We introduce a Channel Distribution Information (CDI)-aware Generative Adversarial Network (GAN), designed to address the unique challenges of adversarial attacks in wireless communication systems. The generator in this CDI-aware GAN maps random input noise to the feature space, generating perturbations intended to deceive a target modulation classifier. Its discriminators play a dual role: one enforces that the perturbations follow a Gaussian distribution, making them indistinguishable from Gaussian noise, while the other ensures these perturbations account for realistic channel effects and resemble no-channel perturbations. This approach crafts a new perturbation for each channel use, making it harder to detect using pilot signals. Our proposed CDI-aware GAN can be used as an attacker and a defender. As a defender, it significantly increases the resilience of the legitimate modulation classifier against adversarial attacks, outperforming known methods. Furthermore, as an attacker, the CDI-aware GAN effectively deceives the target classifier while transmitting a new perturbation signal for each channel use.

We show the importance of designing and using application-specific, parameterized, custom loss functions in predictive Deep Neural Network (DNN) applications. We consider a cellular traffic prediction application in next-generation communication systems where predicting less than the actual traffic value is more harmful than predicting more than the actual traffic value. Cellular network operators aim to maintain service-level agreements (SLAs) that carry high penalty costs for violations, i.e., negative prediction errors. Conversely, the cost of overprovisioning, i.e., positive prediction errors, is less significant to operators compared to SLA violations. Standard loss functions, being symmetric, cannot account for the differing importance of positive and negative prediction errors. In this work, we introduce and compare several custom loss functions where the weight of the negative prediction error is determined by the SLA violation rate. We find that our proposed weighted Mean Exponential Error (wMEE) based loss function results in the smallest overprovisioning volume while adhering to a given SLA violation rate. More crucially, our findings suggest that custom loss function parameter design should be incorporated into the hyperparameter tuning process for DNN applications.

We consider the average age of information in G/G/1/1 systems under two service discipline models. In the first model, if a new update arrives when the service is busy, it is blocked; in the second model, a new update preempts the current update in service. For the blocking model, we first derive an exact age expression for G/G/1/1 systems. Then, using the age expression for G/G/1/1 systems, we calculate average age expressions for special cases, i.e., M/G/1/1 and G/M/1/1 systems. We observe that deterministic interarrivals minimize the average age of G/M/1/1 systems for a given mean interarrival time. Next, for the preemption in service model, we first derive an exact average age expression for G/G/1/1 systems. Then, similar to blocking discipline, using the age expression for G/G/1/1 systems, we calculate average age expressions for special cases, i.e., M/G/1/1 and G/M/1/1 systems. Average age for G/M/1/1 can be written as a summation of two terms, the first of which depends only on the first and second moments of interarrival times and the second of which depends only on the service rate. In other words, interarrival and service times are decoupled. We prove that deterministic interarrivals are optimum for G/M/1/1 systems for a given mean interarrival time. On the other hand, we observe for non-exponential service times that the optimal distribution of interarrival times depends on the relative values of the mean interarrival time and the mean service time. Finally, we propose a simple to calculate upper bound to the average age for the preemption in service discipline.

We study age of information in a multiple source-multiple destination setting with a focus on its scaling in large wireless networks. There are n nodes uniformly and independently distributed on a fixed area that are randomly paired with each other to form n source-destination (S-D) pairs. Each source node wants to keep its destination node as up-to-date as possible. To accommodate successful communication between all n S-D pairs, we first propose a three-phase transmission scheme which utilizes local cooperation between the nodes along with what we call mega update packets to serve multiple S-D pairs at once. We show that under the proposed scheme average age of an S-D pair scales as O(n1/4logn) as the number of users, n, in the network grows. Next, we observe that communications that take place in Phases I and III of the proposed scheme are scaled-down versions of network-level communications. With this along with scale-invariance of the system, we introduce hierarchy to improve this scaling result and show that when hierarchical cooperation between users is utilized, an average age scaling of O(nα(h)logn) per-user is achievable, where h denotes the number of hierarchy levels and α(h) = 1/3·2h+1. We note that α(h) tends to 0 as h increases, and asymptotically, the average age scaling of the proposed hierarchical scheme is O(logn). To the best of our knowledge, this is the best average age scaling result in a status update system with multiple S-D pairs.

