Saeedeh Parsaeefard

Cooperative Secure Resource Allocation in Cognitive Radio Networks with Guaranteed Secrecy Rate for Primary Users

In this paper, we introduce a new cooperative paradigm for secure communication in cognitive radio networks (CRNs) where secondary users (SUs) are allowed to access the spectrum of primary users (PUs) as long as they preserve the secure communication of PUs in the presence of malicious eavesdroppers. To do so, the SU transmission is divided into two hops: at first hop, the SU transmitter sends the information to a relay set and the SU receiver acts as a friendly jammer to disturb the overhearing of eavesdroppers and at the second hop, one of the relays is selected to pass the information to the SU receiver and the SU transmitter acts as a friendly jammer for the PU. In this new setup, the time duration for each hop, the power transmissions of all nodes in CRN, and relay selection at the second hop are allocated in such a way that the secrecy rate of the SU is maximized subject to the minimum required PUs secrecy rate. From primary service perspective, this transforms the possibly disturbing secondary service activities into a beneficial network element. We investigate instantaneous and ergodic resource allocation problems for perfect and imperfect channel state information (CSI). Since these problems are non-convex, we propose a solution based on decomposition of main optimization problem into three subproblems related to the power allocation, time allocation, and relay selection. We show that the power allocation problem can be transformed into a generalized geometric programming (GGP) model via the so-called scaled algorithm and it can be solved very efficiently. Simulation results indicate that in terms of the secondary secrecy rate, the proposed setup outperforms the conventional setup in which the secrecy rate of the PU is not guaranteed.

Secure Robust Ergodic Uplink Resource Allocation in Relay-Assisted Cognitive Radio Networks

We investigate the ergodic uplink resource allocation problem for secure communication in relay-assisted orthogonal frequency-division multiple access (OFDMA)-based cognitive radio networks (CRNs) in the presence of a set of passive eavesdroppers where relay nodes assist the legitimate users to transmit their messages. Previous works have commonly assumed the availability of channel state information (CSI) for this type of problems. However, due to the decentralized nature of CRNs and hidden activities of eavesdroppers, the assumption of availability of exact values of CSI is not realistic. In this paper, we consider uncertainty on the estimated values of CSI between different transmitters and receivers, e.g., CSI between each legitimate transmitter and its corresponding receiver and CSI of each legitimate user and each eavesdropper. We utilize the worst-case robust formulation to find power and sub-carrier allocations in such a way that under the worst condition of error, the regulatory constraints imposed to CRN are satisfied and the secrecy rate of each secondary legitimate user is stabilized. It is well known that the robust approaches impose a high computational complexity to the system and reduce the system performance as they conservatively consider the error in the maximum extent. We demonstrate how the robust formulation can be significantly simplified and tradeoff parameters can be introduced to moderate the effect of the worst-case approach. Simulation results are provided to demonstrate the performance of CRNs for different uncertain system parameters.

Improving Wireless Secrecy Rate via Full-Duplex Relay-Assisted Protocols

In this paper, we examine the use of a friendly full-duplex (FD) relay to increase the secrecy rate over a fading channel between the legitimate source and the destination in the presence of residual self-interference (SI) and eavesdropper. In particular, we consider two different protocols based on the FD capability of relay: 1) FD transmission (FDT), in which the FD-Relay receives and sends data concurrently; 2) FD-Relay with jamming (FDJ), where first, the FD-Relay simultaneously receives data and sends jamming to the eavesdropper; then, it forwards the data, while the source jams the eavesdropper. We first develop the secrecy rate expressions for half-duplex transmission (HDT), half-duplex with jamming (HDJ), FDT, and FDJ relaying protocols, and then use them to derive their performance properties in terms of the channel gains between nodes, eavesdropper types, and more importantly, the SI level in FD-Relay. We further investigate the non-convex power allocation problems for the developed FDT and FDJ to maximize the secrecy rate under the power constraints. In particular, we develop an efficient iterative algorithm based on the difference-of-two-concave-functions programming. Analytical and simulation results show the strong influence of SI level on the achieved secrecy rate of the FDT and the FDJ. For sufficiently low SI, FDT achieves a much higher secrecy rate than FDJ, HDJ, and HDT. However, for higher SI, FDJ becomes more effective in enhancing the achieved secrecy rate. The results also indicate that adaptive power allocation can significantly improve the performance and confirm that the proposed FDT and FDJ outperform the HDT and the HDJ.

