Combating Chirp Interference for Multi-target LoRa Localization

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design.

This letter investigates the throughput of a dense multi-antenna network in the presence of channel state information (CSI) errors at transmitter. The Gauss–Markov auto-regressive model is put forth to characterize CSI errors. We focus on a class of cases that the variance of CSI error is independent of serving distances between users and BSs. An original expression for area spectral efficiency (ASE) is derived with the tools of stochastic geometry. Based on the expectations of effective channel gains, a more tractable approximation for ASE is further obtained. With the help of the tractable approximation, we consider the optimization problem for ASE maximization with respect to the number of active users. We demonstrate that ASE scales linearly with the number of antennas when the number of antennas is larger than a certain value. The CSI errors determine the increasing slope.

In systems employing pilot-symbol aided channel estimation, the pilot-to-data power ratio is known to have a large impact on performance. Therefore, previous works proposed methods setting the pilot power such that either the weighted sum of the mean squared error (MSE) of the estimated data symbols is minimized or the overall spectral efficiency (SE) is maximized. However, previous works did not take into account the impact of correlated antennas and channel state information (CSI) errors on the optimal pilot power setting. In this paper, we consider the uplink of a multiuser multiple-input multiple-output (MU MIMO) system employing a receiver that minimizes the MSE of the received data symbols in the presence of CSI errors and derive closed-form expressions for the MSE and the achievable SE. These expressions take into account the impact of antenna correlation and CSI errors, and are a function of pilot power and the number of receive antennas. The analytical and numerical results can help set the pilot power, minimizing the MSE in multiple antenna systems.

This paper investigates the influence of channel state information (CSI) error at the transmitter on the achievable throughput of our previously reported layered partially non-orthogonal block diagonalization (BD) precoding method with adaptive interference admission control (AIAC) for downlink multiuser (MU) multiple-input multiple-output (MIMO) transmission employing multiple base station (BS) cooperation. The impacts of the CSI estimation error at the user terminal and feedback delay are separately examined and they are compared to conventional approaches. Computer simulation results reveal that the layered BD with AIAC is more robust against CSI error at the transmitter than full BD with full CSI feedback, which implies the effectiveness of the layered BD with AIAC method in practice.

Non-orthogonal multiple access (NOMA) is one of the key technologies to serve in ultra-dense networks with massive connections which is crucial for Internet of Things. Besides, NOMA provides better spectral efficiency compared to orthogonal multiple access. However, NOMA systems have been mostly investigated only in terms of ergodic capacity (EC) and outage probability (OP) whereas error performances have not been well-studied. In addition, in those analysis, mostly perfect successive interference canceler (SIC) is assumed or the considered imperfect SIC model is not reasonable. Besides, channel state information (CSI) errors are also not considered in most studies. However, this is not the case for the practical scenarios, and these imperfect SIC and CSI effects limit the performance of NOMA involved systems. Moreover, the imperfect SIC causes unfairness between users. In this paper, we introduce reversed decode-forward relaying NOMA (R-DFNOMA) to improve user fairness compared to conventional DFNOMA (C-DFNOMA). In the analysis, we define imperfect SIC effect as dependant to channel fading and with this imperfect SIC and CSI errors, we derive exact expressions of EC and OP. We also provide upper bound for EC, and asymptotic and lower bound expressions for OP. Furthermore, we evaluate bit error performance of the proposed R-DFNOMA and derive exact bit error probability (BEP) in closed-form with imperfect CSI which is the first study analyzing error performances of decode-forward relaying NOMA with imperfect CSI. Then, we define user fairness index in terms of all key performance indicators (KPIs) (i.e., EC, OP and BEP). Based on extensive simulations, all derived expressions are validated, and it is proved that the proposed R-DFNOMA provides better user fairness than C-DFNOMA in terms of all KPIs. Finally, we discuss the effect of power allocations at both source and relay on the performance metrics and user fairness.

In this paper, we consider a new design problem of optimizing a linear transceiver for correlated multiple-input multiple-output (MIMO) interference channels in the presence of channel state information (CSI) errors, which is a more realistic and practical scenario than those considered in the previous studies on uncorrelated MIMO interference channels. By taking CSI errors into account, the optimization problem is initially formulated to minimize the average mean square error (MSE) under the general power constraints. Since the objective function is not jointly convex in precoders and receive filters, we split the original problem into two convex subproblems, and then linear precoders and receive filters are obtained by solving two subproblems iteratively. It is shown that the proposed algorithm is guaranteed to converge to a local minimum. The numerical results show that the proposed algorithm can significantly reduce the sensitivity to CSI errors compared with the existing robust schemes in the correlated MIMO interference channel.