Given n randomly located source-destination (S- D) pairs on a fixed area network that want to communicate with each other, we study the age of information with a particular focus on its scaling as the network size n grows. We propose a three- phase transmission scheme that utilizes hierarchical cooperation between users along with mega update packets and show that an average age scaling of O(nα(h)log n) per-user is achievable where h denotes the number of hierarchy levels and α(h) = 1 / (3.2h+1) which tends to 0 as h increases such that asymptotically average age scaling of the proposed scheme is O(log n). To the best of our knowledge, this is the best average age scaling result in a status update system with multiple S-D pairs.

We study age of information in a multiple source-multiple destination setting with a focus on its scaling in large wireless networks. There are n nodes that are randomly paired with each other on a fixed area to form n source-destination (SD) pairs. We propose a three-phase transmission scheme which utilizes local cooperation between the nodes by forming what we call mega update packets to serve multiple S-D pairs at once. We show that under the proposed scheme average age of an S-D pair scales as O(n1/4log n) as the number of users, n, in the network grows. To the best of our knowledge, this is the best age scaling result for a multiple source-multiple destination setting.

In this paper, we consider non-overlay planning of microcells in a heterogeneous cellular network. In this approach, in order to minimize overlaps between microcell and macrocell service areas, macrocells lower their common pilot channel power as the number of microcells around the hexagonal site edges increases. Under such a deployment assumption, we solve an energy efficiency optimization problem over certain parameters like the Inter Site Distance (ISD), the number of microcells per macrocell and the size of microcells. We assume coverage and spectral efficiency constraints and investigate the effect of user density on the optimization problem. The results show that the optimum number of microcells depends heavily on the user density and the target area spectral efficiency.

In this paper, we introduce nonoverlay microcell/macrocell planning that is optimally designed for improving energy efficiency of the overall heterogeneous cellular network. We consider two deployment strategies. The first one is based on a fixed hexagonal grid and the second one is based on a stochastic geometry. In both of our models, microcells are placed in those areas where the received signal power levels of macrocell common pilot channels are below a certain threshold. Thus, interference between microcells and macrocells is minimized. As a result, addition of microcells increases the achieved number of bits per unit energy. Under such deployment assumptions, we investigate the effects of certain parameters on the energy efficiency. These parameters include the user traffic, the Intersite Distance (ISD), the size of microcells and the number of microcells per macrocell for the grid model, and macrocell density and microcell density for the stochastic model. The results of our performance analyses show that utilizing microcells in a sparse user scenario is worse for the energy efficiency whereas it significantly improves both energy and spectral efficiencies in a dense user scenario. Another interesting observation is that it is possible to choose an optimum number of microcells for a given macrocell density.

Due to growing power consumption and data-rate demand in cellular networks, it is crucial to design the future networks with energy and spectral efficient strategies. Using heterogeneous networks (HetNets) is one possible way of improving energy and spectral efficiency in cellular networks. In this paper, under a coverage constraint, we consider deploying strategies of low power microcells in order to improve the energy and area spectral efficiencies of the conventional macrocell only cellular network. We start our analysis with accurate power consumption models for both macro and micro base stations. Then, we analyze the energy and area spectral efficiencies with respect to the number and size of microcells. We observe that deploying microcells while keeping the coverage area the same increases both energy and spectral efficiencies. We also observe that microcell deployment strategy depends on the inter-site distance (ISD). Finally, we conclude that it is possible to choose a range of ISD that maximizes the energy efficiency while providing the required traffic demand.

In this paper, we consider a single user block fading communication system where both the transmitter and receiver have multiple antennas. A transmission block is divided into training and data transmission phases. The channel estimation at the receiver is noisy. The transmitter has long term statistics of the estimated channel in the form of a covariance matrix. The general problem is to find optimum transmit covariance matrix under these channel state information (CSI) assumptions. Beamforming strategy is optimum when the optimum transmit covariance matrix is unit rank. Previous works find conditions under which beamforming is optimal when the receiver has instantaneous and full CSI. Here, we consider noisy CSI at the receiver and introduce a condition over channel covariance matrix, block length and the transmit power constraint, where beamforming is always the optimum transmission strategy. We show through simulations that beamforming is optimal for a larger set of parameters when block duration is short and when SNR is low. An interesting result we observe through simulations is that beamforming is always optimal when transmission block is at most three symbols long. This means that when block fading becomes faster, beamforming becomes optimal almost always regardless of number of antennas or other parameters.

