Signal‐to‐interference‐plus‐noise ratio estimation for wireless communication systems: Methods and analysis

The aim of this paper is to present a non‐linear statistical model to fit and forecast the signal‐to‐interference plus noise ratio (SINR) in two‐tier heterogeneous cellular networks which consist of macrocells and femtocells. Since in these networks the number and locations of femtocell base stations (FBS) are variable, SINR forecasting can be useful in some areas such as power control and handover management. So far, linear autoregressive (AR) models have commonly been used in forecasting the received signal strength (rss) in macrocellular networks. However, AR modelling results in high mean square error (MSE) when data are non‐linear. This paper focuses on SINR which takes into account signal strength, interference and noise effects. Moreover, macro‐femto cellular network is considered. The F ‐test results show that the SINR data are non‐linear, leading to use non‐linear models instead of AR model. A non‐linear logistic smooth threshold AR (LSTAR) model is utilised to model and forecast the SINR data. Kolmogorov–Smirnov (K‐S) test demonstrates that LSTAR provides good fitness to the SINR samples. The results indicate that LSTAR model achieves much better performance in modelling and forecasting of SINR data than the AR model.

ABSTRACTNext‐generation wireless networks are migrating to higher frequency bands to access abundant spectrum resources. The short wavelengths at these frequencies enable large‐scale antenna arrays with dynamic beamforming to counteract significant path loss. However, beam blockage becomes much more likely at these frequencies. This makes accurate beam quality assessment, which typically relies on the signal‐to‐interference‐plus‐noise ratio (SINR), essential for timely channel reconstruction. In this paper, we identify a phenomenon known as the self‐induced effect, where a high SINR may be estimated even when no signal is actually reaching the receiver. This effect prevents accurate beam quality assessment and limits the effective utilisation of reconfigurable intelligent surface for channel reconstruction. To address this challenge, we present a simple and efficient scheme to eliminate this effect. Numerical results show the effectiveness of the proposed scheme.

Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semantic communication systems. In this paper, we model the interference cancellation problem as a maximum a posteriori (MAP) problem over the joint posterior probability of the signal and interference, and theoretically prove that the solution provides excellent estimates for the signal and interference. To solve this problem, we develop an interference cancellation diffusion model (ICDM), which decomposes the joint posterior into independent prior probabilities of the signal and interference, along with the channel transition probability. The log-gradients of these distributions at each time step are learned separately by DMs and accurately estimated through deriving. ICDM further integrates these gradients with advanced numerical iteration method, achieving accurate and rapid interference cancellation. Extensive experiments demonstrate that ICDM significantly reduces the mean square error (MSE) and enhances perceptual quality compared to schemes without ICDM. For example, on the CelebA dataset under the Rayleigh fading channel with a signal-to-noise ratio (SNR) of 20 dB and signal to interference plus noise ratio (SINR) of 0 dB, ICDM reduces the MSE by 4.54 dB and improves the learned perceptual image patch similarity (LPIPS) by 2.47 dB. The code is available at https://github.com/Wireless3C-SJTU/ICDM.

In this chapter, an effective blind source separation (BSS) algorithm is applied to solve the co-channel interference problem in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the complex domain and improving output signal to interference plus noise ratio (SINR), but also have relatively low computational complexity. We propose a fast Fourier transform (FFT)-based algorithm called feedback independent component analysis (FebICA) that is able to blindly separate complex modulated digital signals. By applying this algorithm to communication signals, it is observed that it has the advantages of SINR gain improvement as well as low computational complexity. The performance of the FebICA algorithm is shown to be better than the joint approximate diagonalization of eigen-matrices (JADE) algorithm in terms of the output SINR and requires lower computational complexity than the analytical constant modulus algorithm (ACMA). The algorithm is also shown to be more robust with increasing number of sources compared to other algorithms. The separation performance by using the collected field data has also been demonstrated.

