We address the problem of detecting misbehavior in the coexistence etiquette between LTE and Wi-Fi systems operating in the 5GHz U-NII unlicensed bands. We define selfish misbehavior strategies for the LTE that can yield an unfair share of the spectrum resources. Such strategies are based on manipulating the operational parameters of the LTE-LAA standard, namely the backoff mechanism, the traffic class parameters, the clear channel access (CCA) threshold, and others. Prior methods for detecting misbehavior in homogeneous settings are not applicable in a spectrum sharing scenario because the devices of one system cannot decode the transmissions of another. We develop implicit sensing techniques that can accurately estimate the operational parameters of LTE transmissions under various topological scenarios andwithout decoding.These techniques apply correlation-based signal detection to infer the required information. Our techniques are validated through experiments on a USRP testbed. We further apply a statistical inference framework for determining deviations of the LTE behavior from the coexistence etiquette. By characterizing the detection and false alarm probabilities, we show that our framework yields high detection accuracy at a very low false alarm rate. Although our methods focus on detecting misbehavior of the LTE system, they can be generalized to detect Wi-Fi misbehavior and to other coexistence scenarios.

In latent-variable private information retrieval (LV-PIR), a user wishes to retrieve one out of$K$messages (indexed by θ) without revealing any information about a sensitive latent attribute (modeled by a latent variable$S$correlated with θ). While conventional PIR protocols, which keep θ2private, also suffice for hiding S, they can be too costly in terms of the download overhead. In this paper, we characterize the capacity (equivalently, the optimal download cost) of LV-PIR as a function of the distribution PS|θ. We present a converse proof that yields a lower bound on the optimal download cost, and a matching achievable scheme. The optimal scheme, however, involves an exhaustive search over subset queries and over all messages, which can be computationally prohibitive. We further present two low-complexity, albeit sub-optimal, schemes that also outperform the conventional PIR solution.

Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of K messages from N non-colluding replicated databases without learning anything about the remaining K-1 messages. However, the goal of perfect privacy can be too taxing for certain applications. In this paper, we investigate if the information-theoretic capacity of SPIR (equivalently, the inverse of the minimum download cost) can be increased by relaxing both user and DB privacy definitions. Such relaxation is relevant in applications where privacy can be traded for communication efficiency. We introduce and investigate the Asymmetric Leaky PIR (AL-PIR) model with different privacy leakage budgets in each direction. For user privacy leakage, we bound the probability ratios between all possible realizations of DB queries by a function of a non-negative constant ϵ. For DB privacy, we bound the mutual information between the undesired messages, the queries, and the answers, by a function of a non-negative constant δ. We propose a general AL-PIR scheme that achieves an upper bound on the optimal download cost for arbitrary ϵ and δ. We show that the optimal download cost of AL-PIR is upper-bounded as D*(ϵ,δ) ≤ 1+\frac 1N-1-\frac δeϵNK-1-1. Second, we obtain an information-theoretic lower bound on the download cost as D*(ϵ,δ) ≥ 1+\frac 1Neϵ-1-\frac δ(Neϵ)K-1-1. The gap analysis between the two bounds shows that our AL-PIR scheme is optimal when ϵ = 0, i.e., under perfect user privacy and it is optimal within a maximum multiplicative gap of \frac N-e-ϵN-1 for any ϵ > 0 and δ > 0.

Motivated by the shared spectrum paradigm, we address the problem of implicit coordination between coexisting wireless systems that do not share a common control plane. We consider the coexistence of LTE and Wi-Fi and study mechanisms for conserving energy when the wireless channel is occupied. In a Wi-Fi only system, the network allocation vector (NAV) included in the header of IEEE 802.11 frames advertises the duration of an imminent transmission. Nearby Wi-Fi terminals decode the frame header and transition to sleep mode to conserve energy. However, when heterogeneous systems coexist (e.g., LTE and Wi-Fi), frames that belong to other systems are not decodable. This leads to continuous channel sensing even when the channel is to be occupied for a long duration. We design two implicit mechanisms to play the role of the NAV. Our mechanisms predict the duration of an imminent LTE transmission by predicting the frame's traffic class. The prediction is based on the elapsed idle slots between successive transmissions and the transmission history. We show that our methods achieve significant energy savings without stifling transmission opportunities.

In many applications, content accessed by users (movies, videos, news articles, etc.) can leak sensitive latent attributes, such as religious and political views, sexual orientation, ethnicity, gender, and others. To prevent such information leakage, the goal of classical PIR is to hide the identity of the content/message being accessed, which subsequently also hides the latent attributes. This solution, while private, can be too costly, particularly, when perfect (information-theoretic) privacy constraints are imposed. For instance, for a single database holding K messages, privately retrieving one message is possible if and only if the user downloads the entire database of K messages. Retrieving content privately, however, may not be necessary to perfectly hide the latent attributes.Motivated by the above, we formulate and study the problem of latent-variable private information retrieval (LV-PIR), which aims at allowing the user efficiently retrieve one out of K messages (indexed by θ) without revealing any information about the latent variable (modeled by S). We focus on the practically relevant setting of a single database and show that one can significantly reduce the download cost of LV-PIR (compared to the classical PIR) based on the correlation between θ and S. We present a general scheme for LV-PIR as a function of the statistical relationship between θ and S, and also provide new results on the capacity/download cost of LV-PIR. Several open problems and new directions are also discussed.

