The increasing complexity and density of modern networks necessitate advanced, AI-driven solutions to manage traffic efficiently and maintain high-quality service. In this paper, we present a novel reinforcement learning (RL) framework designed to optimize handover parameters for load balancing in cellular networks. Our framework adopts a hierarchical multi-agent RL approach. Closely adjacent cells (a.k.a., cluster) are controlled by cluster-level agents, whereas inter-cluster parameters are controlled by a network-level agent. By intricate design of state spaces and agent communication, both cluster-level and network-level agents work collaboratively to enhance network performance in terms of throughput and coverage. This method reduces the action and state spaces for each agent, facilitating faster learning, scalable network-wide control, and more efficient decision-making. Our simulation results demonstrate significant improvements in downlink throughput with respect to fully decentralized agents. Our approach incurs negligible throughput loss when compared to a fully centralized agent with full knowledge of the entire network. Our approach not only achieves scalable load balancing with minimal overhead but also allows for customizable reward functions tailored to different network needs.

As the demand for mobile data continues to grow, the energy consumption of mobile networks becomes a major concern. Specifically, base stations account for over 76% of energy usage in mobile networks. We consider a two-tier cellular system, one offering basic coverage while the other offers extra capacity. To save energy, existing network features can opportunistically shut down capacity layer cells when physical resources are lightly utilized. Nevertheless, this is performed without guaranteeing coverage cells can maintain the sought user-centric service quality. To address this challenge, we propose a machine learning (ML)-aided search approach that dynamically designs energy-saving configurations for each cell in each hour while being constrained with a pre-defined quality of service (QoS) measure. This ML model was trained using data collected from 10,283 cells of a live network. We introduce two different approaches to provide these settings: Adaptive QoS Threshold Optimization Algorithm (AQTOA) and an Exhaustive Search (ES) baseline. AQTOA is a low complexity ML-aided search algorithm designed to determine the optimal shutdown threshold for capacity cells while ensuring that QoS requirements are met. Through extensive live network experimentation, the AQTOA results indicate a 1.8% improvement in energy savings compared to the earlier static settings models while maintaining a more strict QoS level than the one addressed in the previous work.

A status updating system is considered in which a source updates a destination over an erasure channel. The utility of the updates is measured through a function of their age-of-information (AoI), which assesses their freshness. Correlated with the status updates is another process that needs to be kept private from the destination. Privacy is measured through a leakage function that depends on the amount and time of the status updates received: stale updates are more private than fresh ones. Different from most of the current AoI literature, a post-sampling waiting time is introduced in order to provide a privacy cover at the expense of AoI. More importantly, it is also shown that, depending on the leakage budget and the channel statistics, it can be useful to retransmit stale status updates following erasure events without resampling fresh ones.

Intelligent reflecting surfaces (IRSs) continue to gain a growing research interest for their potential to support next-generation wireless communications without incurring additional power consumption. In this work, we propose a deep reinforcement learning (DRL)-driven and IRS-aided active/passive beamforming solution for multi-user multiple-input single-output (MISO) settings in beyond 5G networks, which is both lightweight and energy-efficient. The proposed solution is based on a hybrid finely-engineered design that leverages two Twin-Delayed DDPG (TD3) agents. Compared to classical optimization techniques, our numerical evaluation shows that the proposed DRL approach achieves 60% reduction in online computation complexity at the expense of only 1 dB higher power consumption.

Balancing traffic among cellular networks is very challenging due to many factors. Nevertheless, the explosive growth of mobile data traffic necessitates addressing this problem. Due to the problem complexity, data-driven self-optimized load balancing techniques are leading contenders. In this work, we propose a comprehensive deep reinforcement learning (RL) framework for steering the cell individual offset (CIO) as a means for mobility load management. The state of the LTE network is represented via a subset of key performance indicators (KPIs), all of which are readily available to network operators. We provide a diverse set of reward functions to satisfy the operators' needs. For a small number of cells, we propose using a deep Q-learning technique. We then introduce various enhancements to the vanilla deep Q-learning to reduce bias and generalization errors. Next, we propose the use of actor-critic RL methods, including Deep Deterministic Policy Gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3) schemes, for optimizing CIOs for a large number of cells. We provide extensive simulation results to assess the efficacy of our methods. Our results show substantial improvements in terms of downlink throughput and non-blocked users at the expense of negligible channel quality degradation.

Wireless networks have been going through tremendous proliferation recently. As a result, a continuous configuration and management are necessary to sustain a balanced performance while facing such continued growth and endless changes. A self-managed network is required to replace manual management, which is costly, time-consuming, and error-prone. In this paper, we propose a machine-learning-based cellular network management system. The proposed system aims to enhance the network stability and adaptability to temporal changes (e.g., load imbalances across cells). The presented approach is a deep reinforcement learning scheme that enables a network manager to learn a policy that maximizes the network average sum throughput while trying to minimize the consumed energy and the number of blocked users. In addition to controlling the transmitted power and the cell individual offset, MIMO can be switched ON and OFF to control the consumed energy without affecting the quality of service. This results in a hybrid action space, i.e., our action vector has some binary actions as well as continuous actions. We present a novel algorithm to deal with this hybrid action space. Our results reveal that our proposed algorithm is flexible, efficient, and reliable. We report significant performance gains compared to some baselines (without self-management) and previously proposed algorithms.

