Reconfigurable intelligent surfaces (RIS) can reflect an incoming signal toward a target location to enhance the received signal quality. In most studies, it is assumed that the base station (BS) provides configuration data to an RIS controller. However, for a passive RIS with a high number of elements, this data transmission from the BS can introduce significant delays. This challenge is especially pronounced in vehicular communications, where reflection coefficients must be updated even for a few millimeters of movement. To overcome this limitation, we propose a self-tuning RIS controller that independently calculates the reflection coefficients with minimal input from the BS. Our RIS controller leverages the user’s location information to compute the reflection coefficients in three steps: channel gain calculation, steering vector calculation, and optimal phase control. A pipelined architecture is developed to support these steps in real-time. The proposed VLSI architecture is implemented on an Artix-7 XC7A100TCSG324-1 FPGA to calculate reflection coefficients for a vehicle moving at a 100 kmh speed.

Massive multiple-input multiple-output (MIMO) technology utilizes large antenna arrays at the base-station (BS) to support a large number of users with the same time/frequency resources. Uplink signal detection poses a significant challenge in massive MIMO systems. Although minimum-mean square-error based detectors become the mainstay of classical massive MIMO systems, they require a large-dimensional matrix inversion, which is computationally extensive. This paper proposes a new massive MIMO detector based on the two-parameter over relaxation (TOR) algorithm. The relaxation and acceleration parameters are carefully selected on the basis of the spectral radius to achieve a good balance between the performance and the complexity. Compared to existing linear detectors, the proposed TOR based detector is more stable because of its relaxation and acceleration parameters. The results show that the proposed massive MIMO detector achieves a remarkable performance gain and overall complexity reduction compared to existing detectors, particularly when the number of users is comparable to the number of BS antennas. The proposed TOR detector achieves better performance than the existing detectors when using the same number of iterations.

Integrated sensing and communications (ISAC) has emerged as a promising solution for addressing spectrum congestion in sixth-generation communication systems. In this work, we consider the deployment of ISAC in a full-duplex cell-free (FD-CF) multi-input multi-output (MIMO) system that is aided by a reconfigurable intelligent surface (RIS). To overcome the performance limitations of a single ISAC base station (BS), we consider multiple FD access points (APs) that simultaneously perform target detection and multi-user uplink (UL) communication, assisted by a RIS. We aim to maximize the weighted sum of the output radar and communication signal-to-interference-plus-noise ratios (SINRs) by jointly designing the radar and communication receive beamformers, UL transmission powers, joint downlink (DL) sensing beamformers, and RIS reflection coefficients. The total UL, DL power budgets, and the RIS phase shift unit-modulus constraints are considered to guarantee the balance between sensing and communication requirements. The resulting problem is non-convex and rather formidable to solve. Nonetheless, an efficient solution to this problem is developed based on alternating optimization, which utilizes majorization-minimization (MM), fractional programming (FP), and the penalty method. Simulations demonstrate the effectiveness of the proposed solution and the advantages of deploying RIS to assist integrated cooperative sensing and communication (ICSAC) in FD-CF MIMO systems.

The evolution of communication networks towards multi-functionality has paved the way for integrated sensing, communication, and power transfer (ISCAPT) systems, enabling efficient data transmission, environmental sensing, and wireless energy transfer. However, conventional ISCAPT architectures face inherent trade-offs between high-rate communication, precise sensing, and efficient energy transfer, exacerbated by interference and dynamic channel conditions. To address these limitations, rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS) are integrated into ISCAPT systems. Hence, in this paper, we consider active RIS-aided RSMA ISCAPT to maximize the sum communication rate while ensuring the sensing performance, harvesting adequate energy, and satisfying the transmit power budgets of the BS and RIS. To achieve this goal, the transmit beamforming matrix, RIS reflection matrix, power splitting factor, radar receive beamforming, and common rate allocation are jointly designed. However, the formulated maximization problem is challenging due to its non-convex nature and the coupling among the optimization variables. To efficiently tackle this issue, the formulated problem is decomposed into three sub-problems, which are reformulated into quadratic-constrained-quadratic programs (QCQPs). Then, an alternating optimization (AO)-aided majorization-minimization (MM) and successive convex approximation (SCA) algorithm is proposed to iteratively optimize these sub-problems. Simulation studies are conducted to demonstrate the effectiveness of the proposed framework and illustrate trade-offs compared to established benchmarks.

Ambient backscatter communication (AmBC) has attracted significant attention as a viable paradigm for energy efficient communication in the next generation of Internet-of-Things (IoT). Channel estimation, a vital task in AmBC receiver design, becomes more challenging when accounting for more practical scenarios such as frequency selective fading and the presence of hardware impairments such as I/Q imbalance. Full-duplex communication further complicates channel estimation, due to the presence of self-interference. In this work, we consider OFDM-based full-duplex AmBC systems affected by I/Q imbalance, and propose two methods for the joint estimation of the channel and I/Q imbalance. The first algorithm is a pilot-based estimator, while the second is a semi-blind estimator based on the concept of decision-directed (DD) estimation. In addition, we propose a novel design of appropriate pilot sequences to optimize the performance of the proposed methods. As benchmarks on estimation performance, we obtain analytical expressions for the pilot-based and semi-blind Cramer-Rao bounds (CRBs). Our simulations demonstrate that both proposed estimators converge to their respective bounds. The proposed methods offer different tradeoffs between accuracy and computational complexity, which are suitable for different use cases.