Wi-Fi and LTE standards define several traffic classes to prioritize applications based on their requirements. When these technologies coexist in unlicensed bands, the class selection of one system impacts the performance of the other. In this paper, we investigate how the traffic class selection affects the delay for completing the transmission of a fixed number of bits. We develop an analytical framework which characterizes the average delay under Wi-Fi/LTE coexistence. Our framework allows us to optimize the class selection for a Wi-Fi or LTE station based on the traffic class selected by the surrounding stations and minimize the average delay. We show that operating at a high priority class does not always minimize delay. Under certain contention and class selection conditions, a low priority class reduces the collision probability while increasing the airtime once the channel is captured. This leads to a lower overall delay. We provide numerical examples that demonstrate the inherent tradeoffs between the traffic class parameters.

Private information retrieval (PIR) allows users to retrieve data from databases without revealing the identity of that data. An extensive body of works has investigated efficient schemes to achieve computational and information-theoretic privacy. The latter guarantees that no information is revealed to the databases, irrespective of their computational power. Although information-theoretic PIR (IT-PIR) provides a strong privacy guarantee, it can be too taxing for certain applications. In this paper, we initiate the study of leaky private information retrieval (L-PIR), where a bounded amount of privacy leakage is allowed and measured through a parameter ε. The classical IT-PIR formulation is obtained by setting ε = 0, and for ε > 0, we explore the opportunities offered for reducing the download cost. We derive new upper and lower bounds on the download cost of L-PIR for any arbitrary ε, any number of messages K, and for N = 2 databases.

We investigate the model of a single primary-transceiver pair with multiple secondary-transceiver pairs. The secondary pairs can act as relays for the primary transmitter enabling access to its channel resources. Each secondary user (SU) is assumed to be a radio-frequency energy-harvester node. We formulate a framework that aims at specifying the optimal SU set that operates as relay nodes for the primary user (PU) data message. The set of the SUs is selected such that the SUs total throughput is maximized under a certain quality-of-service (QoS) requirement constraint on the PU target data rate. We propose both centralized and distributed approaches for solving the formulated optimization problems. The centralized approach is based on solving a convex optimization problem at the PU. On the other hand, the distributed approach leverages a Sackelberg game where all users interact to achieve the best relay-selection scheme and PU’s transmit power. We prove the uniqueness and Nash equilibrium of the considered Stackelberg game, and develop a game-theoretic relay and PU’s transmit power selection algorithm. We also introduce a fairness optimization-based scheme (FOBS) that aims at enhancing the fairness among the SUs under our proposed centralized approach. Our simulation results show the efficiency of our proposed schemes in terms of SUs total throughput.

In this paper, we consider the fair coexistence between LTE and Wi-Fi systems in unlicensed bands. We focus on the misbehavior opportunities that stem from the heterogeneity of the coexisting systems and the lack of explicit coordination mechanisms. We show that a selfishly behaving LTE can gain an unfair share of the spectrum resources through the manipulation of the parameters defined in the LAA-LTE standard, including the manipulation of the backoff mechanism of LAA, the traffic class, the clear channel assignment threshold and others. We develop a detection mechanism for the Wi-Fi system that can identify a misbehaving LTE system. Our mechanism advances the state of the art by providing an accurate monitoring method of the LTE behavior under various topological scenarios, without explicit cross-system coordination. Deviations from the expected behavior are determined by computing the statistical distance between the protocol-specified and estimated distributions of the LAA-LTE protocol parameters. We analytically characterize the detection and false alarm probabilities and show that our detector yields high detection accuracy at very low false alarm rate, for a wise choice of statistical parameters.

We study the scenario of hybrid cloud storage where the client utilizes both an off-site and a local storage. The former is a distributed storage system (DSS) with the presence of an eavesdropper that has access to the content stored in and downloaded to some subset of nodes. The latter (local) storage is utilized to store a secret key to secure the stored file against the eavesdropper. We introduce two possibilities to utilize local storage (secret key) in enhancing the DSS. First, the key can be used to increase the maximum file size stored in the DSS. We propose an upper bound for this scenario and show constructions achieving it. Second, the key can be used to decrease the number of contacted nodes required to reconstruct the file at the client. We extend the product matrix (PM) framework and construct codes that enables efficient data access. Our analysis includes both minimum repair bandwidth regenerating (MBR) and minimum storage regenerating (MSR) codes.

Energy harvesting has emerged as a promising technique which helps to increase the sustainability of wireless networks. In this paper, we consider a network with a single source, single destination and a single relay equipped with multiple antennas. We develop an optimization framework that exploits the energy harvested from the source radio frequency signals with smart antenna selection schemes at the relay node. Our main target is to minimize the source power and the antennas' circuit power jointly subject to quality of service constraints on the rate. To overcome the computational complexity of the optimization problem, we propose two special case schemes, namely Fixed Source Power Antenna Selection (FSP-AS) and All Receive Antenna Selection (AR-AS). Also, we suggest two sub-optimal heuristic schemes with low complexity and compare their performance with the optimization problem solutions numerically. The simulation results show the gain of our optimal scheme in terms of energy efficiency, which can be up to 80% as compared to solutions proposed in the literature.

Energy efficient operation of cellular systems becomes a core design goal for economic and environment-friendly network operation. Several studies have shown that the energy consumed in base stations represents 60-80% of the energy consumption in cellular networks. In this paper, we develop an optimization framework that exploits several energy efficient techniques including switching power modes of base stations, Adaptive Modulation (AM), and the use of relays. Our main objective is to reduce both, transmitted and circuit power, subject to satisfying the quality of service constraints. To accommodate the complexity of the target problem, we further propose two sub-optimal algorithms, minimum power heuristic (MPH) and minimum relays heuristic (MRH). The simulation results show that energy saving merits of our proposed schemes can be up to 80%.