In several wireless settings, channel statistics may be controllable and/or predictable. For instance, next generation reconfigurable antennas have the potential to control channel statistics. Further, the known trajectory and operation protocol of communication satellites results in networks with predictable statistics. These settings give rise to a non-stationary model for which the fundamentals are largely unknown. We consider the canonical two-user broadcast packet erasure channel in which channel statistics vary at a priori known points in time. We consider a multi-modal setting with two non-transient modes (whose lengths scale linearly with the blocklength) and an arbitrary number of transient modes. We provide a new set of outer-bounds on the capacity region of this problem when the encoder has access to causal feedback. The results reveal the significant role of the non-transient mode with higher erasure probability both on the outer and the inner bounds. We show the outer-bounds are achievable in non-trivial regimes, characterizing the capacity region for a wide range of parameters. We also discuss the regimes where the inner and outer bounds diverge and analyze the gap between the two. A key finding of this work is the significant gain of inter-modal coding over the separate treating of individual modes.

We consider the problem of communicating over a channel that breaks the message block into fragments of random lengths, shuffles them out of order, and deletes a random fraction of the fragments. Such a channel is motivated by applications in molecular data storage and forensics, and we refer to it as the torn-paper channel. We characterize the capacity of this channel under arbitrary i.i.d. fragment length distributions and deletion probabilities. Precisely, we show that the capacity is given by a closed-form expression that can be interpreted as F−A, where F is the coverage fraction, i.e., the fraction of the input codeword that is covered by output fragments, and A is an alignment cost incurred due to the lack of ordering in the output fragments. We then consider a noisy version of the problem, where the fragments are corrupted by binary symmetric noise. We derive upper and lower bounds to the capacity, both of which can be seen as F−A expressions. These bounds match for specific choices of fragment length distributions, and they are approximately tight in cases where there are not too many short fragments.

Next generation communications demand better spectrum management, lower latency, and guaranteed quality-of-service (QoS). Recently, artificial intelligence (AI) has been widely introduced to advance these aspects in next generation wireless systems. However, such AI applications suffer from limited training data, low robustness, and poor generalization capabilities. To address these issues, we introduce a model-driven deep unfolding (DU) algorithm in this paper to address the gap between traditional model-driven communication algorithms and data-driven deep learning. Focusing on the QoS-aware rate-splitting multiple access (RSMA) resource allocation problem in multi-user communications, a conventional fractional programming (FP) algorithm is first applied as a benchmark. The solution is further refined using projection gradient descent (PGD). DU is employed to further accelerate convergence, thereby improving the efficiency of PGD. Moreover, the feasibility of results is guaranteed by designing a low-complexity projection based on scale factors, and adding violation control mechanisms into the loss function that minimizes error rates. Finally, we provide a detailed analysis of the computational complexity and analysis design of the proposed DU algorithm. Extensive simulations are conducted and the results demonstrate that the proposed DU algorithm can reach the optimal communication efficiency with only 1.1% violation rate for the five-layer DU. The DU algorithm also exhibits robustness in out-of-distribution tests and can be effectively trained with as few as 50 samples.

5G network operators are constantly under pressure from regulatory agencies who restrict the deployment of base stations (gNBs) close to incumbent services, such as radio astronomy services (RAS), to avoid interfering with them. Recent works for coexistence with RAS employ limited channel modeling approaches and use explicit out-of-band communication between the gNB and RAS. However, the strict latency requirements of 5G make explicit communications less desirable due to their overheads. Deploying gNBs close to RAS is also not yet supported. In this paper, we propose a proactive open-loop beamforming and interference nullification technique in which a gNB nullifies its downlink signal at a nearby RAS telescope while beamforming to its users (UEs). We estimate the gNB-RAS channel using raytracing on open-source terrain maps. We formulate a problem that maximizes the minimum rate for UEs under the constraint of maximum allowable interference power at the RAS and show that its time complexity scales cubically with the number of gNB antennas. Hence, we propose a heuristic solution that achieves 4 orders better latency and 100 dBW lower interference power than the max-min rate solution with similar sum rate on users. Our proposed solution consistently achieves less than -310 dBW interference power, even when the gNB-RAS distance is less than 1 km, satisfying international regulations, and is robust against moving users that vary in location and elevation.

