be easier to estimate than complete instant CSI. Second, since clients only need to estimate phases of their own instant CSI (instead of CSI of all clients), with channel reciprocity, this can be easily achieved by letting the server broadcast pilots to all clients. The server in FL-SCSI-A is also efficient. It only needs to estimate the sum of channel gains of all clients, which can be easily achieved by letting clients transmit pilots simultaneously. To further reduce the communication overhead, FL-SCSI-B is proposed. The precoders in FL-SCSI-B are similar to FL-SCSI-A, while the server does not require any knowledge of instant CSI, which reduces the demands of channel estimations. For both schemes, we prove that the distortion caused by the noisy fading MAC is bounded, and the convergences of the learning processes are guaranteed for strongly smooth losses with heterogeneous data assumptions. Experimental results show that the proposed schemes perform better than benchmark schemes while reducing efforts required by channel estimation.

Robust Beamforming for Active Reconfigurable Intelligent Omni-Surface in Vehicular Communications

Two key impediments to reconfigurable intelligent surface (RIS)-aided vehicular communications are, respectively, the double fading experienced by the signal on RIS-aided cascaded links and the high-mobility-induced intractability of acquiring channel state information (CSI). To overcome these challenges, a novel kind of RIS is presented in this paper, namely active reconfigurable intelligent omni-surface (RIOS), each element of which is supported by active loads, that concurrently transmits and reflects the incident signal amplified rather than just reflecting it as compared to the case of a passive reflecting-only RIS. We consider the use of an active RIOS to a vehicular communication system for mitigating double fading effect. Specifically, the active RIOS is mounted on the vehicle window to enhance transmission for users in the vehicle and for adjacent vehicles. We aim to jointly optimize the transmit precoding matrix at the base station (BS) and RIOS coefficient matrices to minimize the BS’s transmit power relying exclusively upon the imperfect knowledge of the large-scale CSI. To significantly relax the frequency of channel information updates, initially an efficient transmission protocol is put forward to reap the high active RIOS beamforming gain with low channel training overhead by appropriately tailoring the time-scale of CSI acquisition. Then, two algorithms, namely an alternating optimization (AO)-based algorithm and a constrained stochastic successive convex approximation (CSSCA)-based algorithm, are developed to tackle with the investigated resource allocation problem, whose pros and cons are elaborated, respectively. Simulation results substantiate the significant performance improvement of active RIOS as well as determine the validity and robustness of our proposed algorithms over various benchmark schemes.