Integrated Sensing and Communication (ISAC) requires the development of a waveform capable of efficiently supporting both communication and sensing functionalities. This paper proposes a novel waveform that combines the benefits of both the orthogonal frequency division multiplexing (OFDM) and the Chirp waveforms to improve both the communication and sensing performance within an ISAC framework. Hence, a new architecture is proposed that utilizes the conventional communication framework while leveraging the parameters sensed at the receiver (Rx) for enhancing the communication performance. We demonstrate that the affine addition of OFDM and chirp signals results in a near constant-envelope OFDM waveform, which effectively reduces the peak-to-average power ratio (PAPR), a key limitation of traditional OFDM systems. Using the OFDM framework for sensing in the conventional fashion requires the allocation of some resources for sensing, which in turn reduces communication performance. As a remedy, the proposed affine amalgam facilitates sensing through the chirp waveform without consuming communication resources, thereby preserving communication efficiency. Furthermore, a novel technique of integrating the chirp signal into the OFDM framework at the slot-level is proposed to enhance the accuracy of range estimation. The results show that the OFDM signal incorporated with chirp has better autocorrelation properties, improved root mean square error (RMSE) of range and velocity, and lower PAPR. Finally, we characterize the trade-off between communications and sensing performance.

Reconfigurable intelligent surfaces (RISs) are eminently suitable for improving the reliability of wireless communications by jointly designing the active beamforming at the base station (BS) and the passive beamforming at the RIS. Therefore, the accuracy of channel estimation is crucial for RIS-aided systems. The challenge is that only the cascaded two-hop channel spanning from the user equipments (UEs) to the RIS and spanning from the RIS to the BS can be estimated, due to the lack of active radio frequency (RF) chains at RIS elements, which leads to high pilot overhead. In this paper, we propose a low-overhead linear minimum mean square error (LMMSE) channel estimation method by exploiting the spatial correlation of channel links, which strikes a trade-off between the pilot overhead and the channel estimation accuracy. Moreover, we calculate the theoretical normalized mean square error (MSE) for our channel estimation method. Finally, we verify numerically that the proposed LMMSE estimator has lower MSE than the state-of-the-art (SoA) grouping based estimators.

Reconfigurable holographic surfaces (RHSs) constitute a promising technique of supporting energy-efficient communications. In this paper, we formulate the energy efficiency maximization problem of the switch-controlled RHS-aided beamforming architecture by alternately optimizing the holographic beamformer at the RHS, the digital beamformer, the total transmit power and the power sharing ratio of each user. Specifically, to deal with this challenging non-convex optimization problem, we decouple it into three sub-problems. Firstly, the coefficients of RHS elements responsible for the holographic beamformer are optimized to maximize the sum of the eigen-channel gains of all users by our proposed low-complexity eigen-decomposition (ED) method. Then, the digital beamformer is designed by the singular value decomposition (SVD) method to support multi-user information transfer. Finally, the total transmit power and the power sharing ratio are alternately optimized, while considering the effect of transceiver hardware impairments (HWI). We theoretically derive the spectral efficiency and energy efficiency performance upper bound for the RHS-based beamforming architectures in the presence of HWIs. Our simulation results show that the switch-controlled RHS-aided beamforming architecture achieves higher energy efficiency than the conventional fully digital beamformer and the hybrid beamformer based on phase shift arrays (PSA). Moreover, considering the effect of HWI in the beamforming design can bring about further energy efficiency enhancements.

Reconfigurable intelligent surfaces (RIS) constitute a promising technology for future wireless communications in terms of improving the spectral-efficiency and energy-efficiency.In this context, a novel RIS structure, which we refer to as coordinated RIS architecture, is formulated, where different RIS elements can be connected by configurable impedances to eliminate the channel fading.In the proposed RIS architecture, both the RIS element connection pattern and the configurable impedances can be optimized, based on the channel state information (CSI).The proposed architecture exhibits higher optimization flexibility than the state-of-the-art single-connected RIS architecture and group-connected RIS architecture, where only the configurable impedances can be optimized.Specifically, when considering base stations (BS) having a single antenna, the maximal ratio combining (MRC) criterion may be harnessed for designing the RIS element connection pattern, while in the case of multiple BS antennas, the alternating optimization algorithm may be employed for iteratively optimizing the BS's active beamforming vector and the RIS's passive beamforming matrix.Our numerical results show that the proposed coordinated RIS architecture achieves higher power gain than the group-connected RIS architecture having the same number of configurable impedances.Furthermore, the power gain in our proposed RIS architecture tends to that of the fullyconnected architecture upon increasing the number of RIS elements, while requiring significantly fewer configurable impedances.