The rapid increase in the number of satellites increases the amount of signal interference at ground stations, which are typically equipped with large aperture antennas (LAAs). In this work, we propose an interference mitigation solution for satellite communications by placing a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) at the first focal point of the LAA exploiting the existing structure and power supply. The goal is to use the STAR-RIS to improve the signal-to-interference-plus-noise ratio (SINR) of the desired satellite in the presence of multiple interferes. We turn the SINR maximization into fractional programming for which we use Dinkelbach's method to find the STAR-RIS coefficients. Our numerical results reveal the effectiveness of our approach in enhancing the SINR at the ground station.

The physical layer security of a non-orthogonal multiple access (NOMA) system is investigated. In order to maximize the security level of each NOMA user in the system, a multiobjective optimization (MOO) problem is proposed for handling the relationship among conflicting objectives. A deep neural network-based framework is designed for solving the associated MOO problem and for estimating the Pareto front. The framework shows that the estimated Pareto front is very close to the true one, thus allowing designers to select Pareto optimal solutions for striking the most appropriate compromise for all users, even on dynamically time-variant basis. Numerical results are provided for illustrating the associated trade-offs.

Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require complicated iterative algorithms, which cannot meet the stringent latency requirement by users with limited resources. Recently, data-driven methods are explored to alleviate this issue. However, they suffer from poor generalizability and scarce training data to achieve satisfactory performance. In this paper, we propose a fractional programming (FP) based deep unfolding (DU) approach to address resource allocation problem for a weighted sum rate optimization in RSMA. By carefully designing the penalty function, we couple the variable update with projected gradient descent algorithm (PGD). Following the structure of PGD, we embed a few learnable parameters in each layer of the DU network. Through extensive simulation, we have shown that the proposed model-based neural networks can yield similar results compared to the traditional optimization algorithm for RSMA resource management but with much lower computational complexity, less training data, and higher resilience to out-ofdistribution (OOD) data.

Physical-layer (PHY) attacks increasingly pose a threat to location-based services. To secure the localization mechanism against PHY attacks, we propose a novel framework based on localization and user-and-attacker detection, with the help of unsupervised machine learning (ML) algorithms and multiple signal classification (MUSIC) spectra. Our proposed framework consists of two stages: i) uplink localization and detection; and ii) downlink secure transmission. Noticeably, in the proposed framework, a reciprocal relationship between the localization mechanism and the user/attacker detection is developed, where the localization supports the detection and vice versa. This reciprocal relationship allows wireless systems to detect localization attacks and further localize the attacker. Through simulation, we show the efficacy of combining localization and detection in the uplink. We then demonstrate the benefit of employing both localization and movable-antenna arrays for secure downlink transmission.

Security threats of an IEEE 802.11ax (WiFi 6) system can occur throughout the layers of the protocol stack. Looking at the security aspect of the physical layer and from an attacker's perspective, we present how a spoofing attack can cause an access point to steer the beam dedicated to a legitimate user toward the attacker. More particularly, we propose the BeamSteal attack that takes advantage of the vulnerability of the beamforming feedback (BFF) mechanism. The purpose of the BeamSteal attack is to cause the access point to receive the wrong BFF -information-bearing bits and end up steering the beam toward the attacker instead of the legitimate user. To contrast the harmful effect of the proposed attack, we compare it with two benchmarks, namely the no attack case and the jamming attack case. Our numerical results show that the BeamSteal attack not only degrades the performance of the legitimate user, but also helps the attacker improve its performance significantly.

We propose a new reconfigurable intelligent surface (RIS) structure, referred to as interconnected RIS (I-RIS), which allows the RIS elements to be interconnected and share the inci-dent signals using simple binary radio frequency (RF) switches and mix them into the reflecting signals. This structure enables multi-user scaling and requires fewer elements (i.e., a compact structure) compared to standard RIS (S-RIS), which assumes no interconnection between the elements. The I-RIS compact design makes it practical for deployment on space-limited nodes, e.g., unmanned aerial vehicles (UAVs). Hence, in this work, we propose a beamforming design based on I-RIS in a multi-user network, where we use binary RF switches as RIS elements. We show that our switch-based I-RIS offers a higher gain compared to an S-RIS using phase shifters. Finally, we introduce two optimization methods, sigmoid filled function (SFF) and semi-definite binary optimization (SBO), to optimize the RIS elements and evaluate their performance in terms of sum-rate and comolexity.