We investigate the problem of semantic private information retrieval (semantic PIR). In semantic PIR, a user retrieves a message out of$K$independent messages stored in$N$replicated and non-colluding databases without revealing the identity of the desired message to any individual database. The messages come withdifferent semantics, i.e., the messages are allowed to havenon-uniform a priori probabilitiesdenoted by$(p_{i}>0,\: i \in [K])$, which are a proxy for their respective popularity of retrieval, andarbitrary message sizes$(L_{i},\: i \in [K])$. This is a generalization of the classical private information retrieval (PIR) problem, where messages are assumed to have equal message sizes. We derive the semantic PIR capacity for general$K$,$N$. The results show that the semantic PIR capacity depends on the number of databases$N$, the number of messages$K$, the a priori probability distribution of messages$p_{i}$, and the message sizes$L_{i}$. We present two achievable semantic PIR schemes: The first one is a deterministic scheme which is based on message asymmetry. This scheme employs non-uniform subpacketization. The second scheme is probabilistic and is based on choosing one query set out of multiple options at random to retrieve the required message without the need for exponential subpacketization. We derive necessary and sufficient conditions for the semantic PIR capacity to exceed the classical PIR capacity with equal priors and sizes. Our results show that the semantic PIR capacity can be larger than the classical PIR capacity when longer messages have higher popularities. However, when messages are equal-length, the non-uniform priors cannot be exploited to improve the retrieval rate over the classical PIR capacity. We provide two extensions of the semantic PIR problem, namely, the semantic PIR from MDS-coded databases and the semantic PIR from colluding databases. For both extensions, we derive the exact PIR capacity in addition to providing a corresponding optimal scheme.

We study the problem of private set intersection (PSI). In this problem, there are two entities$E_{i}$, for$i=1, 2$, each storing a set$\mathcal {P}_{i}$, whose elements are picked from a finite set$\mathbb {S}_{K}$, on$N_{i}$replicated and non-colluding databases. It is required to determine the set intersection${\mathcal {P}}_{1} \cap {\mathcal {P}} _{2}$without leaking any information about the remaining elements to the other entity, and to do this with the least amount of downloaded bits. We first show that the PSI problem can be recast as a multi-message symmetric private information retrieval (MM-SPIR) problem with certain added restrictions. Next, as a stand-alone result, we derive the information-theoretic sum capacity of MM-SPIR,$C_{MM-SPIR}$. We show that with$K$messages,$N$databases, and a given size of the desired message set$P$, the exact capacity of MM-SPIR is$C_{MM-SPIR} = 1 - \frac {1}{N}$when$P \leq K-1$, provided that the entropy of the common randomness$S$satisfies$H(S) \geq \frac {P}{N-1}$per desired symbol. When$P = K$, the MM-SPIR capacity is trivially 1 without the need for any common randomness$S$. This result implies that there is no gain for MM-SPIR over successive single-message SPIR (SM-SPIR). For the MM-SPIR problem, we present a novel capacity-achieving scheme which builds seamlessly over the near-optimal scheme of Banawan-Ulukus originally proposed for the multi-message PIR (MM-PIR) problem without any database privacy constraints. Surprisingly, our scheme here is exactly optimal for the MM-SPIR problem for any$P$, in contrast to the scheme for the MM-PIR problem, which was proved only to be near-optimal. Our scheme is an alternative to the successive usage of the SM-SPIR scheme of Sun-Jafar. Based on this capacity result for the MM-SPIR problem, and after addressing the added requirements in its conversion to the PSI problem, we show that the optimal download cost for the PSI problem is given by$\min \left \{{\left \lceil{ \frac {P_{1} N_{2}}{N_{2}-1}}\right \rceil, \left \lceil{ \frac {P_{2} N_{1}}{N_{1}-1}}\right \rceil }\right \}$, where$P_{i}$is the cardinality of set${\mathcal {P}}_{i}$.

We consider the problem of privately updating a message out of K messages from N replicated and non-colluding databases. In this problem, a user has an outdated version of the message Ŵθof length L bits that differ from the current version Wθin at most f bits. The user needs to retrieve Wθcorrectly using a private information retrieval (PIR) scheme with the least number of downloads without leaking any information about the message index θ to any individual database. To that end, we propose a novel achievable scheme based on syndrome decoding. Specifically, the user downloads the syndrome corresponding to Wθ, according to a linear block code with carefully designed parameters, using the optimal PIR scheme for messages with a length constraint. We derive lower and upper bounds for the optimal download cost that match if the term ${\log _2}\left( \sum\nolimits_{i = 0}^f \binom L i \right)$ is an integer. Our results imply that there is a significant reduction in the download cost if $fθdirectly using classical PIR approaches without taking the correlation between Wθand Ŵθinto consideration.

We introduce the problem of timely private information retrieval (PIR) from$N$non-colluding and replicated servers. In this problem, a user desires to retrieve a message out of$M$messages from the servers, whose contents are continuously updating. The retrieval process should be executed in a timely manner such that no information is leaked about the identity of the message. To assess the timeliness, we use the age of information (AoI) metric. Interestingly, the timely PIR problem reduces to an AoI minimization subject to PIR constraints under asymmetric traffic. We explicitly characterize the optimal tradeoff between the PIR rate and the AoI metric (peak AoI or average AoI) for the case of$N=2,\ M=3$. Further, we provide some structural insights on the general problem with arbitrary$N,\ M$.

We investigate the problem of semantic private information retrieval (PIR) from coded databases, where a user requires to download a message out of$M$independent messages, without revealing its identity to the databases. These messages are coded using an (N, K) MDS code and stored in$N$non-colluding databases. The$M$messages are allowed to have different semantics, e.g., different sizes and different probabilities of retrieval. We characterize the exact capacity of semantic PIR with coded databases, and provide an achievable scheme with non-uniform subpacketization. We show that the retrieval rate of semantic PIR with coded databases outperforms that of classical PIR with coded databases when the effects of zero padding shorter messages are taken into account.