In this paper, we consider a full-duplex, decode-and-forward, fading MIMO relay channel where the transmitters have partial channel state information (CSI) and the receivers have noisy channel estimates. Under block fading assumption, we divide the transmission block into two parts: training phase and data transmission phase. The destination and relay receivers estimate the instantaneous channel realizations during the training phase by using linear minimum mean square error (MMSE) estimation, extract the covariance information of the channel, and feed it back to the source and relay transmitters. We obtain a lower bound expression to the relay channel capacity in terms of a max-min optimization problem over channel estimation and data transmission parameters. By applying matrix differential calculus, we jointly optimize this achievable rate over source and relay transmit covariance matrices, training phase length, training phase powers and training sequences.

In this paper, the source and relay transmit covariance matrices are jointly optimized for a fading multiple antenna relay channel when the transmitters only have partial channel state information (CSI) in the form of covariance feedback. For full-duplex transmission, we evaluate lower and upper bounds on the ergodic channel capacity. These bounds require a joint optimization over the source and relay transmit covariance matrices. The methods utilized in the previous literature fail to find fast and efficient algorithms for the system model considered in this paper. Therefore, we utilize matrix differential calculus in order to solve the joint optimization problem. In this method, there is no need to specify the eigenvectors of the transmit covariance matrices first. Through simulations, we observe that lower and upper bounds are close to each other.

In this paper, we consider a full-duplex mode MIMO relay channel for decode and forward strategy where the receivers have perfect channel state information (CSI) and the transmitters have only covariance feedback at the transmitters. We derive a lower bound to the ergodic capacity for this scenario and propose an iterative algorithm that finds lower bound achieving transmit covariance matrices of the source and relay nodes. The solution of the optimization problem in the lower bound expression is given for three cases depending on channel covariance matrices. For one of these cases, it seems that an efficient and fast algorithm achieving the lower bound is not possible. However, under a certain practical channel assumption, we propose a power allocation algorithm that gives a solution much faster than classical convex optimization methods. Moreover, we show that this fast algorithm results in a data rate which is very close to the lower bound to the capacity.

We consider a point-to-point, correlated and block fading multiple antenna channel where the receiver has a noisy channel estimation and the transmitter has the statistics of the channel. Although very high rates are promised when the receiver knows the channel perfectly, the capacity is not known when the receiver has a noisy channel estimation. In this paper, we will derive an upper bound to the capacity of the system that we consider, and analyze the tightness of this bound and previously derived lower bounds with respect to block length and total average power. We observe that the lower and upper bounds converge with increasing block length, however there is a non-vanishing difference between the bounds with increasing total average power.

Multiple antenna systems are known to provide very large data rates, when the perfect channel state information (CSI) is available at the receiver. However, this requires the receiver to perform a noise-free, multi-dimensional channel estimation, without using communication resources. In practice, any channel estimation is noisy and uses system resources. We shall examine the trade-off between improving channel estimation and increasing the achievable data rate. We consider transmit side correlated multi-input multi-output (MIMO) channels with block fading, where each block is divided into training and data transmission phases. The receiver has a noisy CSI that it obtains through a channel estimation process, while the transmitter has partial CSI in the form of covariance feedback. In Part I of this two-part paper, we consider the single-user case, and optimize the achievable rate jointly over parameters associated with the training phase and data transmission phase. In particular, we first choose the training signal to minimize the channel estimation error, and then, develop an iterative algorithm to solve for the optimum system resources such as time, power and spatial dimensions. Specifically, the algorithm finds the optimum training duration, the optimum allocation of power between training and data transmission phases, the optimum allocation of power over the antennas during the data transmission phase.

This is the second part of a two-part paper on the joint channel estimation and resource allocation problem in MIMO systems with noisy channel estimation at the receiver side and partial CSI, in the form of covariance feedback, available at the transmitter side.We consider transmit-side correlated MIMO channels with block fading, where each block is divided into training and data transmission phases. In this paper, we extend the single-user results of Part I to the multiple access channel. For the data transmission phase, we propose an iterative algorithm to solve for the optimum system resources such as time, power and spatial dimensions. This algorithm updates the parameters of the users in a round-robin fashion. In particular, the algorithm updates the training and data transmission parameters of a user, when those of the rest of the users are fixed, in a way to maximize the achievable sum-rate in a multiple access channel; and iterates over users in a round-robin fashion. Finally, we provide a detailed numerical analysis to support the analytical results of both parts of this two-part paper.