We present various robust precoder designs for two-way relaying in a cognitive radio network, where a pair of cognitive (or secondary) transceiver nodes communicate with each other assisted by a set of cognitive two-way relays. The secondary nodes share the spectrum with a licensed primary user (PU) node while keeping the interference to the PU below a specified threshold. The PU node and the cognitive transceivers employ single transmit/receive antennas whereas the secondary relay nodes employ multiple transmit/receive antennas. The proposed precoder designs ensure robust performance in the presence of errors in the channel state information (CSI). Such robust designs are of significant interest since in practice it is very difficult to obtain perfect CSI. We consider CSI errors with two different types of characterization and corresponding robust designs. First, we consider robust relay precoder designs that are applicable when CSI errors have known first and second moments. Next, we consider robust designs that are applicable when the CSI error can be characterized in terms of spherical uncertainty region. We show that the proposed designs can be reformulated as convex optimization problems that can be solved efficiently. Through numerical simulations and comparisons we illustrate the performance of the proposed designs.

This paper focuses on multicell coordinated beamforming in the presence of channel state information (CSI) errors, where base stations (BSs) collaboratively mitigate their intercell interference (ICI). Assuming that the CSI errors are hyper-spherically bounded, we consider an optimization problem that minimizes the overall transmission power of BSs subject to signal-to-interference-plus-noise-ratio (SINR) constraints at each mobile station (MS). We solve this problem in a distributed manner with a limited information exchange among BSs. Using semidefinite relaxation (SDR) and the S-Lemma, we first reformulate our optimization problem into a numerically tractable one. Since the SINR constraints are coupled, we introduce an algorithm by which each BS can obtain a local version of its coupling variables via a small data exchange with other BSs. Then, we propose an iterative algorithm that employs the projected gradient method to coordinate ICI across multiple BSs. Finally, we extend the application of the proposed algorithm to solve the problem of robust and distributed per-user SINR maximization under per-BS power constraints. Simulation results confirm the effectiveness of the proposed algorithm in terms of power efficiency and convergence in the presence of CSI uncertainties.

In this paper, we study the performance of regularized channel inversion (RCI) precoding in large MISO broadcast channels with confidential messages (BCC). We obtain a deterministic approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas M and the number of users K grow to infinity in a fixed ratio β=K/M. We derive the optimal regularization parameter ξ and the optimal network load β that maximize the per-antenna secrecy sum-rate. We then propose a linear precoder based on RCI and power reduction (RCI-PR) that significantly increases the high-SNR secrecy sum-rate for 1<;β<;2. Our proposed precoder achieves a per-user secrecy rate which has the same high-SNR scaling factor as both the following upper bounds: (i) the rate of the optimum RCI precoder without secrecy requirements, and (ii) the secrecy capacity of a single-user system without interference. Furthermore, we obtain a deterministic approximation for the secrecy sum-rate achievable by RCI precoding in the presence of channel state information (CSI) error. We also analyze the performance of our proposed RCI-PR precoder with CSI error, and we determine how the error must scale with the SNR in order to maintain a given rate gap to the case with perfect CSI.

This paper considers robust multi-cell coordinated beamforming (MCBF) design for downlink wireless systems, in the presence of channel state information (CSI) errors. By assuming that the CSI errors are complex Gaussian distributed, we formulate a chance-constrained robust MCBF design problem which guarantees that the mobile stations can achieve the desired signal-to-interference-plus-noise ratio (SINR) requirements with a high probability. A convex approximation method, based on semidefinite relaxation and tractable probability approximation formulations, is proposed. The goal is to solve the convex approximation formulation in a distributed manner, with only a small amount of information exchange between base stations. To this end, we develop a distributed implementation by applying a convex optimization method, called weighted variable-penalty alternating direction method of multipliers (WVP-ADMM), which is numerically more stable and can converge faster than the standard ADMM method. Simulation results are presented to examine the chance-constrained robust MCBF design and the proposed distributed implementation algorithm.

In this work we consider the multiple-input multiple-output (MIMO) interference broadcast channel (IBC) and analyse the performance of interference alignment (IA) under imperfect channel state information (CSI), where the variance of the CSI error depends on the signal-to-noise ratio (SNR). First, we derive an upper bound on asymptotic mean loss in sum rate compared to the perfect CSI case and then we quantify the achievable degrees of freedom (DoF) with imperfect CSI. Both sum rate loss and achievable DoF are found to be dependent on the number of cells in the system and the amount of users per cell, in addition to the CSI error parameters themselves. Results show that when error variance is inversely proportional to SNR, full DoF are achievable and the asymptotic sum rate loss is bounded by a derived value. Additionally if the CSI imperfection does not disappear for asymptotically high SNR, then the full DoF gain promised by IA cannot be achieved; we quantify this loss in relation to the CSI mismatch itself. The analytically derived bounds are validated via system simulation, with the cellular counterparts of the maximum signal-to-interference-plus-noise ratio (Max-SINR) and the minimum weighted leakage interference (Min-WLI) algorithms being the IA techniques of choice. Secondly, inspired by the CSI mismatch model used to derive the bounds, we present a novel Max-SINR algorithm with stochastic CSI error knowledge (Max-SINR-SCEK) for the MIMO IBC. Simulations show that the proposed algorithm improves performance over the standard one under imperfect CSI conditions, without any additional computational costs.