We study the capacity region of the canonical two-user broadcast packet erasure channel when the erasure probabilities vary over the course of the communication block. In particular, we assume the network statistics may be in two distinct modes with a a priori known transition time between the two. We further consider the scenario in which the transmitter is informed of the delivery status of the previously transmitted packets through the feedback channel. We first derive a new set of the outer-bounds for this problem where the slope of the boundaries of the outer-bound region is dominated by the mode with the larger of the two erasure probabilities, and the corner points come from the average probability of each link being active. We show that under certain ratios of the lengths of the modes, these outer-bounds are achievable and thus the capacity region is known. We also discuss the behavior of the inner and outer bounds in other regimes and analyze the gap between the two. One key observation is that coding across the modes is superior to treating each mode as an individual problem.

We introduce an information-theoretic framework to dynamically pair up different reconfigurable intelligent surfaces (RISs) with wireless users with goal of maximizing the fundamental network capacity. We focus on a double RIS-aided broadcast packet network with two users. We show using a dynamic RIS-user association and an opportunistic protocol, the network capacity could be significantly enhanced and superior to other benchmarks with static associations. The results include new outer-bounds on network capacity and their achievability. We discuss the optimal RIS-user association.

In this article, we study reconfigurable intelligent surface (RIS)-assisted networks to support Internet of Things (IoT) devices. We propose RIS beamforming strategies to maximize sum rate and fairness and analyze the RIS location. We derive a theoretical lower bound of the minimum number of RIS elements needed to guarantee specific network performance metrics and validate our results via simulations. We present two RIS scenarios in this study, both with the same total number of RIS elements: 1) centralized RIS, where a single RIS assists the network and 2) distributed RIS, where each transmitter has its own dedicated RIS. We study addressing two practical challenges related to RIS elements and channel state information (CSI) assumptions. First, we consider hardware limitations by assuming that each RIS element is equipped with a discrete phase shifter (PS). Second, we investigate the impact of CSI perfectness and availability in the network; therefore, we evaluate the performance of the RIS-assisted network under two scenarios: 1) centralized RIS with imperfect global CSI and 2) distributed RIS, where imperfect local CSI is available at each transmitter.

As potential solutions to empower transmissions among the Internet of Things (IoT) devices, ambient radio frequency (RF) backscatter technology and reconfigurable intelligent surfaces (RISs) have recently attracted a lot of attention. To improve energy and spectrum efficiency, we design a system with a transmit antenna selection (TAS)-aided base station (BS) relying on nonorthogonal multiple access (NOMA), RIS, and backscatter communications (BackCom) with robust transmission links, allowing more users to be served effectively. We adopt the two-user grouping model in the coverage of main BS associated with a particular RIS and interference from coordinate BS is also considered to showcase differences among the performance of the two different kinds of users (i.e., the IoT user with and the IoT user without a dedicated RIS). To exhibit the system performance, we derive closed-form expressions for two main system performance metrics, namely, outage probability and ergodic capacity. A degraded performance is also considered for the case of imperfect successive interference cancellation (SIC). The benefits of the BackCom RIS-aided NOMA system are then demonstrated by comparing its performance to that of traditional orthogonal multiple access (OMA) RIS-aided backscatter systems. We then introduce analytical models to characterize the impact of the main factors on the outage performance and characterize the optimal performance in specific cases. Together with extensive simulations, our analysis shows that the system performance can be adjusted by controlling factors, including power allocation coefficients, the number of metasurfaces of RIS, and target rates.

Motivated by the DNA storage paradigm, we consider the torn-paper channel (TPC), which models data storage in long DNA molecules and breaks the input sequence into a random number of out-of-order variable-length non-overlapped fragments. We propose a computationally-efficient code construction for this model. More specifically, we introduce a family of nested Varshamov-Tenengolts (VT) codes to merge and sort the fragments in order to recover the stored data. We numerically show that our scheme (i) obtains rates that are higher than in prior results, (ii) has a decoding complexity that is cubic in the number of codeword fragments, which is significantly lower than the complexity of the brute-force approach, and (iii) offers decreasing and negligible error rates as the codeword length increases. We also propose a new construction for VT codes, quantify the number of required parity bits, and show that our approach requires fewer parity bits compared to known results.

