Robust Beamforming Optimization for Downlink Cloud Radio Access Networks

We propose a novel restriction and relaxation (RAR) method for robust relay beamforming design in cognitive two-way relay networks with imperfect channel state information (CSI). Due to the uncertainty in CSI, the self-interference cannot be completely canceled. Hence, both channel uncertainties and residual self-interference should be considered. The proposed RAR method aims to minimize the transmit power of relay nodes while guaranteeing the worst-case signal-to-interference plus noise ratio (SINR) of secondary users (SUs) as well as satisfying the interference temperature (IT) constraints. Specifically, in the restriction step of RAR, infinite number of complicated constraints are reformulated into a finite number of linear matrix inequalities (LMIs). In the relaxation step of RAR, the nonconvex robust design problem is transformed into a convex semidefinite program (SDP), which can be efficiently solved by interior-point methods. Simulation results verify the effectiveness and robustness of the proposed method.

For multi-input multi-output (MIMO) systems, the detection performance can be severely deteriorated by the channel state information (CSI) uncertainties. In this paper, we propose a learnable robust MIMO detector by taking the statistics of CSI imperfection into account. Specifically, we first formulate a robust maximum likelihood (ML) detection problem and then develop an alternating direction method of multipli-ers (ADMM) based solution, which involves the calculations of closed-form expressions in each iteration. Furthermore, a model-driven neural network is established by unfolding the derived ADMM algorithm whose penalty parameters are learned via offline training. Simulation results demonstrate that the proposed network can considerably outperform the conventional mismatched ML detector and even approach the optimal robust ML detector with only 5 layers.

We investigate the problem of robust joint transmitter and receiver power allocation for uplink multi-input multi-output (MIMO) transmissions. The channel model is assumed to have Rayleigh flat fading. The objective of power allocation is to minimize the total MSE each of which has limited transmit power. The problem is formulated as a non-linear optimization problem. In addition, we investigate robust joint transmitter and receiver power allocation with imperfect channel state information (CSI). The CSI error is assumed to be unknown but bounded by a constant. This problem is formulated as a semidefinite programming (SDP) problem with bilinear matrix inequality (BMI) constraints. Numerical results indicate that, with imperfect CSI, the BER performance improvement obtained by taking robustness into account in the joint power allocation process.

In this paper, we consider a real-time E-Health Internet of Things (IoT) system with the uncertainty of channel state information (CSI), in which a wearable device collects radio frequency (RF) energy from a Personal Digital Assistant (PDA), and then transmits healthcare data status updates to the corresponding PDA promptly. The Peak Age of Information (PAoI) is considered as a parameter to measure the freshness of information. Our goal is to minimize the average PAoI under non-convex constraints related to an uncertain CSI mismatch model. Only mean and variance information is specified in the distributional ambiguity set. This distributionally robust optimization problem is transformed into a tractable semi-definite programming (SDP) problem using the Conditional Value-at-Risk (CVaR) based method. To solve this NP-hard problem effectively, we decompose the PAoI minimization problem into two subproblems, and propose a low complexity iterative algorithm to derive a suboptimal solution. Simulation results show an average PAoI-energy tradeoff in the considered healthcare IoT, and the CVaR based method can achieve a better performance than a non-robust method.

The design problem associated with robust downlink beamforming in multicast, multigroup, multicell wireless systems is addressed. The channel state information (CSI) of users is assumed to be imperfect and the uncertainty of CSI is modelled using the Frobenius norm. The objective is to optimise the signal-to-interference-plus-noise ratio over all users with a constraint on the maximum total transmitted power. This was achieved through a robust solution using the successive convex approximation (SCA) method. The beamforming problem is treated as a bi-convex problem, which is solved using the iterate-alternative convex technique. Here, the CSI uncertainty is addressed using a convex package through the non-monotone spectral projected gradient method and the beamforming vector is extracted using the SCA method. Also, the authors offer the required condition to extract the beamform vector using the SCA method through a suboptimal solution that always addressed before using different beamforming methods. Their simulation results examine all proposed system parameters in order to show convergence and feasibility of the solution. They also compare the solution with a suboptimal solution and the quality of service method for imperfect CSI in downlink beamforming. Numerical results show that the robust solution achieves the best power efficiency for practical solutions.

We study a beamformed Alamouti coding scheme for a multigroup multicast downlink system. In this letter, we take into account imperfect channel state information (CSI) at the base station, where the CSI uncertainty region is formed by the intersection of multiple ellipsoids. This unified CSI uncertainty model leads to a challenging robust rank-two multicast beamforming problem. We first develop a conservative approximation method to handle the intractable constraints in the formulated robust beamforming problem under the considered CSI uncertainty region. Then, we apply the semidefinite relaxation (SDR) technique to approximately solve this problem. Our rank-profile analysis shows that the SDR is tight when there are at most two users in each multicast group and the CSI uncertainty region is not too large. Finally, we carry out simulations to validate the effectiveness of the proposed conservative approximation method. The simulation results coincide with the rank-profile analysis.

The following topics are dealt with: MIMO communication; array signal processing; radiofrequency interference; probability; wireless channels; iterative methods; OFDM modulation; optimisation; channel estimation; precoding.

Coordinated multi-point (CoMP) is an efficient technique to increase cell-edge coverage probability and throughput in cloud radio access network (C- RAN). In this paper, we develop a tractable framework to analytically evaluate the performance of two typical CoMP schemes, namely, zero-forcing beamforming(ZFBF) and non-coherent joint transmission (NC-JT). Using the stochastic geometry based approach, we characterize the signal-to- interference ratio (SIR) distribution at a typical downlink user. Furthermore, we consider the effect of imperfect channel state information (CSI) on coverage probability of the typical user from the practical design perspective. It is observed that ZFBF can outperform NC-JT only when there is sufficient overhead to convey the quantized CSI. In summary, this work serves as a good rule of thumb to compare the effect of CoMP techniques in C-RAN.

