Joint Beamforming for Integrated Satellite-Terrestrial ISAC Systems.

In this paper, a comprehensive comparison analysis in terms of outage probability and average symbol error ratio (SER) is presented for cooperative cognitive multiple-input and multiple-output (CC-MIMO) multiuser systems with amplify-and-forward (AF) protocol. Specially, we consider two scenarios where the CC-MIMO multiuser systems have the perfect and imperfect channel state information (CSI). The CC-MIMO multiuser systems consist of one multi-antenna source, one single-antenna relay, and multiple multi-antenna destinations. At the secondary source and destinations, the maximal ratio transmission (MRT) and maximal ratio combining (MRC) are employed, respectively. For such CC-MIMO multiuser systems, we first obtain the exact closed-form expressions of outage probability under the two cases where the CC-MIMO multiuser systems have the perfect and imperfect CSI. Then, to reduce the implementation complexity, the tight lower bounds of outage probability and average SER are derived. Finally, to obtain insight, by using the high signal-to-noise ratio (SNR) approximation, the asymptotic estimations of outage probability are achieved. The numerical results show that the derivations are agreed with the simulations, which validate our derivations. At the same time, the results show that, for the systems without perfect CSI, the achievable diversity order reduces to one, regardless of the number of antennas at the cognitive source and destinations as well as the number of the cognitive destinations. Nevertheless, these key parameters affect the coding gain of the CC-MIMO multiuser systems. When the systems have the perfect CSI (or without feedback delay), the achievable diversity gain is determined by the minimum between the number of source’s antennas and the product of the number of destinations and the number of destination’s antennas. For the effect of PU’s parameters, our results indicate that primary systems only affect the coding gain but not the diversity gain.

The design of spatio-temporal power allocation schemes is considered for space-time coding over spatially correlated multiple-input multiple-output (MIMO) channels. The focus is on linear dispersion (LD) space-time codes that are constructed to maximize the mutual information between the input of the space-time encoder and the output of the channel. While perfect channel state information (CSI) is assumed at the receiver, three cases are considered for the CSI at the transmitter: 1) perfect CSI is available, 2) only statistical CSI is available, and 3) partial CSI in the form of a B-bit quantized channel information along with the statistical information is available. In all the three cases, it is shown that the optimal temporal power allocation is uniform. The optimal spatial power allocation for the case where only statistical CSI is available was studied previously in [1] where it was shown to be a nontrivial function of the spatial correlation. Here, the cases of perfect and partial CSI are studied. For the perfect CSI case, it is shown that it is optimal to excite only one spatial mode. For the partial and statistical CSI cases, the optimal allocation excites multiple modes, in general. However, it is attractive to use a low-complexity scheme that excites only the dominant spatial mode. We show that this low-complexity scheme is near-optimal in two settings: 1) large receive antenna asymptotics, and 2) for fixed antenna dimensions, when the transmit and receive covariance matrices are ill- and well-conditioned, respectively. Based on the optimal schemes for the extreme cases of perfect and statistical CSI, low-complexity spatial power allocation for the case of partial CSI is considered. Simulation results indicate that even in this case, exciting one spatial mode leads to a minimal loss in performance over the optimal spatial power allocation scheme.

In this paper, the aim is to study the robustness against imperfect channel state information (CSI) of the power allocation policies maximizing the constrained and non-convex Shannon rate problem in a relay-aided cognitive radio network. The primary communication is protected by a Quality of Service (QoS) constraint and the relay only helps the secondary communication by performing complex and non-linear operations. First, we derive the optimal power allocation policies under Compress-and-Forward (CF) relaying under perfect CSI. Second, we investigate the robustness of this solution jointly with that of the deep learning existing solution for Decode-and-Forward (DF), which we exploit here for CF as well. For all these solutions that strongly rely on perfect CSI, our numerical results show that errors in the channel estimations have a damaging effect not only on the secondary rate, but most importantly on the primary QoS degradation, becoming prohibitive for poor quality estimations. Nevertheless, we show that the deep learning solutions can be made robust by adjusting the training process to rely on both perfect and imperfect CSI observations. Indeed, the resulting predictions are capable of meeting the primary QoS constraint at the cost of secondary rate loss, irrespective from the channel estimation quality.

Interference alignment (IA) promises substantial theoretical gain to achieve the maximum degrees of freedom (DoF) in interference channel. However, perfect global channel state information (CSI) is required, which is difficult to achieve in practical wireless communication system. Imperfect channel knowledge severely degrades the gain of IA, this paper investigates the performance of IA with limited feedback and CSI delay. Given imperfect CSI caused by the limited feedback and channel delay, the expression of data rate is derived. Compared to the rate with perfect CSI, a rate loss upper bound is obtained by analytical derivation. Furthermore, we show that if the time delay and signal-to-noise-ratio (SNR) satisfy a certain relationship, the system rate loss can maintain a constant value, and with SNR increases, optimal transmission DoF can be obtained. Extensive simulations demonstrated that the rate loss reduces when the number of feedback bits increases and the channel delay decreases. Meanwhile, as SNR increases, the system performance with imperfect CSI improves more slowly than with perfect CSI.

