The rapid growth of the Internet of Things (IoT) has heightened the need for energy-efficient wireless sensor networks (WSNs), in which sensor nodes operate under strict battery constraints. This paper proposes a Low-Energy Circular Cluster Hierarchy (LECCH), which combines Density-Based Distorted Circle Clustering (DBDCC) with a novel Concentric Circle Band Routing (CCBR) protocol for energy-aware clustering and multi-hop routing. The proposed framework is governed by key routing and clustering parameters, including the network communication range, cluster radius limit, concentric band width, hop limit, and cluster-head (CH) selection mode, thereby enabling adaptation to different WSN scales and node densities. A theoretical energy-consumption analysis demonstrates that LECCH requires less per-iteration energy than the low-energy adaptive clustering hierarchy (LEACH) in practical network configurations. CCBR employs a three-phase concentric band-based routing framework and supports adaptive CH selection, designating one master CH and multiple worker CHs. We further introduce the minimum angle neighbor selection algorithm (MANSA) for efficient next-hop selection, minimizing both transmission energy and angular deviation toward the CH. MANSA achieves a time complexity of $\mathcal {O}(N^{3/2})$, outperforming array-based $\mathcal {O}(N^{3})$ and heap-based $\mathcal {O}(N^{2} \log N)$ Dijkstra's algorithms, making it scalable for larger WSNs. Simulation results on WSN deployments demonstrate that LECCH consistently outperforms LEACH across various clustering algorithms in terms of network lifetime, transmitted data volume, and average throughput. In particular, the multi-CH configuration significantly improves network sustainability by mitigating CH energy depletion and enhancing routing robustness.

In this letter, we investigate an unexplored yet critical issue: channel estimation (CE) for orthogonal time frequency space (OTFS) under delay-Doppler (DD) domain jamming attacks. To address the challenge of CE under DD jamming attacks, we propose a robust, spectrally efficient, low-complexity, and fast-converging recursive Versoria-aware Champernowne function (VACF)-based adaptive CE algorithm. We derive the recursive channel estimate update equation of the proposed algorithm and present a detailed analysis of its computational complexity to demonstrate its applicability in practice. Along with the simulation results, we conducted an experiment employing software-defined radios to verify the resilience of the proposed algorithm in a real-time setting.

Ambient backscatter communication (ABC) is a promising technology for future wireless systems due to its ultra-low-power operation and simplicity. To further enhance its performance, multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) has been adopted in this work. However, carrier frequency offset (CFO) can disrupt the orthogonality of the MIMO-OFDM signal, which significantly degrades the performance of the reader’s receiver. To address this issue, we consider an asynchronous ABC system and propose a deep learning (DL) algorithm to estimate and compensate the CFO at the reader, ensuring reliable communication in MIMO-OFDM-based ABC systems. The proposed architecture integrates a hybrid attention mechanism, which captures inter-dependencies between the two types of extracted features, and a squeeze-and-excitation network that adaptively refines channel-wise feature responses. The proposed architecture can also work for conventional OFDM and MIMO-OFDM systems. Furthermore, to validate the model’s robustness and generalization capability, additional test datasets are generated for various modulation schemes and different 3GPP TR38.901 tapped delay line channel models. The simulation results indicate its outperformance over existing approaches and has lower computational complexity than the existing methods.

Integrated sensing and communication (ISAC), near-field communication, and extremely large-scale antenna arrays are transformative and indispensable technologies that revolutionize 6G communications. This work proposes a monostatic ISAC architecture for near-field systems employing extremely large-scale uniform planar arrays (XL-UPAs). A theoretical analysis of multi-user interference and sensing-over-communication interference is provided for the considered near-field ISAC system, where the sensing and communication channels are modeled using a non-uniform spherical wave (NUSW) model. In addition to communication, a monostatic ISAC system must ensure simultaneous target detection with a constant probability of false alarm, independent of noise statistics. A grid-based two-dimensional constant false alarm rate (2D-CFAR) detection scheme is proposed to address the issue. Furthermore, an analytical expression is derived to quantify the achievable range resolution in near-field ISAC systems.

This letter proposes an integrated full-duplex and ambient backscatter communication (FD-AmBC) system using a single dual-polarized reconfigurable intelligent surface (DP-RIS). The proposed system enables full-duplex communication by optimizing phase shifts of vertically polarized components, while enhancing tag activation energy and improving signal strength at the reader through the horizontally polarized components of the DP-RIS. We formulate a joint optimization problem to maximize the reader’s received signal while ensuring sufficient energy harvesting at the tag. The resulting non-convex problem is efficiently solved by reformulating it into a convex form using Dinkelbach’s transform and Taylor series approximation. The system is further evaluated under practical conditions, considering hardware impairments, imperfect channel state information, and polarization mismatch. Performance analysis in terms of outage probability, energy efficiency, and ergodic capacity shows significant improvements over existing techniques.