Delayed channel state information (CSI) feedback was shown to be very helpful in enlarging the capacity region of the two-user broadcast packet erasure channel (PEC), even with single-user feedback. However, feedback link itself requires additional resources and may also cause additional delay to data transmission. In this work, we aim to study how to optimally tradeoff the number of feedback bits and the reliable forward communication rate. In our model, one receiver does not provide its CSI while the other one can alternate between delayed CSI feedback and no feedback. This model includes the intermittent single-user feedback as a special case. Our achievability is an extension of previous opportunistic network coding such that the network coding gain can still be enjoyed even when the single-user feedback is not always available. Interestingly, when two users have the same link erasure probabilities, boundaries of the capacity regions are identified since they can be achieved by the proposed schemes. Our results also reveal that even when the single-user feedback is alternating, strictly positive capacity benefits can be attained over the no-feedback capacity.

With the turn of new decade, wireless communications face a major challenge on connecting many more new users and devices, at the same time being energy efficient and minimizing its carbon footprint. However, the current approaches to address the growing number of users and spectrum demands, like Massive MIMO, demand exorbitant energy consumption. The reason is that traditionally Massive MIMO requires a digital beamforming architecture that needs a separate RF chain per antenna, so the power consumption scales with number of antennas. Instead, GreenMO creates a new Massive MIMO architecture with just a single physically laid RF chain, shared by all the antennas and introduces for the first time, the concept of virtualizing the RF chain hardware. That is, GreenMO creates an optimal number of virtual RF chains to serve a given number of spatial streams, depending on channel conditions and network load. Due to efficient, softwarized control over the number of virtual RF chains, GreenMO paves the way for green and flexible massive MIMO. We prototype GreenMO on a PCB with eight antennas and evaluate it with a WARPv3 SDR platform in an office environment. The results demonstrate that GreenMO is 3× more power-efficient than traditional Massive MIMO and 4× more spectrum-efficient than traditional OFDMA systems, while multiplexing 4 spatial streams, and can save upto 50% power in modern 5G NR base stations.

Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) systems have been implemented for over a decade, from transportation to military surveillance, and is proven worthy of integration in the next generation of wireless protocols. Though UAVs have immense potential, they have major drawbacks when it comes to real-world implementation, such as energy capacity, loss of signal quality, and spectrum limitations. To overcome these challenges, integration of UAVs with spectrum-efficient techniques, including cognitive radio (CR) and nonorthogonal multiple access (NOMA) has been proposed. In this article, we incorporate transmit-antenna selection (TAS) into an underlay cognitive radio NOMA network, which provides additional benefits through employing multiple-antenna-selection approach at the UAV with the goal of better serving the ground NOMA devices. The links associated with the multiantenna UAV are theoretically assumed to experience Nakagami-$m$fading distribution. We also emphasize the degraded performance caused by imperfect successive interference cancelation (SIC) when decoding signals at the ground NOMA devices. The closed-form expressions for the proposed model are derived to evaluate two main performance metrics, namely, the outage probability and the ergodic capacity. Monte Carlo simulations are performed to analyze the performance of the system in different scenarios. We observe that the power allocation factors for the devices in a group and the altitude of UAV have a noticeable impact on the performance of the system. Furthermore, the increase in the number of antennas at the UAV can complement these effects and further improve the system performance.

The physical-layer security (PLS) of a space–ground communication system is examined. To improve the security performance, a pair of reconfigurable intelligent surfaces (RISs) is integrated into the system and benchmarked against a scheme, where there is only a single RIS close to the ground station. As for the double-RIS scenario, we formulate a secrecy rate maximization problem, and then propose an alternating optimization (AO) algorithm for jointly optimizing three vectors, namely, the beamformer of the ground station and the reflecting vectors of two different RISs. Similarly, as for the single-RIS case, we also propose another AO algorithm for optimizing a pair of vectors, namely, the beamformer of the ground station and the reflecting vector of the single RIS. Both the double-RIS and the single-RIS AO algorithms are developed on the basis of the first-order Taylor expansion and Dinkelbach’s method, which allow us to approximate nonconvex optimization problems by convex ones. Our results demonstrate that the proposed double-RIS scheme outperforms the single-RIS benchmark scheme in terms of its security.