Joint resource provisioning and admission control in wireless virtualized networks

This paper studies joint resource provisioning and admission control in wireless virtualized networks (WVN), where one base station of an OFDMA-based wireless network is virtualized into two types of slices with resource-based and rate-based reservations. Aiming to maximize the total rate of WVN, first, the resource provisioning optimization problems are formulated by guaranteeing a minimum requirement for each slice. Via constraint relaxation and variable transformations, an iterative algorithm is developed for power and sub-carrier allocation. Due to the channel variations, WVN suffers from non-zero outage probability, i.e., slice requirements cannot always be met. To prevent this issue, we present an admission control algorithm in which slice requirements are dynamically adjusted based on channel state information. The simulation results demonstrate the effectiveness of our proposed algorithms.

Joint User-Association and Resource-Allocation in Virtualized Wireless Networks

In this paper, we consider the down-link dynamic resource allocation in multi-cell virtualized wireless networks (VWNs) to support the users of different service providers (slices) within a specific region by a set of base stations (BSs) through orthogonal frequency division multiple access (OFDMA). In particular, we develop a joint BS assignment, sub-carrier, and power allocation algorithm to maximize the network sum rate, while satisfying the minimum required rate of each slice. Under the assumption that each user at each transmission instance can connect to no more than one BS, we introduce the user-association factor to represent the joint sub-carrier and BS assignment as the optimization variable vector in the problem formulation. Sub-carrier reuse is allowed in different cells, but not within one cell. As the proposed optimization problem is inherently non-convex and NP-hard, by applying the successive convex approximation (SCA) and complementary geometric programming (CGP), we develop an efficient two-step iterative approach with low computational complexity to solve the proposed problem. For a given problem, Step 1 derives the optimum user-association and subsequently, and for an obtained user-association, Step 2 finds the optimum power allocation. Simulation results demonstrate that the proposed iterative algorithm outperforms the traditional approach in which each user is assigned to the BS with the largest average value of signal strength, and then, joint sub-carrier and power allocation is obtained for the assigned users of each cell. Simulation results reveal a coverage improvement, offered by the proposed approach, of 57% and 71% for uniform and non-uniform users distribution, respectively, leading to higher spectrum efficiency for VWN.

Resource Provisioning in Wireless Virtualized Networks via Massive-MIMO

This letter proposes a dynamic resource provisioning scheme for an OFDMA wireless virtualized network (WVN), where one base-station equipped with a large number of antennas serves users belonging to a number of service providers via different slices. In particular, joint power, sub-carrier, and antenna allocation problems are presented for both perfect and imperfect channel knowledge cases, aiming to maximize a sum-utility while maintaining a minimum rate per slice. Subsequently, relaxation and variable transformation are applied to develop the efficient algorithm to solve the formulated non-convex, combinational optimization problem. Simulation results reveal the benefits of applying a large number of antennas in this setup and evaluate the network performance for different system conditions.

Limited-Feedback Resource Allocation in Heterogeneous Cellular Networks

In this paper, we develop and analyze three limited-feedback resource allocation algorithms suitable for uplink transmission in heterogeneous wireless networks (HetNets). In this setup, one macro-cell shares the spectrum with a set of underlay cognitive small-cells via the orthogonal frequency-division multiple access (OFDMA), where the interference from small-cells to the macro-cell should be kept below a predefined threshold. The resource allocation algorithms aim to maximize the weighted sum of instantaneous rates of all users over all cells by jointly optimizing power and subcarrier allocation under power constraints. Since in practice, the HetNet backhaul capacity is limited, reducing the amount of channel state information (CSI) feedback signaling passed over the backhaul links is highly desirable. To reach this goal, we apply the Lloyd algorithm to develop the limited-feedback two-phase resource allocation scheme. In the first offline phase, an optimal codebook for power and subcarrier allocation is designed and sent to all nodes. In the second online phase, based on channel realizations, the appropriate codeword of the designed codebook is chosen for transmission parameters, and the macro-cell only sends the codeword index represented by a limited number of bits for subcarrier and power allocation to its own users and small-cells. Then, each small-cell informs its own users by their related codewords. The offline phase involves a mixed-integer nonconvex resource allocation problem encountering high computational complexity. To solve it efficiently, we apply the general iterative successive convex approximation (SCA) approach, where the nonconvex optimization problem is transformed into the approximated convex optimization problem in each iteration. The simulation results reveal that the Lloyd algorithm can offer a performance close to the perfect-CSI case (without a limited number of feedback bits).