Massive multiple-input multiple-output (MIMO) systems employing partially connected structures (PCSs) require sparse precoders with ideal correlation properties for omni-directional broadcasting. While two-dimensional sparse Golay complementary array sets (2D SGCASs) have been proposed for this purpose, existing constructions are largely restricted to specific powers-of-two dimensions and fixed set sizes. This paper presents a novel algebraic framework based on weighing matrices to construct flexible sparse precoders for PCS-based massive MIMO systems. The proposed (Q, Q,Q − k, 2L/Q)-2D SGCAS construction employs weighing matrices derived from a Golay complementary pair (GCP) and its corresponding mate. The proposed framework provides triple flexibility by supporting row dimensions and set sizes that are not restricted to powers of two, together with fully adjustable column dimensions given by L2 = Q − k, thus allowing for every integer column dimension in the range 2 ≤ L2 ≤ Q. Here, Q = 2(N + L), 0 ≤ k ≤ Q − 2, N = 2a10b26c with a, b, c ≥ 0, and L is an even positive integer. Simulation results demonstrate that the proposed precoder achieves stable omnidirectional transmission and outperforms conventional Zadoff-Chu (ZC) and random sparse precoding schemes.

In this paper, integration of intelligent reflecting surface (IRS) with drone-assisted network-coded cooperation (UA-NCC) system is proposed for improving the reliability of the infrastructure-less network in sixth generation (6G) wireless communication. A mathematical framework is proposed by considering Nakagami-$m$ fixed channel gain for ground-to-ground (G2G) links and height-dependent Nakagami-$m$ channel gain for air-to-ground (A2G) links. Moreover, a cumulative distribution function (CDF) for end-to-end (E2E) signal-to-noise ratio (SNR) is derived for the proposed scheme, by considering the signal is reached the end users either through an IRS or via a UAV. Furthermore, an outage probability and its asymptotic behavior for the proposed system model are derived by considering the effect of both the signal coming via UAV as well as IRS elements in Nakagami-$m$ fading environments. The findings demonstrate that the simulation results are aligned with our theoretical derivation, and it is also compared with the existing state-of-the-art.

In this letter, channel estimation for massive multiple-input multiple-output (mMIMO) is performed by using binary zero correlation zone (ZCZ) sequences having a length in the form of a non-power of two (2(n+k+1 )+ 2(n+k-1)). The sequences are constructed using generalized Boolean functions (GBFs) that do not depend upon pre-existing sequences such as Hadamard sequences, complementary sequences, and complementary sets and optimally satisfy the Tang-Fan-Matsufuji bound on ZCZ sequences. The performance of MIMO channel estimation indicates that the proposed ZCZ sequences outperform those of using the existing sequences.

In this paper, a reconfigurable intelligent surface (RIS)-assisted full duplex communication for quadrature channel modulation (QCM) is proposed. The proposed scheme exploits transmit antennas and radio frequency mirror selection to transmit the real and imaginary parts of a QCM symbol independently to improve spectral efficiency. A dual polarized RIS is also employed to extend the indexing of receive antennas at a dual polarized half duplex receiver to separate the real and imaginary parts of the QCM symbol transmitted during downlink communication. This setup facilitates quadrature spatial modulation at the half duplex receiver and further enhances spectral efficiency. After transmit antenna selection, the remaining silent antennas at the base station are further harnessed to receive the QCM modulated uplink symbol from the half duplex transmitter unit. A decision-making method is adopted at the base station to find these silent antennas that receive uplink signals. Each antenna functions as either a transmit or a receive antenna with the help of a duplexer switch which assists in full duplex communication with minimal self-interference. Finally, an enhanced greedy detector is proposed to detect the signal with lower computational complexity. The system performance is evaluated using measures like average bit error rate, throughput, ergodic capacity, and energy efficiency, and compared with that of the existing state-of-the-art techniques.

