Resource Allocation for Secure OFDMA Networks with Imperfect CSIT

Multiple antennas can be used for increasing the packet reliability (spatial diversity) or the spectral efficiency (spatial multiplexing) in wireless communication systems with slow fading. Zheng and Tse showed that both types of gains can be simultaneously obtained for a given multiple antenna channel, but there is a fundamental tradeoff between spatial diversity and spatial multiplexing. They considered the case of no CSIT but perfect CSIR. In this paper, we focus on the role of channel state information at the transmitter (CSIT) on the diversity multiplexing tradeoff. When there is perfect CSIT, there is no packet outage in slow fading channels through proper rate adaptation and hence, the diversity order can be regarded as infinity. However, perfect CSIT is not realistic. Thus, we consider the diversity and multiplexing tradeoff with imperfect CSIT for slow fading MIMO channels. The imperfect CSIT is parameterized by the CSIT estimation error sigma2 DeltaH. Hence, the results obtained are the generalizations of the Zheng-Tse results. We show that when SNRsigma2 DeltaH rarr infin as SNR rarr infin, the availability of the imperfect CSIT does not improve the diversity multiplexing tradeoff

This paper addresses the performance analysis of an adaptive wireless link with one antenna transmitter and a multiple antenna maximum-ratio combining (MRC) receiver. Two main assumptions are used in this paper: (1) Rayleigh fading correlated channels (i.e., MRC branch correlation) and (2) imperfect (outdated) channel state information at the transmitter (CSIT) side. The main contribution of this work lies in the derivation of analytic expressions (in terms of a series expansion) of the statistics of correct packet reception conditional on the decisions made by the transmitter based on outdated CSIT. The novelty of this derivation is the joint modelling of spatially correlated branches, imperfect CSIT, and adaptive modulation based on threshold-trigger decision. Contrary to common belief, the results presented here suggest that spatial correlation not always affects the performance of the MRC receiver: at low signal-to-noise ratio (SNR), correlation can improve performance rather than degrading it. In contrast, at high SNR, correlation is found to always degrade performance. At high SNR, correlation tends to worse the degrading effects of imperfect CSIT, particularly when the number of antennas increases. Imperfect CSIT causes errors in the assignment of MCSs, thus reducing throughput performance. These errors become more evident in the high SNR regime, particularly when the values of branch correlation and the number of antennas increase.

Application of multiple-input and multiple-output (MIMO) technology in wireless systems not only provides high spectral efficiency by the deployment of many number of antennas at both sides of a communication system for a given total transmit power, but it also enhances physical- layer security. Given the need for evaluation of realistic systems, this works studies the total information flow during multicast transmission in a region $\mathcal{R}$, accounting for the unavoidable statistical spatial randomness of users locations, both in absence or presence of eavesdroppers under conditions of imperfect channel state information at the transmitter (CSIT) due to users' mobility. In particular, we first introduce a lower-bound of the space-time mutual information without any eavesdropping, and then we proceed with the derivation for both finite and infinitely large number of antennas. Moreover, in case that eavesdropping is present, we determine a metric, called space-time secrecy rate with imperfect CSIT, which characterizes the total amount of common confidential information flow per receive antenna, and we obtain it with asymptotic arguments in terms of the numbers of antennas at each side. Numerical results, validated by simulations, shed light on how the imperfect conditions affect the performance. Remarkably, imperfect CSIT may present benefits, since it engenders degradation in eavesdropper's performance.

In this paper, resource allocation and scheduling in an orthogonal frequency division multiple access (OFDMA) downlink network is studied. The considered problem is modeled as an optimization problem which takes into account a multiple antenna eavesdropper and artificial noise generation for secure communications. Power, secrecy data rate, and subcarrier allocation are optimized for maximization of the average secrecy outage capacity (bit/s/Hz securely delivered to the users). The optimization problem is solved by using dual decomposition resulting in an efficient iterative resource allocation algorithm. Simulation results illustrate that the proposed iterative resource allocation and scheduling algorithm converges in a small number of iterations and guarantees a non-zero secrecy date rate even if the eavesdropper is closer to the base station than the desired users.

This paper addresses the performance of bit-interleaved coded multiple beamforming (BICMB) [1], [2] with imperfect knowledge of beamforming vectors. Most studies for limited-rate channel state information at the transmitter (CSIT) assume that the precoding matrix has an invariance property under an arbitrary unitary transform. In BICMB, this property does not hold. On the other hand, the optimum precoder and detector for BICMB are invariant under a diagonal unitary transform. In order to design a limited-rate CSIT system for BICMB, we propose a new distortion measure optimum under this invariance. Based on this new distortion measure, we introduce a new set of centroids and employ the generalized Lloyd algorithm for codebook design. We provide simulation results demonstrating the performance improvement achieved with the proposed distortion measure and the codebook design for various receivers with linear detectors.We show that although these receivers have the same performance for perfect CSIT, their performance varies under imperfect CSIT.