Robust Ergodic Uplink Resource Allocation in Underlay OFDMA Cognitive Radio Networks

The ergodic resource allocation (ERA) problem for uplink transmission in underlay cognitive radio networks (CRNs) is investigated. The objective is to maximize the ergodic sum-rate of secondary users (SUs) considering the unavailability of perfect channel state information (CSI), and subject to transmit power limitations of SUs, and the interference threshold constraint to guarantee the quality of service of primary users. Since with average-based formulation of ERA, the interference threshold constraint and transmit power limitations of SUs do not hold instantaneously, one can replace the average-based constraints in ERA with their outage-based counterparts. For the uncertainty on the CSI values, we utilize the robust optimization theory where the uncertain parameters are modeled as a sum of the estimated value and error which is assumed to be bounded. We then map the considered ERA problems to their robust counterparts. Generally, the robust approaches degrade the performance (e.g., sum rate of SU), as they conservatively consider the error to be in the maximum extent and try to preserve the constrains under any condition of error (worst-case scenario). We aim to moderate this effect by using appropriate models for uncertain parameters, relaxing the worst-case scenario, and stochastically preserving the constraints. Moreover, robust problems are in general non-convex and suffer from high computational complexity due to the existence of uncertain system parameters. Therefore, we use effective suboptimal approaches to solve them with a reasonable complexity. This includes methods based on chance constraint approach as well as an iterative scheme. The proposed solutions provide a trade-off between robustness, performance, and complexity. Simulation results reveal that by using the proposed schemes, stable sum-rate of SUs in the presence of CSI uncertainties can be achieved while the instantaneous power and interference constraints are met with a desired probability.

Robust probabilistic distributed power allocation by chance constraint approach

We propose a scheme for maintaining the requested SIR of each user under uncertainty of system parameters in the power control of interference limited wireless networks. In doing so, we keep the outage probability of users below their predefined threshold with minimal power consumption. To reduce the complexity, we apply the notion of chance constraint robust optimization to the outage probability. This approach preserves the convexity of the problem and maintains its tractability. For solving the reformulated problem, a distributed probabilistic robust power algorithm is developed based on the standard interference function and local convergence, which utilizes infrequent message passing. We derive the conditions for the convergence of our algorithm, and prove the optimality of the equilibrium.

Robust Equilibria in Additively Coupled Games in Communications Networks

We obtain the robust Nash equilibrium (RNE) for a wide range of multi-user communications networks under uncertainty by utilizing the robust optimization theory for the worst-case uncertainties. To do so, we consider the uncertainty as a distance between the estimated and the actual values of the system parameters as a general norm function, and utilize the finite-dimensions variational inequalities (VI) to derive the conditions for existence and uniqueness of RNE. Two effects of uncertainty on the performance of the system are investigated: the difference between the achieved social utility at the RNE and the Nash equilibrium (NE) of the nominal game, and the distance between the deployed strategies of users at the RNE and at the NE. We quantify these two effects for the cases of unique NE and multiple NEs, and show that when the NE is unique, the achieved social utility at the RNE is always less than that of the NE. Interestingly, the worst-case robustness approach may lead to a higher social utility at the RNE in the multiple NEs scenario. Considering uncertainty at RNE introduces coupling between users, and hence, developing distributed algorithms for reaching RNE is more challenging as compared to the NE in the nominal game. However, for some special forms of utilities and norm functions, we propose simultaneous and sequential distributed algorithms; and investigate the performance of the robust game for power control in interference channels, and for flow control in Jackson networks.