Recent advances in power line communications (PLC) have made it popular for in-home networking. This makes PLC an increasingly relevant source of interference for digital subscriber line (DSL) networks within the home environment. This paper presents measured PLC-to-DSL coupling channels for a worst case DSL deployment scenario. We then propose an interference cancelling scheme based on a frequency domain interference canceller (FDIC) that utilizes the common mode (CM) PLC interference to estimate and remove the differential mode (DM) PLC interference. The mean square error (MSE) of the proposed FDIC is derived and compared to the minimum MSE achieved by an optimum Wiener filter. Improvement in the signal to interference plus noise ratio (SINR) achieved by using the FDIC is also presented. Both mathematical analysis and simulation results demonstrate the effectiveness of the proposed FCID in reducing the DM PLC interference on the DM DSL signal.

This paper presents a theoretical analysis of subband adaptive array combining the cyclic prefix transmission scheme (SBAA-CP) in a multipath fading environment. The exact expressions for optimal weights, array outputs and the output signal to interference plus noise ratio (SINR) are derived. The analysis shows that use of the cyclic prefix data transmission scheme can significantly improve the performance of subband adaptive array (SBAA). We also show that if the output SINR is plotted versus the delay of the delay path then the output SINR of SBAA-CP is in effect the SINR of SBAA shifted by C samples to the right.

A novel linear transform is presented which is suitable for noisy fronthaul channels in distributed MIMO C-RAN systems. A linear transform based on the Karhunen-Loeve transform is optimized in terms of mean squared error (MSE). Using MSE as the objective function enables us to derive a concise closed MSE formula. By using this MSE formula and the Lagrange multiplier method, the optimal linear transform can be derived in a closed form and no optimization processes are required for designing this transform. We also present a dimension expansion method with a Vandermonde matrix as a noise mitigation method. The MSE formula for the Vandermonde dimension expansion indicates an explicit tradeoff relation be-tween spectral efficiency and energy efficiency of the fronthaul channel. Results from numerical experiments and evaluations support our theoretical arguments.

In wireless communication systems with multiple users, the near-far effect and the cochannel interferences decrease the spectral efficiency and system performance. An efficient known method to combat these destructive effects is to properly adjust the transmit powers. A practical and efficient way for power control (PC) in cellular communication systems is the autonomous distributed PC (DPC). In this method, the transmit power of each link is independently modified based on the received signal-to-interference-plus-noise ratio (SINR) reported from the other side of that link. It is known that in a perfect scenario where the reported SINRs are exact, this algorithm converges to the optimum transmit powers. However, in the case of erroneous SINR reports, the transmit powers deviate from their optimum values. This paper addresses the destructive effect of inaccurate SINR reports on the performance of a DPC algorithm. Then, independent variable step-sizes that update the transmit powers are proposed, and convergence of the proposed DPC algorithm to the optimum solution of the original problem is analytically shown. Additionally, a protocol to adaptively tune the step-sizes in our algorithm, which improves the performance of PC in the presence of inaccurate SINR reports, is proposed. Computer simulations are used to study the performance of the proposed method in terms of both the speed of converges and the steady-state accuracy of link qualities.

In this paper, we use joint perfect power control (PPC) and adaptive beamforming algorithms as constrained least mean square (CLMS) and conjugate gradient adaptive beamforming (CGBF) in a 2D urban environment. The CLMS algorithm is capable of efficiently adapting according to the environment and able to permanently maintain the chosen frequency response in the look direction while minimizing the output power of the array. In addition, with the CGBF algorithm, the desired users’ signal in an arbitrary path is passed and the inter-path interference (IPI) is canceled in other paths in each RAKE finger. The adaptability of the algorithms is closely observed for uniformly spaced linear array. In addition, in this paper, we present switched-beam (SB) technique. In the SB technique by using a number of fixed, independent, or directional antennas we increase the uplink capacity of the wireless systems. Also, we study an analytical approach for the evaluation of the impact of power control error (PCE) on wireless communication systems. Simulation results indicate that the SB technique is able to considerably increase the signal to interference plus noise ratio (SINR) of desired user in comparison with other algorithms. In addition, we observe that the SINR in the CLMS algorithm is lower than the CGBF algorithm. Finally, we discuss two parameters of the PCE and path-loss exponent and their effects on capacity of the system via some computer. Keywords: Adaptive beamforming, Power control, Wireless networks