For slow fading channels, the MIMO performance is limited by packet outage even if powerful component codes are used at the transmitter. It is well-known that MIMO links in slow fading channels can benefit significantly through rate and power adaptations when there is knowledge of channel state information at the transmitter (CSIT). However, practical MIMO transmitter can only support finite combinations of the channel encoding rates and modulation constellation levels due to the finite number of bits allocated in the control overhead (embedded in the packet transmission) to inform the receiver of the current transmission mode. As a result of this practical limitation, there are several design issues concerning MIMO adaptation design. In this paper, we shall address various practical MIMO adaptation design issues such as how many different transmission modes are needed, how to determine the specific channel encoding rates and modulation constellation levels for each transmission modes as well as how to choose select a transmission mode for the current packet transmission given a particular CSIT. We propose a systematic design framework to address the MIMO adaptation design based on information theoretical framework. We assume time division duplex (TDD) systems with quasi-static fading channels where packet outage is an important concern with imperfect CSIT. The design problem is cast into an optimization problem similar to the classical vector quantization problem with a modified distortion measure. As an illustration, we apply the design framework to a 2 times 2 MIMO link and compare the goodput performance with the optimal performance predicted by the theory. We found that the goodput performance of the MIMO link predicted by the theoretical framework matches the actual performance closely

Rate Splitting (RS) is a versatile and powerful technique for multi-antenna transmission. In this paper, we study the precoding optimization for RS, which is critically important for improving the system performance but often challenging to address. We first investigate the non-convex sum rate maximization problem under perfect Channel State Information at the Transmitter (CSIT). By constructing a separable structure for the sum-of-functions-of-ratios problem and jointly leveraging the Convex Concave Procedure (CCP) and the Alternating Direction Method of Multipliers (ADMM), we obtain a fast algorithm that substantially reduces the overall computational time through the parallel computation and explicit closed-form solutions. We then investigate the average sum rate maximization problem under imperfect CSIT, which is known as a more challenging non-convex stochastic problem. To obtain a fast algorithm for practical use, we carefully approximate the non-convex stochastic problem to a non-convex deterministic one with acceptable performance loss and tailor the fast algorithm derived from perfect CSIT for imperfect CSIT with modest changes. Numerical results show that compared to the state-of-the-art algorithms, the proposed algorithms achieve comparable sum rates or average sum rates but short computation times for large problem sizes, owing to the unique parallel computation structures and few matrix inverse operations.

In this paper, resource allocation for energy efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs) is studied. The considered problem is formulated as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate for joint BS zero-forcing beamforming (ZFBF) transmission. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation solution is derived with a low complexity suboptimal subcarrier allocation policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency and network capacity.

Rate-Splitting Multiple Access (RSMA) has recently appeared as a powerful and robust multiple access and interference management strategy for downlink Multi-user (MU) multi-antenna communications. In this work, we study the precoder design problem for RSMA scheme in downlink MU systems with both perfect and imperfect Channel State Information at the Transmitter (CSIT) and assess the role and benefits of transmitting multiple common streams. Unlike existing works which have considered single-antenna receivers (Multiple-Input Single-Output–MISO), we propose and extend the RSMA framework for multi-antenna receivers (Multiple-Input Multiple-Output–MIMO) and formulate the precoder optimization problem with the aim of maximizing the Weighted Ergodic Sum-Rate (WESR). Precoder optimization is solved using Sample Average Approximation (SAA) together with the proposed vectorization and Weighted Minimum Mean Square Error (WMMSE) based approach. Achievable sum-Degree of Freedom (DoF) of RSMA is derived for the proposed framework as an increasing function of the number of transmitted common and private streams, which is further validated by the Ergodic Sum Rate (ESR) performance using Monte Carlo simulations. Conventional MU–MIMO based on linear precoders and Non-Orthogonal Multiple Access (NOMA) schemes are considered as baselines. Numerical results show that with imperfect CSIT, the sum-DoF and ESR performance of RSMA is superior to those of the two baselines, and is increasing with the number of transmitted common streams. Moreover, by better managing the interference, RSMA not only has significant ESR gains over baseline schemes but is more robust to CSIT inaccuracies, network loads and user deployments.

In this paper, we present robust joint non-linear transceiver designs for multiuser multiple-input multiple-output (MIMO) downlink in the presence of imperfections in the channel state information at the transmitter (CSIT). The base station (BS) is equipped with multiple transmit antennas, and each user terminal is equipped with one or more receive antennas. The BS employs Tomlinson-Harashima precoding (THP) for inter-user interference pre-cancellation at the transmitter. We consider robust transceiver designs that jointly optimize the transmit THP filters and receive filter for two models of CSIT errors. The first model is a stochastic error (SE) model, where the CSIT error is Gaussian-distributed. This model is applicable when the CSIT error is dominated by channel estimation error. In this case, the proposed robust transceiver design seeks to minimize a stochastic function of the sum mean square error (SMSE) under a constraint on the total BS transmit power. We propose an iterative algorithm to solve this problem. The other model we consider is a norm-bounded error (NBE) model, where the CSIT error can be specified by an uncertainty set. This model is applicable when the CSIT error is dominated by quantization errors. In this case, we consider a worst-case design. For this model, we consider robust i) minimum SMSE, ii) MSE-constrained, and iii) MSE-balancing transceiver designs. We propose iterative algorithms to solve these problems, wherein each iteration involves a pair of semi-definite programs (SDP). Further, we consider an extension of the proposed algorithm to the case with per-antenna power constraints.