This paper investigates a decode-and-forward two-hop relaying system consisting of one source, one relay and one destination, in which orthogonal frequency division multiplexing is used. The relay forwards the message received from the source on a subset of available subcarriers in the second time slot. Firstly, a subcarrier pairing and selection algorithm is proposed, assuming that perfect channel state information (CSI) is available at all nodes, then, power is allocated to both the source and relay stations under individual power constraints in order to maximize the capacity. Secondly, subcarrier selection and pairing, and power allocation (PA) under partial CSI assumption along with individual power constraints are addressed. The result is a novel distributed algorithm with low complexity maximizing the expected value of capacity at the source and relay nodes. Finally, the simulation results show that selective relaying combined with subcarrier pairing and PA improves the system capacity to a considerable extent in both perfect and partial CSI cases.

MIMO systems can provide significant increases in capacity and bandwidth efficiency as well as improvement in the QoS in wireless communications. However, multiple RF chains connected to multiple antennas are usually more expensive and complex than antenna elements themselves. Selective space-time coding and selection diversity can be viewed as practical means to reduce the implementation complexity of MIMO systems while still taking benefit of the use of multiple antennas. In this paper, we evaluate the performance of selective space-time block coding and antenna selection diversity and analyze the performance of both schemes under perfect and imperfect channel state information (CSI) available at both ends of the transmission link. Our performance analysis reveals that, under perfect CSI, selective space-time coding diversity yields a loss in selection diversity gains when combined with space-time coding, and that selecting just a single antenna at the transmitter side to transmit data is the best strategy in this CSI case of operation. We also show that selection diversity still outperforms selective space-time coding diversity when the channel estimation is imperfect.

ABSTRACTIn this paper, the problem of user pairing across subcarriers is investigated for the uplink non‐orthogonal multiple‐access (NOMA) systems, in which every subcarrier is assigned to exactly two users. Considering fixed transmit power for all users, we pair users such that sum‐rate is maximized over all subcarriers. While different (near) optimum allocation schemes exist in the literature, they pertain to perfect channel state information (CSI), which is impractical. To overcome this limitation, we assume statistical CSI is available in the form of the first two moments of channel gains. Then, we maximize a lower bound on the sum rate over user pairings utilizing a block coordinate ascent (BCA) approach with guaranteed convergence to a suboptimal solution. Numerical results corroborate a satisfactory performance for our proposed algorithm versus the perfect CSI benchmark and a simple random assignment approach. For high signal to noise ratios (SNRs) and the more challenging frequency selective fading setup, the solution of our proposed algorithm comes close to those of the optimal pairing with perfect CSI. Furthermore, simulations revealed that if the proposed BCA is utilized assuming perfect CSI, it performs similar to the well‐known global optimum in the frequency flat fading setup and outperforms the best existing near‐optimal solution in the frequency selective fading scenario. These observations offer a testament to the strength of the proposed BCA algorithm in both perfect and imperfect CSI conditions.

With interference alignment (IA), the achievable degrees of freedom (DoFs) in wireless networks can be linearly scaled up with the number of users. However, to attain the full DoF, the availability of perfect network channel state information (CSI) is mandatory, which is not pragmatic in general. In this paper, we quantify the performance of IA under CSI mismatch where the variance of the CSI measurement error depends on the signal-to-noise ratio (SNR). We show that when this error variance is proportional to the inverse of the SNR, the full DoF is achievable, and an upper bound on asymptotic mean loss in sum rate compared with the perfect CSI case is derived. We also derive a bound on the achievable DoF when the CSI error variance is proportional to the inverse of the SNR to a power of a constant. Furthermore, we investigate the effect of CSI mismatch on the performance of the maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. We show that with perfect CSI, the Max-SINR algorithm outperforms interference leakage minimization algorithms, but with the presence of imperfect CSI, its comparative improvement becomes negligible. We then propose an adaptive Max-SINR algorithm that can notably improve the performance of the original Max-SINR algorithm under CSI mismatch.

This paper investigates covert communication in multi-user integrated sensing and communication (ISAC) systems, where a base station (BS) detects target and secretly transmits confidential signals to multiple users under the surveillance of an adversarial warden (Willie), and a mobile friendly jammer is deployed to transmit jamming signals to enhance covertness. The closed-form expression of Willie's detection error probability (DEP) is derived as a metric of covertness performance, and the joint beamforming and jamming power optimization problem is formulated to balance sensing performance and average covert transmission rate. Considering both perfect and imperfect channel state information (CSI) scenarios, a two-step algorithm composed of Rayleigh quotient derivation and successive convex approximation (SCA) is proposed to solve the non-convex multi-variable problem. Numerical simulations validate the theoretical analysis, and demonstrate the benefits of a jammer to the covertness of the ISAC system compared with max ratio transmission (MRT) and zero-forcing (ZF) methods.

In the paper, we consider the imperfect channel state information (CSI) in practical cognitive MIMO systems. We first analyze the feedback of quantified CSI from the primary user (PU) and propose a joint power allocation and beamforming algorithm via game theory. Compared with the game under the condition of perfect CSI, new utility function and cost function are constructed under imperfect CSI. We analyze the error introduced from the uniformly quantified CSI and obtain new constraints. Besides, existence of the Nash equilibrium in case of both perfect CSI and imperfect CSI are proven. We propose a new iterative algorithm to reach the Nash equilibrium (NE). Simulation results show that the proposed algorithm can converge quickly.