Ambient backscatter communication (AmBC) has attracted attention as an enabling technology for green device-to-device (D2D) communication for the next generation of Internet-of-Things (IoT) networks. Most existing works assume that the signals of the source and the backscattering device (BD) are perfectly synchronized at the reader. Perfect synchronization is not feasible in practice, and the timing offset between the two signals can significantly degrade the performance of the reader’s receiver. In this work, we consider an asynchronous AmBC system and solve the problem of joint timing offset and channel estimation at the reader. Assuming generic Nyquist pulse-shaping filters, we develop a pilot-based maximum likelihood (ML) estimator, as well as a semi-blind estimator using the expectation maximization (EM) algorithm. For the special case of the rectangular pulse, we also derive the corresponding ML and EM estimators, and an approximate low-complexity EM (LCEM) algorithm. As theoretical benchmarks, we obtain the Cramer-Rao-bound (CRB) for pilot-based estimation, while for semi-blind estimation the modified CRB (MCRB) is obtained. The performance of the proposed algorithms is investigated using simulations, showing that both the ML and EM approach their corresponding CRBs, and that the EM-type algorithms provide superior estimation and detection accuracy, at the expense of higher complexity.

Ambient backscatter communication (AmBC) is a highly promising technology that enables the ubiquitous deployment of low-cost, low-power devices to support the next generation of Internet-of-Things (IoT) applications. This paper addresses channel estimation for full-duplex multi-antenna AmBC systems. This is highly challenging due to the large number of channel parameters resulting from the use of multiple antennas, the presence of self-interference, and the dependence of the backscattering channel on the state of the backscattering device. Considering both pilot-based and semi-blind estimation strategies, we propose three solutions for this problem. The first is the pilot-based maximum-likelihood (ML) estimator. The second is a semi-blind estimator based on the expectation maximization (EM) framework, which provides higher accuracy than the ML, at the cost of higher computational complexity. The third is a semi-blind estimator based on the decision-directed (DD) strategy, which provides a tradeoff between the ML and the EM. Additionally, we derive the exact Cramer-Rao bound (CRB) for pilot-based estimation and the modified CRB for semi-blind estimation. Simulations show that the ML and the EM perform very close to their respective CRBs, and that the semi-blind estimators offer significantly higher estimation accuracy, as well as superior symbol-error-rate performance, compared to the ML estimator.

In this paper, a printed low‐profile antenna with frequency and pattern reconfigurable functionality is designed in three modes. Each mode operates at different frequency bands and has several options available for pattern reconfiguration in these bands. The proposed antenna consists of eight pin‐diode switches (S1 to S8). The switches S1 and S2, installed in the radiating patch, are used for frequency reconfigurability to control the operating bands of the antenna. The rest of the six switches (S3, S4, S5, S6, S7, and S8), loaded in the stubs on the rear side of the antenna, are used for pattern reconfiguration to control the main lobe beam steering. When all switches are off, the proposed antenna operates in a wideband mode, covering the 3.82‐9.32 GHz frequency range. When S1 is on, the antenna resonates in the 3.5 GHz (3.09‐4.17 GHz) band. When both S1 and S2 are on, the resonant band of the antenna is shifted to 2.5 GHz band (2.40‐2.81 GHz). A very good impedance matching with a return loss of less than ‐10 dB is attained in these bands. The beam steering is done at each operating frequency by controlling the on and off states of the six pin‐diode switches (S3, S4, S5, S6, S7, and S8). Depending on the state of the switches, the antenna can direct the beam in seven distinct directions at 4.2 GHz, 4.5 GHz, and 5 GHz. The main beam of the radiation pattern is steered in five different directions at 5.5 GHz, 3.5 GHz, and 2.6 GHz operating bands for the given state of the mentioned switches. The proposed antenna supports several sub‐6 GHz 5G bands (2.6 GHz, 3.5 GHz, 4.2 GHz, 4.5 GHz, and 5 GHz) and can be used in handheld 5G devices.

Massive multiple-input multiple-output (MIMO) systems have been proposed to meet the user demands in terms of performance and quality of service (QoS). Due to the large number of antennas, detectors in massive MIMO are playing a crucial role in guaranteeing a satisfactory performance, while their complexity is also being increased. This paper considers several approximate algorithms to avoid direct matrix inversion, namely the Neumann method, the Gauss-Seidel (GS) method, the successive over-relaxation (SOR) method, the Jacobi method, the Richardson method, the optimized coordinate descent (OCD), and the conjugate gradients (CG) method. Also, this paper presents a comparison among the approximate matrix inversion methods and the minimum mean square error (MMSE). Simulation of 16×128, and 16×32 MIMO systems shows that a detector based on the GS method outperforms other detectors when the ratio of base station (BS) antennas to user terminal antennas, β, is small. On the other hand, the detector based on the SOR method outperforms the other approximate matrix inversion methods when β is large. In addition, this paper studies and recommends the setting values of relaxation parameter (ω) in the SOR and Richardson methods. It also provides a comparison among the approximate matrix inversion methods in the number of multiplications. Simulation results show that the Neumann method, the OCD method, and the CG method achieve the lowest number of multiplications while the CG method outperforms the Neumann and the OCD methods. This paper also shows that not every iteration improves the performance.

This paper presents a multiuser detection technique using compressive sensing (CS) where the entries data entries of finite-alphabet signals are considered as sparse data. Thus the proposed system is underdetermined, which pave the way for the usage of lattice sphere decoding (LSD) technique. In order to demonstrate the effectiveness of the proposed method, it is tested at different sparsity levels. We show that the proposed method provides substantial performance gain over the previous work.