In this paper, we propose two novel constructions of two-dimensional (2-D) Golay-Zero Correlation Zone (ZCZ) array sets based on extended generalized Boolean functions (EGBFs). The first construction yields an array set with parameters: set size $N$, array size $b^{n} \times b^{m}$, and a ZCZ width of $\left(b^{n}-1\right) \times\left(b^{\pi(2)-1}(b-1)+b^{\pi(3)-1}(b-2)\right)$. The second construction generalizes this further to obtain a set size of $N^{k}$, maintaining the same array size $b^{n} \times b^{m}$, with a corresponding $\mathbf{Z C Z}$ width of $b^{n}-1$ and $b^{\pi_{1}(2)-1}(b-1)+b^{\pi_{1}(3)-1}(b-2)$, where $b \geq 2, k<n, m$. 2-D Golay-ZCZ array sets are of growing interest due to their applications in MIMO omnidirectional precoding, sound source arrays, phased array antennas, and various wireless communication systems, including OFDM and OTFS systems. Moreover, the construction of binary Golay-ZCZ array sets is a special case of the proposed construction and contributes to the ongoing development of efficient sequence design for interference-free communication and efficient signal processing.

Golay sequences with the zero correlation zone (ZCZ), known as Golay-ZCZ sequences, play a pivotal role in reducing intersymbol interference (ISI) during the process of channel estimation in one dimension. Two-dimensional (2-D) Golay complementary array set (GCAS) within their ZCZ has the potential application in multiple input multiple output (MIMO) omnidirectional transmission. In this letter, 2-D Golay-ZCZ array set is constructed by using generalized Boolean function (GBF) without utilizing any kernels. The proposed construction provides 2-D Golay-ZCZ array set with various array sizes and large ZCZ sizes. Also, we get the one dimensional (1-D) Golay- ZCZ sequence set as a special case of the proposed construction.

In this letter, we propose a delay-Doppler domain pilot structure for an orthogonal time frequency space-integrated sensing and communication (OTFS-ISAC) system that addresses the issue of high peak-to-average power ratio in the existing structures. Then, we propose a norm-zero-modified generalized maximum Versoria criterion (l0-MGMVC)-based channel estimator at the communication receiver that is robust under fractional channel Doppler and ghost paths. At the sensing receiver, we propose a mean squared error-based target range and speed estimator. The derived analytical results and simulations indicate that the proposed pilot structure, channel estimator, and sensing parameter estimator outperform the existing state-of-the-art schemes.

Orthogonal Frequency Division Multiplexing (OFDM) is widely employed for multi-carrier communication systems due to its spectral efficiency and resilience to multi-path fading. However, the high peak-to-mean envelope power ratio (PMEPR) remains of significant challenge in OFDM. Golay complementary sets (GCS) have been used to mitigate this issue, but their applicability is limited due to strict constraints on sequence lengths and flock size. To address this limitation, Xin et al. (2004) introduced the concept of multiple-shift complementary sets (MSCS), which extend the GCS framework. An MSCS consists of sequences whose summed aperiodic autocorrelation function (AACF) vanishes at specific integer multiples of a given time shift. In this paper, we propose a new construction of a binary 4 -shift complementary sequence of length $2^{m-1}+4$, where $m \geq 3$, using generalized Boolean functions (GBF). Furthermore, the PMEPR of the resulting sequence is bounded above by 4.

Golay complementary set (GCS) and its extension, the mutually orthogonal Golay complementary set (MOGCS), have important applications in modern wireless communication. Historically, GCS was used in multi-carrier systems such as orthogonal frequency division multiplexing (OFDM) to reduce the peak-to-mean envelope power ratio (PMEPR) of the unencoded signal via encoding. In contrast, the MOGCS has been used in multi-access systems to support a large pool of users in a cell in spread-spectrum communication. The recent decade has seen significant advancement in this area regarding the flexibility of the sequence parameters, such as set size, flock size, alphabet size, etc. Apart from that, many substitutes of GCSs and MOGCSs have also been introduced to serve extensive application requirements. Quite a few times, the constructions of these sequence sets are based on an existing MOGCS. Hence, MOGCS has theoretical importance in the sequence design area, apart from its applied significance. However, it is still an open problem to construct a complete MOGCS with respect to the theoretical set size bound, having flexible parameters. In this paper, we search for MOGCS with as much flexibility as possible in the parameter choices.

In this letter, we present automated modulation classification (AMC) for reconfigurable intelligent surface (RIS)-assisted multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems under imperfect channel state information (CSI), residual carrier frequency offset (CFO) and symbol time offset (STO) errors. We leverage a triple attention-aided vision transformer (TrpViT) architecture, which uses a vision-centric approach within the transformer network to enhance global information acquisition. The TrpViT is implemented by utilizing three complementary attention mechanisms spatial, dilated, and channel attention in a unique attention block. This unique attention block extracts spatially local features while expanding the scope to capture more comprehensive signal features. The adopted attention mechanisms effectively capture long-range spatial dependencies and channel interactions within input signals by optimizing the model complexity. The performance of the proposed method is compared against existing models and it has been demonstrated that the proposed method accurately classifies higher modulation schemes for RIS-assisted MIMO-OFDM systems. The computational complexity of the proposed model is also compared with the existing state-of-the-art.