We investigate the problem of multi-party private set intersection (MP-PSI). In MP-PSI, there are$M$parties, each storing a data set$\mathcal{P}_{i}$over$N_{i}$replicated and non-colluding databases, and we want to calculate the intersection of the data sets$\cap_{i=1}^{M}\mathcal{P}_{i}$without leaking any information beyond the set intersection to any of the parties. For a specific communication protocol, we propose an information-theoretic scheme for MP-PSI based on the connection between the PSI problem and the multi-message symmetric private information retrieval (MM-SPIR) problem. Our scheme is a non-trivial generalization of the 2-party PSI scheme as it needs an intricate design of the shared common randomness. Interestingly, our scheme does not incur any penalty due to the more stringent privacy constraints in the MP-PSI problem compared to the 2-party PSI problem.

We consider the problem of jointly optimizing the transmission power and cell individual offsets (CIOs) in the downlink of cellular networks using reinforcement learning. To that end, we reformulate the problem as a Markov decision process (MDP). We abstract the cellular network as a state, which comprises of carefully selected key performance indicators (KPIs). We present a novel reward function, namely, the penalized throughput, to reflect the tradeoff between the total throughput of the network and the number of covered users. We employ the twin deep delayed deterministic policy gradient (TD3) technique to learn how to maximize the proposed reward function through the interaction with the cellular network. We assess the proposed technique by simulating an actual cellular network, whose parameters and base station placement are derived from a 4G network operator, using NS-3 and SUMO simulators. Our results show the following: 1) optimizing one of the controls is significantly inferior to jointly optimizing both controls; 2) our proposed technique achieves 18.4% throughput gain compared with the baseline of fixed transmission power and zero CIOs; 3) there is a tradeoff between the total throughput of the network and the number of covered users.

The effects ofquantizationandcodingon the estimation quality of Gauss-Markov processes are considered, with a special attention to the Ornstein-Uhlenbeck process. Samples are acquired from the process, quantized, and then encoded for transmission using eitherinfinite incremental redundancy(IIR) orfixed redundancy(FR) coding schemes. A fixedprocessingtime is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of theage-of-information(AoI), defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such functional depends on the quantization bits, codewords lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long-term average MMSE is minimized. This is then characterized in the setting ofgeneral increasing functionals of AoI,not necessarily corresponding to MMSE, which may be of independent interest in other contexts. We first show that the optimal sampling policy for IIR is such that a new sample is generated only if the AoI exceeds a certainthreshold,while for FR it is such that a new sample is deliveredjust-in-timeas the receiver finishes processing the previous one.Enhancedtransmissions schemes are then developed in order to exploit the processing times to make new data available at the receiver sooner. For both IIR and FR, it is shown that there exists an optimal number of quantization bits that balances AoI and quantization errors, and hence minimizes the MMSE. It is also shown that for longer receiver processing times, the relatively simpler FR scheme outperforms IIR.

Over the last few years, finding vacant parking spaces has become a hassle for drivers especially in crowded cities. This problem leads to wasting drivers' time, traffic congestion, and air pollution. Recently, smart parking systems aim to address this problem by enabling drivers to have real-time parking information about vacant parking spaces. However, the existing parking systems rely on a central third party to organize the service, which makes them subject to a single point of failure and privacy breach concerns by both internal and external attackers. In this paper, we propose a secure smart parking system using blockchain technology. Specifically, a consortium blockchain is made of parking lots to ensure security, transparency, and availability of the parking system. Then, to protect the drivers' location privacy, we use cloaking technique to hide the drivers' locations. The blockchain validators return available parking offers with in the cloaked area. Finally, the driver selects the best offer and makes reservation directly with the parking lot. Evaluations are conducted to evaluate the proposed scheme, and results indicate practicality of our scheme.

Balancing traffic among network installed radio base stations is one of the main challenges facing mobile operators because of the unhomogeneous geographical distribution of mobile subscribers in addition to practical and environmental limitations preventing acquiring the best locations to build radio sites. This increases the challenge of satisfying the increasing data speed demand for smartphone users. In this paper, we present a reinforcement learning framework for optimizing neighbor cell relational parameters that can better balance the traffic between different cells within a defined geographical cluster. We present a comprehensive design of the learning framework that includes key system performance indicators and the design of a general reward function. System level simulations show that reinforcement learning based optimization for neighbor cell borders can significantly improve overall system performance; in particular, with a reward function defined as throughput, an improvement up to 50% is achieved.

We investigate the problem of semantic private information retrieval (semantic PIR). In semantic PIR, a user privately retrieves a message out of K independent messages stored in N replicated and non-colluding databases. The messages come with different semantics, i.e., the messages are allowed to have non-uniform a priori probabilities denoted by (pi> 0, i ∈ [K]) and arbitrary message sizes (Li, i ∈ [K]). We derive the semantic PIR capacity for general K, N. We present two achievable semantic PIR schemes: The first one is a deterministic scheme with non-uniform subpacketization. The second scheme is probabilistic and is based on choosing one query set out of multiple options at random to retrieve the required message without the need for exponential subpacketization. We derive conditions for the semantic PIR capacity to exceed the classical PIR capacity with equal priors and sizes. Our results show that the semantic PIR capacity can be larger than the classical PIR capacity when longer messages have higher popularities. However, when messages are of equal-length, the non-uniform priors cannot be exploited to improve the retrieval rate.

The effects of quantization and coding on the estimation quality of a Gauss-Markov, namely Ornstein-Uhlenbeck, process are considered. Samples are acquired from the process, quantized, and then encoded for transmission using either infinite incremental redundancy or fixed redundancy coding schemes. A fixed processing time is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of the age-of-information, defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such (age-penalty) functional depends on the quantization bits, codeword lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long term average MMSE is minimized. This is then characterized in the setting of general increasing age-penalty functionals, not necessarily corresponding to MMSE, which may be of independent interest in other contexts.