The Internet of Things (IoT) is a significant branch of the ongoing advances in the Internet and mobile communications. Yet, the use of a large number of IoT devices can severely worsen the spectrum scarcity problem. The usable spectrum resources are almost entirely occupied, and thus, the increasing demands of radio access from IoT devices cannot be met. To tackle this problem, the Cognitive Internet of Things (CIoT) has been proposed. In a CIoT network, secondary users, i.e., sensors and actuators, can access the licensed spectrum bands provided by licensed primary users (such as cellular telephones). Security is a major concern in CIoT networks. However, the traditional encryption method at upper layers (such as symmetric and asymmetric ciphers) may not be suitable for CIoT networks since these networks are composed of low-profile devices. In this paper, we address the security issues in spectrum-leasing-based CIoT networks using physical layer methods. Considering that the CIoT networks are cooperative in nature, we propose to employ cooperative jamming to achieve secure transmission. In our proposed cooperative jamming scheme, a certain secondary user is employed as the helper to harvest energy transmitted by the source and then uses the harvested energy to generate an artificial noise that jams the eavesdropper without interfering with the legitimate receivers. The goal is to minimize the Signal to Interference plus Noise Ratio (SINR) at the eavesdropper subject to the Quality of Service (QoS) constraints of the primary traffic and the secondary traffic. We formulate the minimization problem into a two-stage robust optimization problem based on the worst-case Channel State Information of the Eavesdropper (ECSI). By using Semi-Definite Programming (SDP), the optimal solutions of the transmit covariance matrices can be obtained. Moreover, in order to build an incentive mechanism for the secondary users, we propose an auction framework based on the cooperative jamming scheme. The proposed auction framework jointly formulates the helper selection and the corresponding energy allocation problems under the constraint of the eavesdropper's SINR. By adopting the Vickrey auction, truthfulness and individual rationality can be achieved. Simulation results demonstrate the effective performance of the cooperative jamming scheme and the auction framework.

Interference alignment (IA) with symbol extensions in the quasi-static flat-fading K-user multipleinput multiple-output (MIMO) interference channel (IC) is considered in this paper. In general, long symbol extensions are required to achieve the optimal fractional degrees of freedom (DOF). However, long symbol extensions over orthogonal dimensions produce structured (diagonal or block diagonal) channel matrices from transmitters to receivers. Most of existing approaches are limited in cases where the channels have some special structures, because they align the interference without preserving the dimensionality of the desired signal explicitly. To overcome this common drawback of most existing IA algorithms, two novel iterative algorithms for IA with symbol extensions are proposed. The first algorithm designs transceivers for IA based on the mean square error (MSE) criterion which minimizes the total MSE of the system while preserving the dimensionality of the desired signal. The novel IA algorithm is a constrained optimization problem which can be solved by Lagrangian method. Its convergence is proven as well. Utilizing the reciprocity of alignment, the second algorithm is proposed based on the maximization of the multidimensional case of the generalized Rayleigh Quotient. It maximizes each receiver’s signal to interference plus noise ratio (SINR) while preserving the dimensionality of the desired signal. In simulation results, we show the superiority of the proposed algorithms in terms of four aspects, i.e., average sum rate, the fraction of the interfering signal power in the desired signal subspace, bit error rate (BER) and the relative power of the weakest desired data stream.

