This paper presents the first bit error rate (BER) analysis of a pinching-antenna (PA)-based non-orthogonal multiple access (NOMA) communication system. The PA is assumed to be able to be placed anywhere along the waveguide and serves two NOMA user equipment (UEs) in both uplink (UL) and downlink (DL) scenarios. Exact closed-form expressions for the average BER of each user are derived under practical imperfect successive interference cancellation (SIC). These expressions are then used to optimize the PA location for minimizing the overall average BER of both UEs. In the UL case, the interference between the users’ channels introduces phase-dependent fluctuations in the BER cost function, making it highly non-convex with many local extrema. To address this challenge, a smoothing technique is applied to extract the lower envelope of the BER function, effectively suppressing ripples and enabling a reliable identification of the global minimum. In the DL case, a joint optimization of the PA location and NOMA power allocation coefficients is proposed to minimize the average BER. Simulation results verify the accuracy of the analytical derivations and the effectiveness of the proposed optimization methods. Notably, the UL results demonstrate that an optimally positioned PA can create the required received power difference between two equally powered UEs for reliable power-domain NOMA decoding under imperfect SIC.

Non-orthogonal multiple access (NOMA) is widely recognized for enhancing the energy and spectral efficiency through effective radio resource sharing. However, uplink NOMA systems face greater challenges than their downlink counterparts, as their bit error rate (BER) performance is hindered by an inherent error floor due to error propagation caused by imperfect successive interference cancellation (SIC). This paper investigates the BER performance improvements enabled by reconfigurable intelligent surfaces (RISs) in multi-user uplink NOMA transmission. Specifically, we propose a novel RIS-assisted uplink NOMA design, where the RIS phase shifts are optimized to enhance the received signal amplitudes while mitigating the phase rotations induced by the channel. To achieve this, we first develop an accurate channel model for the effective user channels, which facilitates our BER analysis. We then introduce a channel alignment scheme for a two-user scenario, enabling efficient SIC-based detection and deriving closed-form BER expressions. We further extend the analysis to a generalized setup with an arbitrary number of users and modulation orders for quadrature amplitude modulation signaling. The analysis is also extended to consider imperfect channel state information (CSI) knowledge and the multi-antenna base station (BS) cases. Using the derived BER expressions, we develop an optimized uplink NOMA power allocation (PA) scheme to minimize the average BER while satisfying the user transmit power constraints. It will be shown that the proposed NOMA detection scheme, in conjunction with the optimized PA strategy, eliminate SIC error floors at the base station. The theoretical BER expressions are validated using simulations, which confirms the effectiveness of the proposed design in eliminating BER floors.

This paper investigates the symbol error rate (SER) performance of the pilot-aided simultaneous communication and localisation (PASCAL) system with a ground-to-air model. A scenario where multiple drones transmit communication signals to a base station (BS), which needs to simultaneously decode the signals and continuously locate the drones’ positions during the communication session, is considered. The BS operates in two stages: first, it estimates the drones’ location parameters using pilot signals; second, it performs data detection by reconstructing the channel response based on the estimated location parameters. The theoretical analysis presented in this paper demonstrates that the distributions of the estimated location parameters follow Gaussian distributions of which the mean values are equal to the actual values of the locations, and their variances are determined by the achievable mean square error of the estimator. Using these distributions, the average SER is derived to quantify the impact of localisation errors on decoding performance. This analysis highlights the synergy between communication and localisation, providing valuable insights into the influence of localisation inaccuracies on the performance of location-aware communication systems. Simulations are conducted to validate the theoretical derivations.

Non-orthogonal multiple access (NOMA) offers enhanced energy and spectral efficiency through effective resource sharing, yet uplink NOMA suffers from bit error rate (BER) degradation due to error propagation from imperfect successive interference cancellation (SIC). This paper investigates the potential BER performance enhancement via reconfigurable intelligent surfaces (RISs) in uplink NOMA systems. A novel RIS-assisted design is proposed, wherein the RIS phase shifts are optimized to amplify the received signals and mitigate channel-induced phase distortions. An accurate effective statistical channel modeling is developed to facilitate our closed-form BER analysis, supported by a two-user channel alignment scheme for efficient SIC detection. Based on the derived BER expressions, an optimized power allocation (PA) strategy is formulated to minimize the average BER under transmit power constraints. Simulation results validate the theoretical analysis, demonstrating that the proposed PA scheme effectively eliminates the BER floors associated with uplink NOMA at the base station.