In this paper we present a novel framework for cluster head (CH) selection and sub-carrier allocation towards intra-cluster communication in an Orthogonal Frequency Division Multiple Access (OFDMA) based Wireless Sensor Network (WSN). The proposed framework maximizes the overall performance of the WSN in terms of the throughput, video quality and network life. The OFDMA PHY optimization criterion for rate maximization is formulated as a bi-level optimization problem for CH node selection followed by optimal OFDMA sub-carrier allocation. It is then demonstrated that the above optimal resource allocation problem can be reduced to the solution of a weighted bipartite graph matching problem and is solved employing three different schemes, namely the popular Hungarian algorithm, game theory based multi-item auction and the Gale-Shapley stable matching scheme. Based on the above formulation, we also derive an optimal scheme to minimize the energy consumption in the WSN. Further, the performance of the proposed schemes is demonstrated through simulations in the context of scalable video transmission in a video sensor network. Results illustrate that these schemes significantly outperform suboptimal WSN cluster head selection schemes in terms of data rate and video quality.

In this paper, the resource allocation policy and algorithm for the uplink Orthogonal Frequency Division Multiple Access (OFDMA) network over the fading channel is studied. With the convex optimization theory, the optimal resource allocation is formulated as a weighted ergodic sum rate maximization problem. The theoretical ergodic capacity region with continuous rate adaption and discrete rate adaption is given subject to per-user power constraint. Simulation results show the ergodic capacity region for two-user uplink OFDMA network.

Energy efficiency (EE) is one fundamental performance metric for design of orthogonal frequency division multiple access (OFDMA) networks besides spectral efficiency (SE). The conventional resource allocation is to maximize data throughput without consideration of EE. In this paper, we propose a new low-complexity resource allocation algorithm to maximize EE, which first meet the quality-of-services (QoS) demand of every user and then assign the rest of subcarriers with criterion of energy efficiency. We also compare this new algorithm with the near-optimal EE algorithm, maximum-power-decrease-first (MPDF), which is proposed in [9]. The proposed algorithm differs from the MPDF algorithm in terms of the number of waterfilling for subcarrier allocation. The waterfilling operation in our proposed algorithm is not related to the number of users so that it reduces the computational complexity significantly. Finally, the simulation results show that the proposed algorithm approach the EE performance of the MPDF algorithm with lower complexity.

In this paper, we analyze optimal (in space and time) adaptive power transmission policies for fading channels when the channel-state information (CSI) at the transmitter (CSIT) and the receiver (CSIR) is available. The transmitter has a long-term (time) average power constraint. There can be multiple antennas at the transmitter and at the receiver. The channel experiences Rayleigh fading. We consider beamforming and space-time coded systems with perfect/imperfect CSIT and CSIR. The performance measure is the bit error rate (BER). We show that in both coded and uncoded systems, our power allocation policy provides exponential diversity order if perfect CSIT is available. We also show that, if the quality of CSIT degrades then the exponential diversity is retained in the low SNR region but we get only polynomial diversity in the high SNR region. Another interesting conclusion is that in case of imperfect CSIT and CSIR, knowledge of CSIT at the receiver is very important. Finally, for the optimal power control policy of the uncoded system we find the error-exponents which provide the rate versus diversity-order tradeoff for this policy. This tradeoff is of an entirely different nature than the well-known Zheng-Tse tradeoff.

SummaryIn this paper, we present a QoS‐driven energy efficient power and subcarrier allocation in green orthogonal frequency division multiple access networks. Our proposed scheme aims at maximizing the effective energy efficiency (EEE), defined as overall effective capacity per total power consumption, subject to given user delay‐QoS requirements and an average sum transmit power constraints. We analytically obtain the uniquely global optimal power and subcarrier using fractional programming, which transforms the fractional program into an equivalent parametric convex program that has a tractable solution. Analytical results show that the optimal solution has the same structure as effective capacity maximization and overall power minimization solutions. Simulation results show that there exists a trade‐off between EEE and delay. When the delay is more stringent, the EEE decreases. Moreover, when the number of subcarriers increase, EEE increases, where a fixed optimal EEE can be achieved regardless of stringency of delay‐QoS requirements. Furthermore, our proposed resource allocation provides significant EEE gains over both the independent resource allocation and overall power minimization schemes. Copyright © 2015 John Wiley & Sons, Ltd.