In this paper, the transmission performance considering an error of channel state information (CSI) feedback for a downlink mobile WiMAX system with multiuser multiple-input multiple-output (MU-MIMO) systems in a computer simulation and field experiment are described. In computer simulation, a downlink MU-MIMO system can be realized by using the block diagonalization algorithm, and each user can receive signals without any signal interference from other users by using the perfect CSI feedback. The bit error ratio performance and channel capacity in accordance with the errors of CSI were simulated in a multipath fading environment. In the field experiment, the received power and downlink throughput in the UDP layer were measured on an experimental mobile WiMAX system developed in Azumino City in Japan. In comparison with the simulated and experimental results, it was confirmed that the maximum throughput performance in the downlink had almost the same performance as the simulated throughput when the error of CSI was 30 %.

The IEEE802.11a standard for indoor wireless LAN systems was released commercially and is wide spread in the marketplace. To achieve higher throughput, IEEE802.11n was proposed and is now undergoing standardization. The MIMO-OFDM technique, which employs multiple antennas for transmission and reception, is the core technology for IEEE802.11n (Foshini and Gans, 1998). This technique requires estimation of channel state information (CSI) on the receiving side. Using the technique called adaptive MIMO-OFDM (Telatar, 1999) in which CSI is applied to both the transmitting and receiving sides, an even higher throughput is achieved. To achieve the maximum performance from adaptive MIMO-OFDM, we must accurately estimate the CSI for all the subcarriers; however, numerous calculations are needed to obtain this estimation. So, to actualize the adaptive MIMO-OFDM system, the calculation load must be decreased. This can be accomplished by applying one CSI to other subcarriers. However, channel error exists between the actual channel and the applied CSI. The channel error also plays a role in decreasing the communications quality (Medard, 2000). The communications quality when using the adaptive MIMO-OFDM technique that has CSI error was evaluated by simulation (Narula et al., 1998). However, there are few measurement evaluation reports on CSI error. This paper focuses on the ratio between the power of the eigenvalue and the interference power that suppresses the communication quality based on the measurement results. We propose an empirical formula for predicting the signal to interference ratio (SIR), which is the ratio between the power of the eigenvalue and the interference power. This SIR empirical formula helps to predict the communication quality and simplify the simulation.

The increasing demand for high data rate in wireless communication systems gives rise to broadband communication systems. The radio channel is plagued by multipath propagation, which causes frequency-selective fading in broadband signals. Orthogonal Frequency-Division Multiplexing (OFDM) is a modulation scheme specifically designed to facilitate high-speed data transmission over frequency-selective fading channels. The problem of channel modeling in the frequency domain is first investigated for the wideband and ultra wideband wireless channels. The channel is converted into an equivalent discrete channel by uniformly sampling the continuous channel frequency response (CFR), which results in a discrete CFR. A necessary and sufficient condition is established for the existence of parametric models for the discrete CFR. Based on this condition, we provide a justification for the effectiveness of previously reported autoregressive (AR) models in the frequency domain of wideband and ultra wideband channels. Resource allocation based on channel state information (CSI) is known to be a very powerful method for improving the spectral efficiency of OFDM systems. Bit and power allocation algorithms have been discussed for both static channels, where perfect knowledge of CSI is assumed, and time-varying channels, where the knowledge of CSI is imperfect. In case of static channels, the optimal resource allocation for multiuser OFDM systems has been investigated. Novel algorithms are proposed for subcarrier allocation and bit-power allocation with considerably lower complexity than other schemes in the literature. For time-varying channel, the error in CSI due to channel variation is recognized as the main obstacle for achieving the full potential of resource allocation. Channel prediction is proposed to suppress errors in the CSI and new bit and power allocation schemes incorporating imperfect CSI are presented and their performance is evaluated through simulations. Finally, a maximum likelihood (ML) receiver for Multiband Keying (MBK) signals is discussed, where MBK is a modulation scheme proposed for ultra wideband systems (UWB). The receiver structure and the associated ML decision rule is derived through analysis. A suboptimal algorithm based on a depth-first tree search is introduced to significantly reduce the computational complexity of the receiver.

With the ellipsoid‐bounded channel state information (CSI) errors, this study investigates the robust secrecy energy efficiency (SEE) optimisation in a wireless powered heterogeneous network. Specifically, the authors consider the network where multiple femtocell base stations (FBSs) are deployed under the coverage of one macrocell base station (MBS). Meanwhile, the MBS serves multiple macrocell users in the presence of a malicious multiple‐antenna eavesdropper (Eve) while each FBS serves a pair of information receiver (IR) and energy receiver (ER) with multiple antennas, where the ER attempts to wiretap the information of IR in the same femtocell. To promote the secrecy performance, artificial noise (AN) is aided into the downlink signal at the MBS and FBSs, and the problem of maximising SEE is formed in a cross‐tier multi‐cell AN‐aided transmit beamforming design. This problem is non‐convex while containing infinite constraints caused by CSI errors, which cannot be solved directly. In this regard, the authors resort to the successive convex approximation, semi‐definite relaxation techniques and Lagrange duality theory to acquire its solvable form. Moreover, to reduce the overhead of information exchange among coordinated BSs, they further propose a distributed approach based on alternative direction multiplier method. Finally, simulation results validate the effectiveness of the proposed design.