We study the problem of private set intersection (PSI). In PSI, there are two entities, each storing a set Pi, whose elements are picked from a finite set SK, on Nireplicated and non-colluding databases. It is required to determine the set intersection P1∩P2without leaking any information about the remaining elements to the other entity. We first show that the PSI problem can be recast as a multi-message symmetric private information retrieval (MM-SPIR) problem. Next, as a stand-alone result, we show that the exact capacity of MM-SPIR is CMM-SPIR= 1 - 1/N when P ≤ K - 1, if the common randomness S satisfies H(S) ≥ P/N-1 per desired symbol. This result implies that there is no gain for MM-SPIR over successive single-message SPIR. We present a novel capacity-achieving scheme which builds seamlessly over the multi-message PIR (MM-PIR) scheme. Based on this capacity result for the MM-SPIR problem, we show that the optimal download cost for the PSI problem is given by min{[P1N2/N2-1],[P2N1/N1-1]}, where P i is the cardinality of the set Pi.

We consider private information retrieval (PIR) of a single file out of K files from N non-colluding databases with heterogeneous storage constraints m = (m1, ⋯, mN). The aim of this work is to jointly design the content placement phase and the information retrieval phase in order to minimize the download cost in the PIR phase. We characterize the optimal PIR download cost as a linear program. By analyzing the structure of the optimal solution of this linear program, we show that, surprisingly, the optimal download cost in our heterogeneous case matches its homogeneous counterpart where all databases have the same average storage constraint μ = 1/N Σn=1Nmn. N Thus, we show that there is no loss in the PIR capacity due to heterogeneity of storage spaces of the databases. We provide the optimum content placement explicitly for N = 3.

We consider the problem of private information retrieval through wiretap channel II (PIR-WTC-II). In PIR-WTC-II, a user wants to retrieve a single message (file) privately out of M messages, which are stored in N replicated and non-communicating databases. An external eavesdropper observes a fraction μn(of its choice) of the traffic exchanged between the nth database and the user. In addition to the privacy constraint, the databases should encode the returned answer strings such that the eavesdropper learns absolutely nothing about the contents of the databases. We aim at characterizing the capacity of the PIR-WTC-II under the combined privacy and security constraints. We obtain a general upper bound for the problem in the form of a max-min optimization problem, which extends the converse proof of the PIR problem under asymmetric traffic constraints. We propose an achievability scheme that satisfies the security constraint by encoding a secret key, which is generated securely at each database, into an artificial noise vector using an MDS code. The user and the databases operate at one of the corner points of the achievable scheme for the PIR under asymmetric traffic constraints such that the retrieval rate is maximized under the imposed security constraint. The upper bound and the lower bound match for the case of M = 2 and M = 3 messages, for any N, and any μ = (μ1, · · · , μN).

A status updating communication system is examined, in which a transmitter communicates with a receiver over a noisy channel. The goal is to realize timely delivery of fresh data over time, which is assessed by an age-of-information (AoI) metric. Channel coding is used to combat the channel errors, and feedback is sent to acknowledge updates' reception. In case decoding is unsuccessful, a hybrid ARQ protocol is employed, in which incremental redundancy (IR) bits are transmitted to enhance the decoding ability. This continues for some amount of time in case decoding remains unsuccessful, after which a new (fresh) status update is transmitted instead. In case decoding is successful, the transmitter has the option to idly wait for a certain amount of time before sending a new update. A general problem is formulated that optimizes the codeword and IR lengths for each update, and the waiting times, such that the long term average AoI is minimized. Stationary deterministic policies are investigated, in which the codeword and IR lengths are fixed for each update, and the waiting time is a deterministic function of the AoI. The optimal waiting policy is then derived, and is shown to have a threshold structure, in which the transmitter sends a new update only if the AoI grows above a certain threshold that is a function of the codeword and IR lengths. Choosing the codeword and IR lengths is discussed in the context of binary symmetric channels.

We consider private information retrieval (PIR) of a single file out of K files from N non-colluding databases with heterogeneous storage constraints m = (m1, ⋯, mN). The aim of this work is to jointly design the content placement phase and the retrieval phase in order to minimize the download cost in the PIR phase. We characterize the optimal PIR download cost as a linear program. By analyzing the structure of the optimal solution of this linear program, we show that, surprisingly, the optimal download cost in our heterogeneous case matches its homogeneous counterpart where all databases have the same average storage constraint μ = 1/N Σn = 1Nmn. We show the optimum content placement explicitly for N = 3.

We consider the problem of private information retrieval (PIR) of a single message out of K messages from N non-colluding and non-replicated databases. Different from the majority of the existing literature, here, we consider the case of non-replicated databases under a special non-replication structure where each database stores M out of K messages and each message is stored across R different databases. This generates an R-regular graph structure for the storage system where the vertices of the graph are the messages and the edges are the databases. We derive a general upper bound for M = 2 that depends on the graph structure. We then specialize the problem to storage systems described by two special types of graph structures: cyclic graphs and fully-connected graphs. We prove that the PIR capacity for the case of cyclic graphs is 2/K+1, and the PIR capacity for the case of fully-connected graphs is min{2/K, 1/2}. In both cases, the results show severe degradation in PIR capacity due to non-replication.