Deploying reconfigurable intelligent surfaces (RIS) is promising for enhancing the transmission reliability of wireless communications by controlling the wireless environment, in which the active beamforming at the base station and the passive beamforming at the RIS are jointly designed based on the acquisition of channel state information. Hence, channel estimation is crucial for RIS-aided systems. Due to the lack of active radio frequency chains at the RIS to process and transmit pilot sequences, only the cascaded twin-hop transmitter-RISreceiver channel can be estimated, which results in extremely high pilot overhead, when a large number of RIS reflecting elements is used. As a remedy, we propose a channel estimation method relying on low pilot overhead, namely the KarhunenLoeve transformation based linear minimal mean square error ` (KL-LMMSE) estimator. This exploits the spatial correlation of the RIS-cascaded channels, for our multi-cell multiple-input and multiple-output RIS-aided systems. Furthermore, we extend our investigations to the effects of realistic phase quantization errors. Additionally, we derive the theoretical mean square error (MSE) of our proposed channel estimators verified by numerical simulations, and compare the results to various benchmark schemes. We show that the MSE performance of our proposed KL-LMMSE estimator is better than that of the state-of-the-art low-overhead channel estimators.

An intelligent omni-surface (IOS) assisted holographic multiple-input and multiple-output architecture is conceived for $360^\circ$ full-space coverage at a low energy consumption. The theoretical ergodic rate lower bound of our non-orthogonal multiple access (NOMA) scheme is derived based on the moment matching approximation method, while considering the signal distortion at transceivers imposed by hardware impairments (HWIs). Furthermore, the asymptotically ergodic rate lower bound is derived both for an infinite number of IOS elements and for continuous aperture surfaces. Both the theoretical analysis and the simulation results show that the achievable rate of the NOMA scheme is higher than that of its orthogonal multiple access counterpart. Furthermore, owing to the HWIs at the transceivers, the achievable rate saturates at high signal-to-noise ratio region, instead of reaching its theoretical maximum.

New reconfigurable intelligent surface (RIS) based amplitude and phase modulation schemes are proposed as an evolution how the phase-only modulation schemes available in the literature. Explicitly, both the amplitude-phase shift keying (A-PSK) and quadrature amplitude-phase shift keying (QA-PSK) are conceived, where the RIS is assumed to be part of a transmitter to deliver information to the multi-antenna aided downlink receiver. In the proposed design, the RIS is partitioned into multiple blocks, and the information bits are conveyed by controlling both the ON-OFF state and the phase shift of the RIS elements in each block. Since the propagation paths spanning from each RIS block to the receiver can be coherently combined as a benefit of appropriately configuring the phase of the RIS elements, the received signal constellations can be designed by controlling both the ON-OFF pattern of the RIS blocks as well as the phase shift of the RIS elements. Both the theoretical analysis and the simulation results show that our proposed RIS-aided modulation schemes outperform the state-of-the-art RIS-based PSK modulation both in terms of its discrete-input-continuous-output memoryless channel (DCMC) capacity and its symbol error probability, especially in the high signal-to-noise-ratio (SNR) region, when considering realistic finite resolution RIS phase shifts.

The linear minimal mean square error estimator for active reconfigurable intelligent surface (RIS)-aided wireless systems is formulated. Furthermore, based on the moment-matching method, we employ the Gamma distribution to approximate the distribution of the instantaneous received signal-to-interference-plus-noise ratio, and then derive the closed-form outage probability and ergodic channel capacity in the presence of realistic channel estimation errors, the thermal noise of RIS amplifiers and the RIS phase shift noise. Our theoretical analysis and simulation results show that the introduction of RIS amplifiers is equivalent to increasing of the transmit power, and also present the performance degradation resulting from the channel estimation error and the RIS phase noise.