Digital and analog beamforming are well-known methods to suppress interference using multiple-antenna structures, but they have practical limitations: (i) Digital beamforming requires multiple analog-to-digital converters (ADCs) to enable digital conversion, which increases the cost and complexity; (ii) Although analog beamforming does not require expensive ADCs, it uses phase shifters, which cause quantization errors, insertion losses, and reduced power efficiency. In this paper, we consider a$K$-user uplink interference channel and propose a low-complexity algorithmic interference-suppression solution relying on simple switch-based reconfigurable antennas at the receivers. We utilize switches to enable/disable antennas to maximize each user’s signal-to-interference-plus-noise ratio (SINR). We present an optimization approach to approximate the optimal solution. To evaluate the results, we compare our method with relevant benchmarks. Moreover, we derive a lower bound on the minimum number of antenna elements per receiver to attain the desired SINR and verify the findings via simulations.

Most DNA sequencing technologies are based on the shotgun paradigm: many short reads are obtained from random unknown locations in the DNA sequence. A fundamental question, studied in [1], is what read length and coverage depth (i.e., the total number of reads) are needed to guarantee reliable sequence reconstruction. Motivated by DNA-based storage, we study the coded version of this problem; i.e., the scenario in which the DNA molecule being sequenced is a codeword from a predefined codebook. Our main result is an exact characterization of the capacity of the resulting shotgun sequencing channel as a function of the read length and coverage depth. In particular, our results imply that while in the uncoded case, O(n) reads of length greater than 2logn are needed for reliable reconstruction of a length-n binary sequence, in the coded case, only O(n/log n) reads of length greater than log n are needed for the capacity to be arbitrarily close to 1.

While encryption can protect network traffic against simple on-path eavesdropping attacks, it cannot prevent sophisticated traffic analysis (TA) attacks from inferring sensitive information. TA attackers utilize machine learning algorithms to learn the traffic patterns of a communication (e.g., a website visit) and then use these learned patterns to accurately identify similar communications (which website is being visited by a targeted user), even though packets are encrypted. In this paper, we propose a novel and effective defense approach to protect users' privacy against TA attacks. The proposed approach is based on two proactive defense paradigms: multipath routing and deception. The route randomization strategy distributes packets of a flow on multiple paths between a source and destination to restrict the amount of traffic that a TA adversary can collect from a flow. The deception strategy augments the randomization strategy by injecting fake packets among the real packets of a flow on different paths. Our focal research problem is to identify the optimal strategies for how real and fake packets must be distributed on multiple paths with different capacities to achieve maximum effectiveness against TA attacks. We formalize the problem as a zero-sum game and show that the water-filling distribution of real and fake packets provides an optimal defense solution. Through theoretical and experimental studies, we demonstrate that the proposed approach can significantly degrade the accuracy of the TA attacks. Unlike other defensive approaches in the literature, our approach works without manipulating the production traffic (e.g., delaying packets or padding), or requiring any real-time information about the protected traffic flows.

We present a new low-complexity interference management algorithm that exploits reconfigurable antennas, removes most of the interference signal power in a$K$-user interference channel, and achieves the promised but unrealized gain of interference alignment in low-to-medium signal-to-noise ratio (SNR) range. The reconfigurable antenna includes several elements, each controlled individually using a radio frequency (RF) switch. The antenna has only one RF chain, resulting in simple hardware design and enabling low-complexity algorithms. We exploit space-time diversity to find two separate combinations of activating or deactivating antenna elements to allow for interference neutralization/elimination. Finding such states is a cumbersome task for which we develop novel, efficient algorithms. We further devise simple encoding and decoding with short precoder length to exploit the benefits of our designs. Our implementation does not rely on channel state information at the transmitters; works at finite signal-to-noise ratios, unlike the typical degrees-of-freedom results; and works in slow-fading environments. We calculate the average achievable rates, the minimum number of elements to reach a specific level of interference suppression, and we provide outage analysis of our technique.