We consider the private information retrieval (PIR) problem from decentralized uncoded caching databases. There are two phases in our problem setting, a caching phase, and a retrieval phase. In the caching phase, a data center containing all the K files, where each file is of size L bits, and several databases with storage size constraint μKL bits exist in the system. Each database independently chooses μKL bits out of the total KL bits from the data center to cache through the same probability distribution in a decentralized manner. In the retrieval phase, a user (retriever) accesses N databases in addition to the data center, and wishes to retrieve a desired file privately. We characterize the optimal normalized download cost to be D/L = Σn-1N+1(n-1N)μn-1(1 - μ)N+1-n(1 + 1/n + ⋯ + 1/nK-1). We show that uniform and random caching scheme which is originally proposed for decentralized coded caching by MaddahAli and Niesen, along with Sun and Jafar retrieval scheme which is originally proposed for PIR from replicated databases surprisingly result in the lowest normalized download cost. This is the decentralized counterpart of the recent result of Attia, Kumar and Tandon for the centralized case.

We consider the problem of private information retrieval from N storage-constrained databases. In this problem, a user wishes to retrieve a single message out of M messages (of size L) without revealing any information about the identity of the message to individual databases. Each database stores μML symbols, i.e., a μ fraction of the entire library, where 1/N ≤ μ ≤ 1. Our goal is to characterize the optimal tradeoff 1 curve for the storage cost (captured by μ) and the normalized download cost (D/L). We show that the download cost can be reduced by employing a hybrid storage scheme that combines MDS coding ideas with uncoded partial replication ideas. When there is no coding, our scheme reduces to Attia-Kumar-Tandon storage scheme, which was initially introduced by Maddah-AliNiesen in the context of the caching problem, and when there is no uncoded partial replication, our scheme reduces to BanawanUlukus storage scheme; in general, our scheme outperforms both.

We consider the two-user multiple-input multipleoutput (MIMO) interference channel with confidential messages (ICCM). We determine the exact secure degrees of freedom (s.d.o.f.) region for the symmetric case of M antennas at both transmitters and N antennas at both receivers. We develop the converse by combining the broadcast channel with confidential messages (BCCM) cooperative upper bound, decodability upper bound for the interference channel with no secrecy constraints, and vector extensions of the secrecy penalty and role of a helper lemmas. For the achievability, we first show that the s.d.o.f. region is a four-vertex polytope. For the sum s.d.o.f. point, we propose a novel achievable scheme for the 2 × 2 ICCM, which combines asymptotic real interference alignment with spatial interference alignment. Using this scheme, we provide achievable schemes for any M and N by proper vector space operations. We achieve the other non-trivial extreme polytope points by employing one of the transmitters as a deaf helper for assisting the secure transmission of the other user. We present simplified achievable schemes for the special case of time-varying MIMO ICCM. The achievable schemes, in this case, make use of the time-varying nature of the channel to construct vector-space alignment counterpart of the real interference alignment used in the static channel case.

We consider the problem of single-round private information retrieval (PIR) from N replicated databases. We consider the case when B databases are outdated (unsynchronized), or even worse, adversarial (Byzantine), and therefore, can return incorrect answers. In the PIR problem with Byzantine databases (BPIR), a user wishes to retrieve a specific message from a set of M messages with zero-error, irrespective of the actions performed by the Byzantine databases. We consider the T-privacy constraint in this paper, where any T databases can collude, and exchange the queries submitted by the user. We derive the information-theoretic capacity of this problem, which is the maximum number of correct symbols that can be retrieved privately (under the T-privacy constraint) for every symbol of the downloaded data. We determine the exact BPIR capacity to be C = (N -2B)/N·(1-T/(N-2B))/(1-(T/(N - 2B))M), if 2B + T <; N. This capacity expression shows that the effect of Byzantine databases on the retrieval rate is equivalent to removing 2B databases from the system, with a penalty factor of (N - 2B)/N, which signifies that even though the number of databases needed for PIR is effectively N - 2B, the user still needs to access the entire N databases. The result shows that for the unsynchronized PIR problem, if the user does not have any knowledge about the fraction of the messages that are missynchronized, the single-round capacity is the same as the BPIR capacity. Our achievable scheme extends the optimal achievable scheme for the robust PIR (RPIR) problem to correct the errors introduced by the Byzantine databases as opposed to erasures in the RPIR problem. Our converse proof uses the idea of the cut-set bound in the network coding problem against adversarial nodes.

We consider the classical setting of private information retrieval (PIR) of a single message (file) out of M messages from N distributed databases under the new constraint of asymmetric traffic from databases. In this problem, the ratios between the traffic from the databases are constrained, i.e., the ratio of the length of the answer string that the user (retriever) receives from the nth database to the total length of all answer strings from all databases is constrained to be τn. This may happen if the user's access to the databases is restricted due to database availability, channel quality to the databases, and other factors. For this problem, for fixed M, N, we develop a general upper bound C̅(τ), which generalizes the converse proof of Sun-Jafar, where database symmetry was inherently used. Our converse bound is a piece-wise affine function in the traffic ratio vector τ = (τ1, · · · ,τN). For the lower bound, we explicitly show the achievability of (M+N-1/M) corner points. For the remaining traffic ratio vectors, we perform time-sharing between these corner points. The recursive structure of our achievability scheme is captured via a system of difference equations. The upper and lower bounds exactly match for M = 2 and M = 3 for any N and any τ. The results show strict loss of PIR capacity due to the asymmetric traffic constraints compared with the symmetric case of Sun-Jafar which implicitly uses τn= N1for all n.