In this paper, we consider multiuser multiple-input single-output (MISO) interference channel where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. The transmit beamforming vectors and receive power splitting (PS) ratios are jointly designed in order to minimize the total transmission power subject to both signal-to-interference-plus-noise ratio (SINR) and EH constraints. Most joint beamforming and power splitting (JBPS) designs assume that perfect channel state information (CSI) is available; however CSI errors are inevitable in practice. To overcome this limitation, we study the robust JBPS design problem assuming a norm-bounded error (NBE) model for the CSI. Three different solution approaches are proposed for the robust JBPS problem, each one leading to a different computational algorithm. Firstly, an efficient semidefinite relaxation (SDR)-based approach is presented to solve the highly non-convex JBPS problem, where the latter can be formulated as a semidefinite programming (SDP) problem. A rank-one recovery method is provided to recover a robust feasible solution to the original problem. Secondly, based on second order cone programming (SOCP) relaxation, we propose a low complexity approach with the aid of a closed-form robust solution recovery method. Thirdly, a new iterative method is also provided which can achieve near-optimal performance when the SDR-based algorithm results in a higher-rank solution. We prove that this iterative algorithm monotonically converges to a Karush-Kuhn-Tucker (KKT) solution of the robust JBPS problem. Finally, simulation results are presented to validate the robustness and efficiency of the proposed algorithms.

In this paper, we investigate joint optimization of secure AirComp and reliable multicasting assisted by a multiple-input multiple-output untrusted two-way relay, where artificial noise is employed at the access point (AP) to interfere the relay for ensuring secure AirComp. We aim to minimize the computation distortion at the AP by jointly designing the transmit variables of all the nodes and the aggregation variables at the AP and relay, under the secure AirComp constraint, the reliable multicasting constraint, and the transmit power constraints of all the nodes. We consider two scenarios that perfect and imperfect channel state information (CSI) are available. For the former, the formulated optimization problem is highly nonconvex, and we propose an effective block coordinate descent (BCD)-penalty successive convex approximation (penaltySCA) method to solve the nonconvex problem. For the latter, we model the imperfect CSI by using worst-case criterion and the formulated robust optimization problem is much more challenging than its counterpart with perfect CSI. To solve the robust problem effectively, we first transform it to a deterministic optimization problem by employing some powerful mathematical lemmas, and then apply the proposed BCD-penaltySCA method to solve the reformulated deterministic problem. The proposed methods are shown by simulations to significantly reduce the computation distortion compared with other benchmarks under considering secure AirComp and reliable multicasting.

Optimization of secure wireless communications for IoT networks in the presence of eavesdroppers

The performance analysis of a space-time coded multiple-input multiple-output (MIMO) system with variable-rate adaptive modulation over flat Rayleigh fading channels for both perfect and imperfect channel state information (CSI) is presented. In this study, the optimum fading gain switching thresholds for attaining maximum spectrum efficiency (SE) subject to an average bit error rate (BER) constraint are derived. The existence and uniqueness of the Lagrange multiplier in the constrained SE optimisation is studied. It is shown that the Lagrange multiplier does exist and is unique for imperfect CSI. On the other hand, the Lagrange multiplier will be unique if the existence condition for MIMO under perfect CSI is satisfied. A practical iterative algorithm based on Newton's method for finding the Lagrange multiplier is proposed. By the switching thresholds, closed-form expressions of the SE and average BER are obtained. Simulation results for SE and BER are in good agreement with the theoretical analysis. The results show that the space-time block coded MIMO system using adaptive modulation (AM-STBC-MIMO) with average BER constraint provides SE better than AM-STBC-MIMO with fixed thresholds, and AM-STBC-MIMO using a BER upper bound, but it has performance degradation in SE for imperfect CSI.

Cooperative spectrum sensing (CSS) is an efficient method to detect vacant spectrum of a primary user (PU) by combining sensing information of multiple cognitive radios (CRs) in presence of fading. In this paper, effects of perfect and imperfect channel state information (CSI) on CSS network is evaluated. The proposed network is operated over Nakagami-q (Hoyt) and Nakagami-n (Rician) fading affecting both reporting (R) and sensing (S) channels. The CRs which employ energy detectors (EDs) are selected on the basis of CSI between them and a fusion center (FC). The knowledge on the quality of R-channels is estimated at FC using a channel estimator. The estimated CSI is either perfect when there is no error in the estimator, or imperfect when errors are present. Accordingly, CRs are selected under both perfect CSI and imperfect CSI cases. All CRs use the decision statistics obtained by EDs and make one-bit binary decisions about the availability of a PU. Selected CRs only transmit decision information to the FC. The miss detection probability and error rate of network under two cases of selection are evaluated by operating majority and maximal ratio combining rules at the FC. Performance is analyzed for different channel and network parameters and comparison between fusion rules for different fading channels is also highlighted.