In the first part of the thesis we investigate bandwidth allocation in next generation cellular systems employing relays similar to LTE advanced systems with type-I relays. We jointly optimize the bandwidth and power usage under constraints on required rate, bandwidth and transmit power. We study scenarios wherein, the relay acts as a forwarder for multiple User Equipments (UEs/users) in both uplink and/or downlink. This includes scenarios when the relay has its own data to send along with forwarding the data of other users. We examine the weighted power minimization problem for relaying with multiple users. We also show specifi c results with N user scenario and also single user case in order to understand how the bandwidth and power are allocated. Numerical evaluations with N users on a three sector LTE-A cell employing Fractional Frequency Reuse (FFR) indicate that power savings of at least 3 dB can be achieved by optimizing over both bandwidth and power. Base stations with a large number of transmit antennas have the potential to serve a large number of users simultaneously at higher rates. They also promise a lower power consumption due to coherent combining at the receiver. However, the receiver processing in the uplink relies on the channel stimates which are known to su ffer from pilot contamination. In the second part of the thesis, we perform an uplink large system analysis of multi-cell multi-antenna system when the receiver employs a matched fi ltering and MMSE fi ltering with a pilot contaminated estimate. We find the asymptotic Signal to Interference plus Noise Ratio (SINR) as the number of antennas and number of users per base station grow large while maintaining a fixed ratio. To do this, we make use of the similarity of the uplink received signal in a multi-antenna system to the representation of the received signal in CDMA systems. The asymptotic SINR expression for both matched fi lter and the MMSE fi lter explicitly captures the e ffect of pilot contamination and that of interference averaging. This also explains the SINR performance of receiver processing schemes at di fferent regimes such as instances when the number of antennas are comparable to number of users as well as when antennas exceed greatly the number of users. Specifi cally, we explore the scenario where the number of users being served are comparable to the number of antennas at the base station. It is seen that MMSE fi lter is capable of suppressing the in-cell interference and that the interference power due to pilot contamination is the same as in a matched fi lter with a pilot contaminated estimate. We find the expression for the amount of interference suppression obtained using an MMSE filter which is an important factor when there are signi ficant number of users in the system as compared to the number of antennas. We validate the asymptotic expression through simulations and compare with an MMSE fi lter with a perfect estimate. Simulation results for achievable rate is close to theory for even a 10-antenna base station with 3 or more users per cell. In a typical set up, in terms of the five percentile SIR, the MMSE fi lter is shown to provide signifi cant gains over matched fi ltering and is within 5 dB of MMSE fi lter with perfect channel estimate. We also show that the achievable rates are within a 1 bit/symbol of the MMSE with perfect estimate when the number of users is comparable to the number of antennas.

A cooperative bidirectional relaying scheme based on distributed relay beamforming is proposed for an underlay cognitive radio network which consists of two secondary transceivers and K cognitive relay nodes and a primary network with a transmitter and receiver, all equipped with single antenna. For this network we assume that the second-order statistics of the channel coefficients between the relays and primary network is available. Also the perfect channel state information (CSI) is assumed for secondary network. Here, we propose a signal-to-interference plus noise-ratio (SINR) Balancing based on distributed relay beamforming. Our aim is to design the beamforming coefficients to maximize quality of service (QoS) for the secondary network while satisfying tolerable interference constraint for the primary network. Using semidefinite relaxation we turned this problem into convex feasibility problem which can be solved along with bisection search method. Our simulation results show that the uncertainty about the channel coefficients can be degrade the performance of the network considerably. Also as we increased the tolerable interference on primary receiver, the maximum achievable SINR is increased.

In this paper we quantify theoretically the effect of the desired-signal power level on the mean square filter estimation error and the normalized output signal-to-interference-plus-noise-ratio (SINR) of sample matrix inversion (SMI)-type estimates of the minimum mean-square-error (MMSE) and the linearly constrained minimum variance (LCMV) filters. We prove that in finite data support situations filters that utilize a sample average estimate of the desired-signal-absent input correlation matrix exhibit superior normalized filter output SINR and mean square filter estimation error when compared to filters that utilize a sample average estimate of the desired-signal-present input correlation matrix. Finally, we investigate pilot-assisted and decision-directed adaptive filter implementations that exhibit near desired-signal-absent SMI-filtering performance while they are trained using desired-signal-present data/observations.

This paper presents a computationally efficient beamforming approach to combat wiretapping in a relay-based multiple-input multiple output (MIMO) communication system which is part of a cognitive radio (CR) network. The system operates in two stages, that is, multiple-access (MA) followed by broadcasting (BC) using physical layer network coding (PNC). The beamforming design is based on minimizing the mean square error (MSE) at the receiving node(s) while enforcing signal-to-interference-plus-noise ratio (SINR) constraints at the eavesdroppers. The constraints take into account uncertainty bounds on eavesdropper channel estimation errors. In each stage of communication, an optimization problem is devised and solved using an iterative procedure, considering two different types of eavesdropper functionality, i.e., selection combining and “blind” beamforming. Numerical results show the convergence of the MSE at the nodes and the SINR distributions at the eavesdroppers for both cases. Comparisons to previously suggested solutions for blind beamforming are also included showing improvements in MSE values in the MA stage and computational efficiency in both stages.