We consider the problem of noisy private information retrieval (NPIR) from$N$non-communicating databases, each storing the same set of$M$messages. In this model, the answer strings are not returned through noiseless bit pipes, but rather throughnoisymemoryless channels. We aim at characterizing the PIR capacity for this model as a function of the statistical information measures of the noisy channels such as entropy and mutual information. We derive a general upper bound for the retrieval rate in the form of a max-min optimization. We use the achievable schemes for the PIR problem under asymmetric traffic constraints and random coding arguments to derive a general lower bound for the retrieval rate. The upper and lower bounds match for$M=2$and$M=3$, for any$N$, and any noisy channel. The lower and upper bounds show a separation between channel coding and retrieval scheme except for adapting the traffic ratio from the databases. We refer to this asalmost separation. Next, we consider the private information retrieval problem from multiple access channels (MAC-PIR). In MAC-PIR, the database responses reach the user through a multiple access channel (MAC) that mixes the responses together in a stochastic way. We show that for the additive MAC and the conjunction/disjunction MAC, channel coding and retrieval scheme areinseparableunlike in NPIR. We show that the retrieval scheme depends on the properties of the MAC, in particular on the linearity aspect. For both cases, we provide schemes that achieve the full capacity without any loss due to the privacy constraint, which implies that the user can exploit the nature of the channel to improve privacy. Finally, we show that the full unconstrained capacity is not always attainable by determining the capacity of the selection channel.

We consider the problem of private information retrieval (PIR) from N non-colluding and replicated databases when the user is equipped with a cache that holds an uncoded fraction r from each of the K stored messages in the databases. We assume that the databases are unaware of the cache content. We investigate D*(r) the optimal download cost normalized with the message size as a function of K, N, and r. For a fixed K and N, we develop an inner bound (converse bound) for the D*(r) curve. The inner bound is a piece-wise linear function in r that consists of K line segments. For the achievability, we develop explicit schemes that exploit the cached bits as side information to achieve K -1 non-degenerate corner points. These corner points differ in the number of cached bits that are used to generate the one-side information equation. We obtain an outer bound (achievability) for any caching ratio by memory sharing between these corner points. Thus, the outer bound is also a piece-wise linear function in r that consists of K line segments. The inner and the outer bounds match in general for the cases of very low-caching ratio and very high-caching ratio. As a corollary, we fully characterize the optimal download cost caching ratio tradeoff for K = 3. For general K, N, and r, we show that the largest gap between the achievability and the converse bounds is 1/6. Our results show that the download cost can be reduced beyond memory sharing if the databases are unaware of the cached content.

We consider the problem of private information retrieval (PIR) of a single message out of$K$messages from$N$replicated and non-colluding databases where a cache-enabled user (retriever) of cache-size$M$possesses side information in the form of full messages that are partially known to the databases. In this model, the user and the databases engage in a two-phase scheme, namely, the prefetching phase where the user acquires side information and the retrieval phase where the user downloads desired information. In the prefetching phase, the user receives$m_{n}$full messages from the$n$th database, under the cache memory size constraint$\sum _{n=1}^{N} m_{n} \leq M$. In the retrieval phase, the user wishes to retrieve a message (which is not present in its memory) such that no individual database learns anything about the identity of the desired message. In addition, the identities of the side information messages that the user did not prefetch from a database must remain private against that database. Since the side information provided by each database in the prefetching phase is known by the providing database and the side information must be kept private against the remaining databases, we coin this model aspartially known private side information. We characterize the capacity of the PIR with partially known private side information to be$C=\left ({1+\frac {1}{N}+\cdots +\frac {1}{N^{K-M-1}}}\right)^{-1}=\frac {1-\frac {1}{N}}{1-\left({\frac {1}{N}}\right)^{K-M}}$. Interestingly, this result is the same if none of the databases knows any of the prefetched side information, i.e., when the side information is obtained externally, a problem posed by Kadhe et al. and settled by Chen-Wang-Jafar recently. Thus, our result implies that there is no loss in using the same databases for both prefetching and retrieval phases.

We consider the problem of private information retrieval (PIR) from N non-colluding and replicated databases, when the user is equipped with a cache that holds an uncoded fraction r from each of the K stored messages in the databases. This model operates in a two-phase scheme, namely, the prefetching phase where the user acquires side information and the retrieval phase where the user privately downloads the desired message. In the prefetching phase, the user receivesrN uncoded fraction of each message from the nth database. This side information is known only to the nth database and unknown to the remaining databases, i.e., the user possesses partially known side information. We investigate the optimal normalized download cost D*(r) as a function of K, N, r. For a fixed K, N, we develop an inner bound (converse) and an outer bound (achievability) for the D*(r) curve. The bounds match in general for the cases of very low caching ratio (r ≤ 1/NK-1) and very high caching ratio (r ≥ K-2/N2-3N+KN). As a corollary, we fully characterize the optimal download cost caching ratio tradeoff for K = 3. For general K, N, and r, we show that the largest gap between the achievability and the converse bounds is 5/32.

We consider the problem of private information retrieval through a wiretap channel II (PIR-WTC-II). In PIR-WTC-II, a user wants to retrieve a message (or file) privately out of M messages, which are stored in N replicated and noncommunicating databases. An eavesdropper observes a fraction μnof the traffic exchanged between the nth database and the user. The databases should encode the returned answer strings such that the eavesdropper learns nothing about the contents of the databases. We aim at characterizing the capacity of the PIR-WTC-II under these joint privacy and security constraints. We obtain an upper bound in the form of a max-min optimization problem. We propose an achievability scheme that satisfies the security constraint by encoding a secret key into an artificial noise vector using an MDS code. The user and the databases operate at one of the corner points of the achievable scheme of the PIR under asymmetric traffic constraints such that the retrieval rate is maximized under the imposed security constraint. The upper bound and the lower bound match for the cases of M = 2 and M = 3 messages, for any number of databases N, and any μn.