We consider the sum capacity of a multi-input multi-output (MIMO) multiple access channel (MAC) where the receiver has the perfect channel state information (CSI), while the transmitters have either no or partial CSI. When the transmitters have partial CSI, it is in the form of either the covariance matrix of the channel or the mean matrix of the channel. For the covariance feedback case, we mainly consider physical models that result in single-sided correlation structures. For the mean feedback case, we consider physical models that result in in-phase received signals. Under these assumptions, we analyze the MIMO-MAC from three different viewpoints. First, we consider a finite-sized system. We show that the optimum transmit directions of each user are the eigenvectors of its own channel covariance and mean feedback matrices, in the covariance and mean feedback models, respectively. Also, we find the conditions under which beamforming is optimal for all users. Second, in the covariance feedback case, we prove that the region where beamforming is optimal for all users gets larger with the addition of new users into the system. In the mean feedback case, we show through simulations that this is not necessarily true. Third, we consider the asymptotic case where the number of users is large. We show that in both no and partial CSI cases, beamforming is asymptotically optimal. In particular, in the case of no CSI, we show that a simple form of beamforming, which may be characterized as an arbitrary antenna selection scheme, achieves the sum capacity. In the case of partial CSI, we show that beamforming in the direction of the strongest eigenvector of the channel feedback matrix achieves the sum capacity. Finally, we generalize our covariance feedback results to double-sided correlation structures in the Appendix.

We consider correlated MIMO multiple access channels with block fading, where each block is divided into training and data transmission phases. We find the channel estimation and data transmission parameters that jointly optimize the achievable data rate of the system. Our results for the training phase are particularly interesting, where we show that the optimum training signals of the users should be non-overlapping in time. For the data transmission phase, we propose an iterative algorithm that updates the parameters of the users in a round-robin fashion. In particular, the algorithm updates the training and data transmission parameters of a user, when those of the rest of the users are fixed, in a way to maximize the achievable sum-rate in a multiple access channel; and iterates over users in a round-robin fashion.

Cognitive radios have the ability to sense their RF environment and adapt their transmission parameters to perform optimally in any situation. Part of this involves selecting the best modulation type for a particular channel. In this paper we consider a variable-rate, variable-power, adaptive, m-ary quadrature amplitude modulation (M-QAM) scheme in a single-user communication scenario. The channel between the transmitter and receiver is assumed to be a Rayleigh block-fading channel. Each block is divided into training and data phases. During the training phase, the receiver estimates the channel and feeds the estimate back to the transmitter. During the data phase, the transmitter sends its message by adapting the size of the M- QAM constellation. We first find a closed-form expression that relates the bit error rate (BER) to the constellation size of the M-QAM, and therefore to the data rate of our system. Then, for a given target BER, we maximize the data rate over the training parameters, which are the training signal, the training duration, and the training power. When these optimum parameters are used in a MATLAB implementation, we find that the target BER is matched to within an order of magnitude, and the resulting data rate is close to the theoretical limit.

We consider both the single-user and the multi-user power allocation problems in MIMO systems, where the receiver side has the perfect channel state information (CSI), and the transmitter side has partial CSI, which is in the form of covariance feedback. In a single-user MIMO system, we consider an iterative algorithm that solves for the eigenvalues of the optimum transmit covariance matrix that maximizes the rate. The algorithm is based on enforcing the Karush-Kuhn-Tucker (KKT) optimality conditions of the optimization problem at each iteration. We prove that this algorithm converges to the unique global optimum power allocation when initiated at an arbitrary point. We, then, consider the multi-user generalization of the problem, which is to find the eigenvalues of the optimum transmit covariance matrices of all users that maximize the sum rate of the MIMO multiple access channel (MIMO-MAC). For this problem, we propose an algorithm that finds the unique optimum power allocation policies of all users. At a given iteration, the multi-user algorithm updates the power allocation of one user, given the power allocations of the rest of the users, and iterates over all users in a round-robin fashion. Finally, we make several suggestions that significantly improve the convergence rate of the proposed algorithms.