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The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, due to the bandwidth constraint, the distributed nodes (DNs) might only provide binary representatives of the source signal, and then transmit them to the central node (CN). In this paper, we consider the joint estimation of signal amplitude and background noise variance from binary observations over decentralized channels. We first analyze the Cramér–Rao lower bounds (CRLBs) of the parameters of interest and develop a quasilinear estimator (QLE), in which the desirable estimates can be obtained from several intermediate parameters linearly. Next, we consider a more realistic situation where the decentralized channel is noisy during the data transmission. Based on the error propagation model, the asymptotic analysis shows that the performance of the proposed QLE is mainly dominated by the thresholds of the quantizers, which encourages us to adopt a correlated quantization (CQ) scheme by exploiting the spatial correlation among background noises/channel noises. To ease the implementation of QLE in practice, an adaptive quantization (AQ) scheme is also proposed so as to obtain reasonable selections of the required thresholds. Finally, numerical simulations are provided to validate our theoretical findings. [ABSTRACT FROM AUTHOR]

Performance analysis is studied in this paper for the wireless transmissions in Internet of Things (IoT) system, where both the direct link and the multihop relaying caching wireless transmission from the source node to the destination node are taken into the consideration. The key feature is the Nakagami channels of the wireless channel from the source node to the destination node, which results in the difficulty of the theoretical analysis over the system performance. To tackle this difficulty, the probability distribution function (PDF) of the received signal-to-noise ratio (SNR) at the destination node is derived by exploiting the function and integral properties. Then, the outage probability and bit error rate (BER) of the whole wireless IoT system are derived in the analytical expression without any approximation. Numerical simulations demonstrate the accuracy of the derived theoretical analysis for this system. [ABSTRACT FROM AUTHOR]

The energy-efficient resource allocation problem is investigated for the downlink massive multiple-input–multiple-output (MIMO) frequency-division duplexing (FDD) system under a correlated Rayleigh fading channel. Our objective is to maximize energy efficiency by adjusting the training duration, training power, and data power, under the constraint of the total transmit energy and spectral efficiency requirement for the user, which evaluates the impacts of the training and data transmission phases simultaneously. The optimization problem is established in a complicated form, where the difficulties lie in the nonanalytic expression of the involved objective function, as well as the nonconvex nature of the optimization problem. To solve it, the deterministic equivalent approximation methodology is introduced to obtain an accurate analytical expression of the cost function. Based on this, the optimization problem in a nonconvex fractional form is transformed into an equivalent optimization problem with a parametric subtractive form, which is still nonconvex. Thus, the objective function is lower bounded by a concave function, which leads to the availability of standard convex theory, and finally, an iterative resource allocation algorithm is proposed. Moreover, we obtain the closed-form solutions using the Lambert W function for some special channel cases. Numerical results validate the benefits of the proposed scheme and illustrate the tradeoff between training and data transmission. [ABSTRACT FROM PUBLISHER]

In this paper, the joint transceiver design issue is investigated for multi-cell systems with multiple mobile terminals (MTs). An optimization problem is formulated based on the criterion of mean squared error minimization for the joint design of the cooperative precoder of all multi-input multi-output (MIMO) cells and the cooperative decoder of all MIMO MTs. With a fixed decoder, the optimum precoder is first derived in a closed-form expression, where no exchange of data streams among all of the MIMO cells is required due to the limited capability of the backhaul. Subsequently, an optimum decoder is then obtained in an analytical expression with the fixed precoder. Based on these two expressions, an iterative algorithm with guaranteed convergence is finally developed to carry out the joint optimization of the cooperative precoders and decoders. Simulation results demonstrating the effectiveness of the proposed algorithm are also provided. [ABSTRACT FROM AUTHOR]

Wireless energy transmission is considered in the Internet-of-Things (IoT) dual-hop communication systems. The power splitting method is employed at the relay node to receive the wireless energy and the wireless information simultaneously from the source to the relay node in the first resource block. With the harvested energy at the relay node, one wireless information node and one wireless energy harvest node are served simultaneously by the relay node in the second resource block. The optimization problem is formulated to maximize the energy efficiency defined as the ratio of the wireless information throughput of the first destination node over the power consumed by the whole IoT systems, which is subject to the required minimum energy harvested at the second destination node of the energy transfer. To tackle the non-convex problem, the exact expression of the wireless information throughput is approximated by the high signal to noise ratio (SNR) approximation method, which is shown very tight to the exact expression. Hence, the asymptotically optimal solution is derived in analytical expression by employing the Lagrangian method. Numerical simulations verify the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]