We consider the problem of private information retrieval (PIR) of a single message (file) out of M messages from N distributed databases under asymmetric traffic from databases. In this problem, the ratios between the traffic from the databases are constrained, i.e., the ratio of the length of the answer string that the user receives from the nth database to the total length of all answer strings from all databases is constrained to be τn. For this problem, for fixed M, N, we develop a general upper bound C̅(τ). Our converse bound is a piece-wise affine function in the traffic ratio vector τ = (τ1, ⋯, τN). For the lower bound, we explicitly show the achievability of (MM+N-1) corner points. For the remaining traffic ratio vectors, we perform time-sharing between these corner points. The recursive structure of our achievability scheme is captured via a system of difference equations. The upper and lower bounds exactly match for M=2 and M=3 for any N and any τ.

We consider the problem of private information retrieval (PIR) from N non-colluding and replicated databases when the user is equipped with a cache that holds an uncoded fraction r from each of the K stored messages in the databases. We assume that the databases are unaware of the cache content. We investigate D*(r) the optimal download cost normalized with the message size as a function of K, N, r. We develop inner and outer bounds for the optimal download cost. Both inner and outer bounds are piece-wise linear functions in r (for fixed N, K) that consist of K line segments. The inner and the outer bounds match in general for the cases of very low caching ratios and very high caching ratios. As a corollary, we fully characterize the optimal download cost caching ratio tradeoff for K = 3. For general K, N, and r, we show that the largest additive gap between the achievability and the converse bounds is [1/6].

We consider the private information retrieval problem from multiple access channels (MAC-PIR). In MAC-PIR, there areN databases, each storing the same set of M messages. The database responses reach the user through a multiple access channel (MAC) that may mix the responses together in a stochastic way. We show that for the additive MAC and the conjunction/disjunction MAC, channel coding and retrieval scheme are inseparable unlike the noisy private information retrieval problem (NPIR). We show that the retrieval scheme depends on the properties of the MAC, in particular on the linearity aspect. For both cases, we provide schemes that achieve the full capacity without any loss due to the privacy constraint, which implies that the user can exploit the nature of the channel in its favor. Finally, we show that the full capacity is not always attainable by determining the capacity of the selection channel.

We consider the problem of private information retrieval from N non-colluding and replicated databases, when the user is equipped with a cache that holds an uncoded fraction r of the symbols from each of the K stored messages in the databases. This model operates in a two-phase scheme, namely, the prefetching phase where the user acquires side information and the retrieval phase where the user privately downloads the desired message. In the prefetching phase, the user receives r/N uncoded fraction of each message from the nth database. This side information is known only to the nth database and unknown to the remaining databases, i.e., the user possesses partially known side information. We investigate the optimal normalized download cost D*(r) in the retrieval phase as a function of K, N, and r. We develop lower and upper bounds for the optimal download cost. The bounds match in general for the cases of very low caching ratio and very high caching ratio. We fully characterize the optimal download cost caching ratio tradeoff for K = 3. For general K, N, and r values, we show that the largest additive gap between the achievability and the converse bounds is 5/32.

We consider the problem of private information retrieval (PIR) over a distributed storage system. The storage system consists of N non-colluding databases, each storing an MDS-coded version of M messages. In the PIR problem, the user wishes to retrieve one of the available messages without revealing the message identity to any individual database. We derive the information-theoretic capacity of this problem, which is defined as the maximum number of bits of the desired message that can be privately retrieved per one bit of downloaded information. We show that the PIR capacity in this case is C = (1 + K/N + K2/N2+ ⋯ + KM-1/NM-1)-1= (1 + Rc+ Rc2+ ⋯ + RcM-1)-1= (1 - Rc)/(1 - RcM), where Rcis the rate of the (N, K) MDS code used. The capacity is a function of the code rate and the number of messages only regardless of the explicit structure of the storage code. The result implies a fundamental tradeoff between the optimal retrieval cost and the storage cost when the storage code is restricted to the class of MDS codes. The result generalizes the achievability and converse results for the classical PIR with replicated databases to the case of MDS-coded databases.

We consider the problem of multi-message private information retrieval (MPIR) from N non-communicating replicated databases. In MPIR, the user is interested in retrieving P messages out of M stored messages without leaking the identity of the retrieved messages. The information-theoretic sum capacity of MPIR CsP is the maximum number of desired message symbols that can be retrieved privately per downloaded symbol, where the symbols are defined over the same field. For the case P ≥ M/2, we determine the exact sum capacity of MPIR as CPs= 1/(1+(M - P)/(PN)). The achievable scheme in this case is based on downloading MDS-coded mixtures of all messages. For P ≤ M/2, we develop lower and upper bounds for all M, P, N. These bounds match if the total number of messages M is an integer multiple of the number of desired messages P, i.e., M/P ∈ N. In this case, CsP= (1+1/N+⋯+1/NM/P-1)-1, i.e., CsP= (1 - 1/N)/(1 - 1/NM/P) for N>1, and CsP= P/M for N = 1. The achievable scheme in this case generalizes the singlemessage capacity achieving scheme to have unbalanced number of stages per round of download. For all the remaining cases, the difference between the lower and upper bound is at most 0.0082, which occurs for M = 5, P = 2, N = 2. Our results indicate that joint retrieval of desired messages is more efficient than successive use of single-message retrieval schemes even after considering the free savings that result from downloading undesired symbols in each single-message retrieval round.

We consider the problem of private information retrieval (PIR) over a distributed storage system. The storage system consists of N non-colluding databases, each storing an MDS-coded version of M messages. In the PIR problem, the user wishes to retrieve one of the available messages without revealing the message identity to any individual database. We derive the information-theoretic capacity of this problem, which is defined as the maximum number of bits of the desired message that can be privately retrieved per one bit of downloaded information. We show that the PIR capacity in this case is C = (1 + K/N + K2/N2+ ··· + KM-1/NM-1)-1= (1 + Rc+ R2c+ ··· + RcM-1)-1= 1-Rc/RcM, where Rcis the rate of the (N, K) code used. The capacity is a function of the code rate and the number of messages only regardless of the explicit structure of the storage code. The result implies a fundamental tradeoff between the optimal retrieval cost and the storage cost. The result generalizes the achievability and converse results for the classical PIR with replicating databases to the case of coded databases.