Reconfigurable intelligent surfaces (RIS) are capable of beneficially ameliorating the propagation environment by appropriately controlling the passive reflecting elements. To extend the coverage area, the concept of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) has been proposed, yielding supporting 360° coverage user equipment (UE) located on both sides of the RIS. In this paper, we theoretically formulate the ergodic sum-rate of the STAR-RIS assisted non-orthogonal multiple access (NOMA) uplink in the face of channel estimation errors and hardware impairments (HWI). Specifically, the STAR-RIS phase shift is configured based on the statistical channel state information (CSI), followed by linear minimum mean square error (LMMSE) channel estimation of the equivalent channel spanning from the UEs to the access point (AP). Afterwards, successive interference cancellation (SIC) is employed at the AP using the estimated instantaneous CSI, and we derive the theoretical ergodic sum-rate upper bound for both perfect and imperfect SIC decoding algorithm. The theoretical analysis and the simulation results show that both the channel estimation and the ergodic sum-rate have performance floor at high transmit power region caused by transceiver hardware impairments.

Reconfigurable intelligent surfaces (RISs) are capable of enhancing the wireless propagation environment of the future Internet of Things (IoT). Recently, they have also been configured as a transmitter to realize information modulation at low hardware complexity. In this article, we conceive a transmitter relying on a single radio frequency (RF) chain for low-complexity RIS-aided multiuser downlink communication. More explicitly, in the proposed architecture, the multiuser information is transmit precoded and modulated at the RIS by appropriately configuring the phase shift and amplitude of each RIS element. We assume that the distribution of multiple users obeys on a Poisson point process (PPP), where we jointly optimize the total power reflected from the RIS and the power allocation fraction assigned to each user, under the practical constraint of a realistic amplitude limitation of each RIS element. Additionally, we theoretically analyze the ergodic rate, symbol error probability, outage probability, and coverage range of the proposed RIS-aided single-RF downlink and confirm the accuracy of our analysis by simulations. Finally, we compare its performance to that of the conventional multiple-input-multiple-output (MIMO) systems employing multiple RF-chains.

With massive deployment, multiple-input–multiple-output (MIMO) systems continue to take mobile communications to new heights, but the ever-increasing demands mean that there is a need to look beyond MIMO and pursue the next disruptive wireless technologies. Reconfigurable intelligent surface (RIS) is widely considered a key candidate technology block to provide the next generational leap. The first part of this article provides an updated overview of the conventional reflection-based RIS technology, which complements the existing literature to include active and semiactive RIS, and the synergies with cell-free massive MIMO (CF mMIMO). Then, we widen the scope to discuss the surface-wave-assisted RIS that represents a different design dimension in utilizing metasurface technologies. This goes beyond being a passive reflector and can use the surface as an intelligent propagation medium for superb radio propagation efficiency. The third part of this article turns the attention to the fluid antenna, a novel antenna technology that enables a diverse form of reconfigurability that can combine with RIS for ultrahigh capacity, power efficiency, and scalability. This article concludes with a discussion of the potential synergies that can be exploited between MIMO, RIS, and fluid antennas.

Reconfigurable intelligent surfaces (RIS) have been actively researched as a potential technique for future wireless communications, which intelligently ameliorate the signal propagation environment. In the conventional design, each RIS element configures and reflects its received signal independently of all other RIS elements, which results in a diagonal phase shift matrix. By contrast, we propose a novel RIS architecture, where the incident signal impinging on one element can be reflected from another element after an appropriate phase shift adjustment, which increases the flexibility in the design of RIS phase shifts, hence, potentially improving the system performance. The resultant RIS phase shift matrix also has off-diagonal elements, as opposed to the pure diagonal structure of the conventional design. Compared to the state-of-art fully-connected/group-connected RIS structures, our proposed RIS architecture has lower complexity, while attaining a higher channel gain than the group-connected RIS structure, and approaching that of the fully-connected RIS structure. We formulate and solve the problem of maximizing the achievable rate of our proposed RIS architecture by jointly optimizing the transmit beamforming and the non-diagonal phase shift matrix based on alternating optimization and semi-define relaxation (SDR) methods. Moreover, the closed-form expressions of the channel gain, the outage probability and bit error ratio (BER) are derived. Simulation results demonstrate that our proposed RIS architecture results in an improved performance in terms of the achievable rate compared to the conventional architecture, both in single-user as well as in multi-user scenarios.