In this paper, we consider a decode-and-forward (DF) full-duplex relay (FDR)-aided downlink wireless communication system consisting of one base station (BS) equipped with large scale antennas and multiple MIMO FDRs, which have been recognized as essential techniques in the fifth generation (5G) wireless communications. In view of the system performance not only limited by self-interference (SI) and inter-relay interference (IRI) caused by FDRs but also by channel state information uncertainty, a distributed worst-case robust design of FDR beamforming and total power minimization for downlink transmission is proposed subject to relays' and users' target rates, a centralized solution is presented, and then its distributed implementation using alternating direction method of multipliers (ADMM) is presented as well. Finally, some simulation results are provided to demonstrate the efficacy of the proposed algorithm.

In cellular networks, a local uplink scheduler cannot compute good estimates of the inter-cell interference even with exact channel state information (CSI). Losses (due to decoding errors) will hence frequently occur. The maximum achievable performance (MAP) can only be achieved when all the cells are scheduled simultaneously centrally so as to manage interference and power optimally. In this paper, we focus on the performance of a practical system with local schedulers (i.e., each cell is scheduled independently) and compare it to MAP. We also study how to improve the performance of practical systems when a Cloud Radio Access Network (C-RAN) is present, with simple coordination schemes. To this end, to compute MAP, we first formulate an offline system-wide scheduling problem and transform it into a more tractable signomial problem that we solve quasi-optimally using an iterative algorithm. Since this problem requires all the channel information in the system and it has a high computational complexity, it is not suitable for a real-time system. Then, we show that a practical system using an efficient local scheduler (in each cell) yields a much lower performance than MAP. To decrease this performance gap, in a system with a C-RAN, we propose a very simple and fast scheme to coordinate the scheduling in all cells and show that it improves the performance significantly (in terms of throughput and losses) even when only partial CSI is available.

<p><strong> </strong><a href="https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7924448">2017 IEEE Wireless Communications and Networking Conference (WCNC)</a></p>

Cloud radio access networks (C-RANs) have drawn considerable interests due to the significant improvements of spectral and energy efficiencies. Since most signal processing functions are moved to the centralized baseband unit (BBU), remote radio heads (RRHs) in C-RANs can be regarded as soft relays to transfer the received signals. The centralization characteristics in C-RANs make traditional channel estimation and training design approaches inefficient, and the requirements of perfect channel state information (CSI) would not be satisfied in turn. To solve this problem, a segment training based individual channel estimation scheme and the corresponding training design are proposed for C-RANs in this paper. Particularly, the channel estimator in terms of the sequential minimum mean-square-error (MMSE) is developed through a prior knowledge of long-term channel correlation statistics and previous channel estimates. The optimal training design for the developed estimator is derived by minimizing the estimation mean-square-error (MSE). Further, the optimal training design for the channel estimation of radio access links is computed by applying the eigenvalue decomposition (EVD). Numerical results show that performance gains of the proposed channel estimation and training design schemes are significant.

We consider a dynamic time division duplexing wireless network and propose a distributed coordinated beamforming algorithm based on Alternating Direction Method of Multipliers (ADMM) technique assuming the availability of perfect channel state information. Our design objective is to minimize the sum transmit power at the base stations subject to minimum signal-to-interference-plus-noise ratio (SINR) constraints for downlink mobile stations and a maximum interference power threshold for uplink mobile stations. First, we propose a centralized algorithm based on the relaxed Semidefinite Programming (SDP) technique. To obtain the beamforming solution in a distributed way, we further propose a distributed coordinated beamforming algorithm using the ADMM technique. Detailed simulation results are presented to examine the effectiveness of the proposed algorithms. It is shown that the proposed algorithm achieves better performance in terms of the design objective and converges faster than the reference algorithm based on primal decomposition.

This letter investigates the bit error rate (BER) performance of orthogonal frequency division multiplexing index modulation, employing the maximal ratio combining-based low-complexity greedy detector (MRC-GD) and the PSK modulation. For performance analysis, we derive tight expressions for both index error probability (IEP) and BER, taking into account channel state information (CSI) uncertainty. This provides insight into various impacts of CSI uncertainty on the diversity gain and error floor of the IEP and the BER, respectively. We clearly show that under imperfect CSI, the MRC-aided GD can perform like the MRC-maximum likelihood detector, at lower complexity. Finally, simulation results are presented to verify the accuracy of derived expressions and the theoretical analysis.

This paper studies the impact of channel error on the achievable rate of symmetrical K-user multiple-input multiple-output linear interference alignment (IA) networks. The upper and lower bounds of the achievable sum rate are derived analytically with the assumption of orthonormal transmit precoders and receive filters designed from imperfect channel state information (CSI) over both the uncorrelated and correlated channels. For uncorrelated channels, quite tight lower and upper bounds are obtained. The impact of channel error on the degrees of freedom (DoF) and the DoF persistence conditions are also investigated. Results show that the DoF of IA networks persists only if the channel error decreases in an order higher than the signal-to-noise ratio. For correlated channel, the lower and upper bounds for one realization of IA are derived. The derived upper bound can be used to characterize the achievable rate approximately. Simulation results indicate that the achievable rate of IA network is influenced significantly by CSI uncertainty. The obtained analytical bounds provide an intuitive way to show the impact of channel error on the achievable rate and thus can help practical systems deign.