Frequency synchronization is essential to achieving the intended performance for single and multicarrier wireless systems. Blind techniques, which don't require prior channel knowledge or pilot symbols, are crucial for dynamic environments where self-adaptive synchronization is needed. A key objective of this paper is to provide the readers as well as the industry's professionals with a comprehensive understanding of the carrier frequency offset (CFO) problem in multicarrier communication systems like orthogonal frequency division multiplexing (OFDM), single carrier-frequency division multiple access (SC-FDMA), multiple input multiple output (MIMO)-OFDM, and MIMO-SC-FDMA. These waveforms are used in today's and future wireless communication systems such as wireless-fidelity (Wi-Fi), fifth-generation, and sixth-generation. Moreover, this paper also develops a taxonomy of the available solutions to address the CFO issue. We study blind techniques for CFO estimation presented in the recent literature and give potential future directions. We summarize various statistical methods and deep learning algorithms for CFO estimation and emphasize their advantages and limitations. We also incorporate the CFO impact on next-generation wireless systems such as orthogonal time frequency space and reconfigurable intelligent surface-assisted communication systems and provide a broader and deeper knowledge of the area. We provide simulation results of some existing estimators and their performance comparison in terms of mean square error for better understanding. Therefore, this paper is perfectly adapted to provide a comprehensive information source on blind CFO estimation techniques.

This letter presents a deep learning (DL) supervised model of estimating carrier frequency offset (CFO) for reconfigurable intelligent surfaces (RIS)-assisted multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems without the channel state information. The proposed architecture consists of the convolution neural network (CNN) with enhanced residual (Res), attention and dense gated linear unit (GLU) blocks, collectively referred to as CNN-RAGNet architecture. The integration of enhanced Res block facilitates feature extraction from various received antenna samples and mitigates the vanishing gradient problem. The attention and D-GLU blocks are incorporated into the model to prioritize relevant features and enhance the CFO estimation accuracy. Furthermore, the proposed architecture is adaptable to various modulation schemes and RIS elements, and works on the realistic 3GPP TR38.901 tapped delay line channel model. The simulation results indicate its outperformance over existing statistical based methods and DL based approaches. The proposed architecture has lower computational complexity than the existing methods.

Although reconfigurable intelligent surfaces (RIS) represent a promising technology, the double fading effect remains a significant concern in RIS-assisted systems, as it can degrade overall system performance if the RIS is not optimally positioned. In this paper, we propose a new compensation technique to remove the double fading effect by using an active RIS system. First, the fading between base station (BS) and RIS is compensated at the active RIS by adjusting the amplification factor and phase of the active RIS elements. Then the resultant reflected signal from the active RIS, form of the source signal with residual error, experiences only single fading at the user equipment (UE). The residual error due to the channel fading compensation of the first link at the active RIS is characterized and collaborated with simulation results. The proposed method not only improves the bit error rate (BER) and outage probability (OP) performance but also enhances the physical layer security (PLS) in the presence of eavesdroppers (Eves). The PLS performance of the proposed system is analyzed in terms of secrecy capacity and secrecy outage probability (SOP). The performance of the proposed model is compared with existing state-of-the-arts.

Complete complementary codes (CCCs) are highly valuable in the fields of information security, radar and communication. The spectrally null constrained (SNC) problem arises in radar and modern communication systems due to the reservation or prohibition of specific spectrums from transmission. The literature on SNC-CCCs is somewhat limited in comparison to the literature on traditional CCCs. The main objective of this paper is to discover several configurations of SNC-CCCs that possess more flexibility in their parameters. The proposed construction utilises the existing CCCs and mutually orthogonal sequences and covers all lengths with the smallest alphabets {-1,0,1} . Further, SNC-CCC is extended to multiple SNC-CCCs with an inter-set zero cross-correlation zone (ZCCZ). We could control the cross-correlation magnitude outside the ZCCZ, through the proposed construction. Consequently, the resulting codes possess both aperiodic and periodic inter-set ZCCZ and feature a low magnitude of cross-correlation value outside the ZCCZ.

A pair of sequences is called a Golay complementary pair (GCP), when it has zero aperiodic auto-correlation function (AACF) sum at every non-zero time shift. Due to its low peak-to-mean envelope power ratio (PMEPR), which is bounded by 2, it is used in many wireless communication applications. In this paper, we present direct constructions of 4h-ary (h≥ 1) GCPs of lengths of the forms 3× 2^m and 11× 2^m , (m≥ 1) using multi-variable extended Boolean functions (EBFs). We have also provided the generating functions of the complementary mates for the proposed GCPs.