Reconfigurable Intelligent Surface (RIS) consists of mostly-passive elements capable of electronically steering the impinging signal with configured phase shifts. However, achieving infinite phase resolution is infeasible and the phases needs to be quantized for practical implementation. In this paper, we propose an unsupervised learning-based method to estimate the optimal discrete RIS phase configuration by utilizing key eigen vectors of the available channel state information (CSI) as the input. The direct link between a base station (BS) and user equipment (UE) is not considered in the proposed method and only the CSI of the RIS-aided link is utilized. Further, the proposed network design is independent of the number of transmit and receive antennas at the BS and UE respectively, making the solution generalized and adaptable towards different BSs and UEs in the communication. Due to the unsupervised nature of the proposed method, the solution does not rely on any labeled data, obtaining which is otherwise an exhaustive process. The performance of the proposed solution is analyzed through rigorous simulations and shows the effectiveness of directly estimating discrete RIS configurations as compared to obtaining infinite resolution phase-shifts and then quantizing them. The performance analysis further demonstrates the efficacy of the proposed system with reduced computational complexity and generalized solution with respect to number of BS and UE antennas.

Intelligent re-configurable surfaces (IRS) constitute electronically steerable structures that can be used to control the propagation of electromagnetic (EM) waves. Owing to their passive nature in terms of signal reflection, IRS require large-scale implementations. This means that the near-field effect, e.g., the curvature of the propagating EM wave must be considered. In this paper, a simple two-step near-field beamforming method is proposed. In the first step, the direction from the IRS center point to the user equipment (UE) is found assuming plane wave model. In the second step, the transmitted beam is focused towards the UE based on the pre-calculated constant phase contours. All operations in the proposed method can be implemented as memory operations, i.e., no complex computations are needed. The operation of the method is verified with simulations. The results show that the proposed beamforming technique is capable of focusing the beam pattern of the IRS towards the user. More specifically, the near-field focusing of a (50 ×50)-element IRS operating at 5 GHz frequency increases the received power by 3 dB when compared to a case where the near-field effect is ignored.

This paper studies the application of tools from Artificial Intelligence and Machine Learning (AI/ML) for the adaptive configuration of reference signals (pilots). Specifically, we propose a deep learningbased framework to infer on the configuration of userspecific demodulation reference signals (DMRS) that are used for composite channel estimation (CCE) in multiple-input multiple-output (MIMO) sy...

Reconfigurable intelligent surfaces (RIS) have the ability to steer the electromagnetic (EM) waves to a desired direction. This enables the improvement of the wireless link performance by allowing the illumination of receivers otherwise shadowed by buildings or hills. In this paper, a standards-compliant link-level simulator is developed to study the performance improvement offered by a RIS in 5G New Radio (NR) uplink operating at sub-6 GHz bands. At these frequencies the direct channel between the user and base station is rarely completely blocked, but given the stringent power restrictions of devices, the RIS is utilized for enhancing the coverage performance in the uplink direction. In the studied cases, the transmitter (TX) is close to the RIS and a line-of-sight (LoS) path between the TX and RIS is assumed. The channel between the TX and receiver (RX) is modeled as a non-line-of-sight (NLoS) channel with 5G NR clustered delay line A (CDL-A) profile. Both LoS and NLoS channels between the RIS and RX are considered. Under state-of-the-art system settings, the RIS is shown to increase the symbol error rate link performance by 6 dB. When the performance is measured with coded bit error rate, the performance gain in simulated cases is 1 dB. The coverage enhancement is measured with the throughput as a function of the distance between the TX and RX. The distance at which the maximum possible throughput can be achieved is increased about 5%. The coverage can be further extended if a lower than the maximum throughput is accepted.