We consider a reconfigurable intelligent surface (RIS) that can implement a phase rotation continuously over the whole surface rather than via a finite number of discrete elements. Such an RIS can be considered a design for future systems where advances in metamaterials make such an implementation feasible or as the limiting case where the number of elements in a traditional RIS increases in a given area. We derive the optimal RIS design for the single-user (SU) scenario assuming a line-ofsight (LoS) from the RIS to the base station (BS) and correlated Rayleigh fading for the other links. We also derive the associated optimal signal-to-noise ratio (SNR) and its mean, a bound on the mean spectral efficiency (SE), an approximation to the SNR outage probability and an approximation to the coefficient of variation for the investigation of channel hardening.

This work investigates the performance of intelligent reflective surfaces (IRSs) assisted uplink nonorthogonal multiple access (NOMA) in energy-constrained networks. Specifically, we formulate and solve two optimization problems; the first aims at minimizing the sum of users’ transmit power, while the second targets maximizing the system-level energy efficiency (EE). The two problems are solved by jointly optimizing the users’ transmit powers and the beamforming coefficients at the IRS, subject to the users’ individual uplink rate and transmit power constraints. A novel and low-complexity algorithm is developed to optimize the IRS beamforming coefficients by optimizing the objective function over the complex circle manifold (CCM). To efficiently optimize the IRS phase shifts over the manifold, the optimization problem is reformulated into a feasibility expansion problem which is reduced to a max-min signal-to-interference-plus-noise ratio (SINR). Then, with the aid of a smoothing technique, the exact penalty method is applied to transform the problem from constrained to unconstrained. The proposed solution is compared against three semi-definite programming (SDP)-based benchmarks which are semi-definite relaxation (SDR), SDP-difference of convex (SDP-DC) and sequential rank-one constraint relaxation (SROCR). The results show that the manifold algorithm provides better performance than the SDP-based benchmarks, and at a much lower computational complexity for both the transmit power minimization and EE maximization problems. The results also reveal that IRS-NOMA is only superior to orthogonal multiple access (OMA) when the users’ target achievable rate requirements are relatively high.

In this paper, we present an intelligent reflective surface (IRS)-assisted physical layer network coding (PNC) system in a two-way relaying channel (TWRC). Specifically, IRS is used to align the effective channels of the two received superimposed signals at the relay, which allows canceling the carrier phase offset (CPO) between the two received signals. The IRS phase shifts are optimized to maximize the received PNC signal amplitude while having a zero CPO constraint. An efficient manifold optimization-based approach is proposed to solve this problem, where the optimization is performed on the complex circle manifold. Moreover, we improve the performance of channel-coded IRS-assisted PNC by introducing the weighted non-binary PNC (WN-PNC) scheme, where the binary data are mapped to, and encoded over, Galois Fields (GFs). We present two WN-PNC cases where the data is encoded over GF(4) and GF(8), then modulated using quadrature phase shift keying (QPSK) and 8-quadrature amplitude modulation (8-QAM), respectively. We also design proper PNC mapping functions for both cases, ensuring that no PNC ambiguity can occur at the relay. Our simulation results show the efficacy of the proposed manifold optimization-based approach and the error performance improvement of the WN-PNC over the binary PNC case.

This paper presents a novel approach for power-constrained internet of things (IoT) networks that employ non-orthogonal multiple access (NOMA) and are assisted by an intelligent reflecting surface (IRS) for uplink transmissions. The main objective of this work is to maximize the sum rate of power-constrained IoT networks by jointly designing the IRS phase shifts and the users’ transmit power allocation. The proposed solution optimizes the power allocation and phase shifts alternatively. We devise a novel approach to optimize the IRS phase shifts that is based on manifold optimization techniques. Specifically, the IRS phase shifts optimization problem is formulated and solved over the complex circle manifold. Our results show that the proposed method outperforms the widely used semi-definite relaxation (SDR) technique as higher sum rates with less power consumption can be achieved.

In this paper, we consider the problem of channel feedback in massive multiple-input-multiple-output (MIMO) systems. For the downlink scenario, we present a detailed comparison between the performance of the quantized and the analog channel feedback schemes for the case of having multiple antenna users. Both schemes’ performance is evaluated by deriving an upper bound on the rate gap between the rate of the system with perfect channel state information (CSI) and with imperfect CSI for both feedback schemes. We compare the two schemes, namely, quantized channel feedback and analog channel feedback, under the same resources allocated for channel feedback for a fair comparison. Moreover, we consider two different downlink transmission schemes; the first one does not consider power allocation across the streams and the second one does power allocation (water-filling) across the streams. Our results show that the analog feedback scheme performs better in the low signal to noise (SNR) region when performing power allocation across the multiple data streams. However, the quantized channel feedback scheme performs better at the high SNR region, where the quantized CSI can provide a better approximation of the actual CSI. Finally, simulation results are presented to verify our theoretical analysis and demonstrate our conclusions.