We consider the problem of multi-message private information retrieval (MPIR) from N non-communicating replicated databases. In MPIR, the user is interested in retrieving P messages out of M stored messages without leaking the identity of the retrieved messages. The information-theoretic sum capacity of MPIR CP is the maximum number of desired message symbols that can be retrieved privately per downloaded symbol. For the case P ≥ M/2, we determine the exact sum capacity of MPIR as CPs=1/1+M-P/PN For P≤M/2, we develop lower and upper bounds for all M, P, N. These bounds match if the number of messages M is an integer multiple of the number of desired messages P, in which case, CPs= 1-1N/1-(1/N)M/P. Our results indicate that joint retrieval of desired messages is more efficient than successive use of single-message retrieval schemes.

We investigate the secure degrees of freedom (s.d.o.f.) of two new channel models: broadcast channel with combating helpers and interference channel with selfish users. In the first model, over a classical broadcast channel with confidential messages (BCCM), there are two helpers, each associated with one of the receivers. In the second model, over a classical interference channel with confidential messages (ICCM), there is a helper and users are selfish. The goal of introducing these channel models is to investigate various malicious interactions that arise in networks, including active adversaries. By casting each problem as an extensive-form game and applying recursive real interference alignment, we show that, for the first model, the combating intentions of the helpers are neutralized and the full s.d.o.f. is retained; for the second model, selfishness precludes secure communication and no s.d.o.f. is achieved.

We consider the multiple access wiretap channel (MAC-WTC), where multiple legitimate users wish to have secure communication with a legitimate receiver in the presence of an eavesdropper. The exact secure degrees of freedom (s.d.o.f.) region of this channel is known. Achieving this region requires users to follow a certain protocol altruistically and transmit both message-carrying and cooperative jamming signals in an optimum manner. In this paper, we consider the case when a subset of users deviate from this optimum protocol. We consider two kinds of deviation: when some of the users stop transmitting cooperative jamming signals, and when a user starts sending intentional jamming signals. For the first scenario, we investigate possible responses of the remaining users to counteract such deviation. For the second scenario, we use an extensive-form game formulation for the interactions of the deviating and well-behaving users. We prove that a deviating user can drive the s.d.o.f. to zero; however, the remaining users can exploit its intentional jamming signals as cooperative jamming signals against the eavesdropper and achieve an optimum s.d.o.f.

We consider a channel with N parallel sub-bands. There is a single user that can access exactly k channels, while maintaining some minimum rate at each accessed channel. The transmission takes place in the presence of a jammer which can access at most m channels. We cast the problem as an extensive-form game and derive the optimal power allocation strategies for both the user and the jammer. We present extensive simulation results regarding convergence of rates, effect of changing the number of accessed bands for the user and the jammer, and the minimum rate constraint.

We consider the multiple-input multiple-output (MIMO) wiretap channel under a minimum receiver-side power constraint in addition to the usual maximum transmitter-side power constraint. This problem is motivated by energy harvesting communications with wireless energy transfer, where an added goal is to deliver a minimum amount of energy to a receiver in addition to delivering secure data to another receiver. In this paper, we characterize the exact secrecy capacity of the MIMO wiretap channel under transmitter and receiver-side power constraints. We first show that solving this problem is equivalent to solving the secrecy capacity of the wiretap channel under a double-sided correlation matrix constraint on the channel input. We show the converse by extending the channel enhancement technique to our case. We present two achievable schemes that achieve the secrecy capacity: the first achievable scheme uses a Gaussian codebook with a fixed mean, and the second achievable scheme uses artificial noise (or cooperative jamming) together with a Gaussian codebook. The role of the mean or the artificial noise is to enable energy transfer without sacrificing from the secure rate. This is the first instance of a channel model where either the use of a mean signal or the use of channel prefixing via artificial noise is strictly necessary for the MIMO wiretap channel. We then extend our work to consider a maximum receiver-side power constraint instead of a minimum receiver-side power constraint. This problem is motivated by cognitive radio applications, where an added goal is to decrease the received signal energy (interference temperature) at a receiver. We further extend our results to: requiring receiver-side power constraints at both receivers; considering secrecy constraints at both receivers to study broadcast channels with confidential messages; and removing the secrecy constraints to study the classical broadcast channel.

In this paper, Collaborative MIMO is introduced to the Uplink of the Long Term Evolution (LTE) system. This technique uses two or more single carrier frequency division multiple access based user equipments (UEs) equipped with single antenna. These UEs transmit their data collaboratively over the same Resource block (RB), and then the Evolved Node B (eNodeB) separates the users' data by means of multiuser frequency domain equalization. This will increase the whole throughput of the LTE Uplink, besides moving the complexity of implementing multiple antennas to the eNodeB. In order to decode the data ZF, MMSE and successive interference cancellation (SIC) are employed although they didn't exploit the full spatial diversity, whereas the standard ML is abandoned for its exponential complexity. In this paper, we propose a novel Initial Guess based ML (IGML) receiver whose complexity is in the same order of ML receiver of OFDM-MIMO systems, and a QR based simplified ML receiver. We also propose two ordering techniques for the SIC receiver when used in shadowing environment, as well as various simulation parameters are examined to find the best collaborating environments. The simulation results reveal that the IGML receiver is far by almost 4.5dB at target BER of 10-4 from the traditional MMSE receiver.