Reconfigurable intelligent surface (RIS) is a programmable structure that can be used to control the propagation of electromagnetic waves by changing the electric and magnetic properties of the surface. By placing these surfaces in an environment, the properties of radio channels can be controlled. This opens up new opportunities to improve the performance of wireless systems. In this paper, the basic operation of antenna array and metasurface based RIS is described. While the current long term (6G) research on RIS often prioritizes very high frequencies from tens to hundreds of GHz, this paper puts emphasis rather on operating frequencies below 10 GHz which promise a much faster to market track for RIS applications. For this purpose, review of the literature on the use of RIS in wireless communication applications operating below 10 GHz frequency band is provided.

The 5G Edge Intelligence for Vertical Experimentation (5G-DIVE) project aims at demonstrating the technical merits and business value proposition of 5G technologies in two vertical pilots, namely the Industry 4.0 (I4.0) and Autonomous Drones Scout (ADS) pilots. This paper presents an overview of the overall 5G-DIVE solution and reports the results of the initial validation campaign of the selected use case, featuring 5G connectivity, distributed Edge computing, and artificial intelligence. The initial results for the I4.0 provide a baseline for next step validation campaign targeting a broader scale 5G implementation, while the ADS results provides promising results for enhancing the autonomous navigation in real-time.

Generalized index modulation (GIM) which implicitly conveys information by the activated indices is a promising technique for next-generation wireless networks. Due to the prohibitive challenge of bit-to-index combination (IC) mapping optimization, conventional GIM system obtains the bit-to-IC mapping table randomly, which may suffer from some performance loss. To circumvent this issue, we propose a low-complexity graph theory assisted bit-to-IC gray coding for GIM systems by minimizing the average hamming distance (HD) between any two ICs having one different value. Specifically, we decompose and transform the optimization problem into two subproblems using the graph theory, i.e., 1) Select an IC set whose corresponding graph has the minimum degree; 2) Design a bit-to-IC mapping principle to minimize the weight of the selected graph. Low-complexity algorithms are developed to solve the subproblems with a significant reduced complexity. Both simulation and theoretical results are shown that the GIM systems with our proposed mapping table are capable of providing significant performance gains over the conventional counterparts without the need for any additional feedback-link and without extra computational complexity. It is also shown that the proposed bit-to-IC mapping table is straightforward for any GIM systems over generalized fading channels.

In this treatise, we introduce a novel polarization modulation (PM) scheme, where we capitalize on the reconfigurable polarization antenna design for exploring the polarization domain degrees of freedom, thus boosting the system throughput. More specifically, we invoke the inherent properties of a dual polarized (DP) antenna for transmitting additional information carried by the axial ratio (AR) and tilt angle of elliptic polarization, in addition to the information streams transmitted over its vertical (V) and horizontal (H) components. Furthermore, we propose a special algorithm for generating an improved PM constellation tailored especially for wireless PM modulation. We also provide an analytical framework to compute the average bit error rate (ABER) of the PM system. Furthermore, we characterize both the discrete-input continuous-output memoryless channel (DCMC) capacity and the continuous-input continuous-output memoryless channel (CCMC) capacity as well as the upper and lower bounds of the CCMC capacity. The results show the superiority of our proposed PM system over conventional modulation schemes in terms of both higher throughput and lower BER. In particular, our simulation results indicate that the gain achieved by the proposed Q-dimensional PM scheme spans between 10dB and 20dB compared to the conventional modulation. It is also demonstrated that the PM system attains between 54% and 87.5% improvements in terms of ergodic capacity. Furthermore, we show that this technique can be applied to MIMO systems in a synergistic manner in order to achieve the target data rate target for 5G wireless systems with much less system resources (in terms of bandwidth and the number of antennas) compared to existing MIMO techniques.