We study the capacity region of two-user erasure interference channels with random receiver-end side-information and delayed channel state knowledge at the transmitters. We present a new set of outer-bounds on the achievable rates when each receiver has access to a random fraction of the message intended for the other receiver. The outer-bounds reveal the significant potential rate boost associated with even a small amount of side-information at each receiver. The key in deriving the bounds is to quantify the baseline entropy that will always become available to the unintended receiver given the intermittent connectivity, random available side-information, and causal feedback. We will also present the achievability of these outer-bounds under certain conditions.

We study the problem of transmitting a message over a channel that randomly breaks the message block into small fragments, deletes a subset of them, and shuffles the remaining fragments. We characterize the capacity of the binary torn-paper channel under arbitrary fragment length distribution and fragment deletion probabilities. We show that, for a message with block length$n$, discarding fragments shorter than$\log(n)$does not affect the achievable rates, and that the capacity is given by a simple closed-form expression that can be understood as “coverage minus reordering-cost”.

We present a new technique to obtain outer-bounds on the capacity region of networks with ultra low-rate feedback. We establish a connection between the achievable rates in the forward channel and the minimum distortion that can be attained over the feedback channel.

Recent information-theoretic studies have resulted in several interference management (IM) techniques that promise significant capacity improvements over interference avoidance techniques. However, in practice, the stable throughput region is a more relevant metric compared to the capacity region. In this work, we focus on the stable throughput region of a two-pair intermittent interference network with distributed transmitters and propose a queue-based transmission protocol in different regimes to handle the data between queues. In this context, we translate physical-layer IM protocols to accommodate stochastic message arrivals. To evaluate our proposed techniques, we compare the stable throughput region to the capacity region and show, through simulations, that the stable throughput region matches the capacity region when the latter is known. We show that in order to achieve the optimal stable throughput region, new ingredients are needed when compared to prior results. We quantify the trade-off between encoding/decoding complexity of the proposed scheme (in terms of number of required algebraic operations), and the achievable rates. Finally, we study the lifetime of messages (i.e. the duration from arrival to successful delivery) versus the total communication time, and we observe that the average lifetime scales as the square root of the total communication time.

Achievable data rates in wireless systems rely heavily on the available channel state information (CSI) throughout the network. However, feedback links, which provide this information, are scarce, unreliable, and subject to security threats. In this work, we study the impact of having intermittent feedback links on the capacity region of the canonical two-user erasure broadcast channels. In our model, at any time instant, each receiver broadcasts its CSI, and at any other node, this information either becomes available with unit delay or gets erased. For this setting, we develop a new set of outer bounds to capture the intermittent nature of the feedback links. These outer bounds depend on the probability that the CSI from both receivers are erased at the transmitter. In particular, if at any time, the CSI from at least one of the two receivers is available at the other two nodes, then the outer-bounds match the capacity with global delayed CSI. We also provide capacity-achieving transmission strategies under certain scenarios, and we establish a connection between this problem and Blind Index Coding with feedback.

We characterize the capacity region of the two-user broadcast packet erasure channel (PEC) with single-user delayed channel state information (CSI). More precisely, we assume one receiver does not provide its channel state to the other two nodes (the other receiver and the transmitter), while the other receiver reveals its state globally with unit delay. This is a hybrid CSI at the transmitter (CSIT) setting where the transmitter has the delayed CSI of one user but not the other. Previous results developed opportunistic network coding schemes for this setting, which strictly enlarge the achievable rate region compared to the no-CSIT baseline. Characterization of the capacity region with single-user delayed CSI, however, remained open. In this work, we develop an improved achievability strategy and show that the capacity region, surprisingly, matches that of the broadcast PEC with global delayed CSI of both users. The key to such improvement over previous results is a new precoding strategy for the retransmission phase of the opportunistic network coding scheme. It harnesses the single-user delayed CSI in the retransmission phase, so that interference from the feedback receiver can be aligned at the other receiver. Besides the broadcast PEC with two private messages, an extension to a model with an additional common message is also provided and the corresponding capacity region with single-user CSI also matches that with global delayed CSI. Finally, further extensions to three-user cases are also provided.

We consider the problem of communicating over a channel that randomly “tears” the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length$n$and pieces of length${\mathrm{ Geometric}}(p_{n})$, we characterize the capacity as$C = e^{-\alpha }$, where$\alpha = \lim _{n\to \infty } p_{n} \log n$. Our results show that the case of${\mathrm{ Geometric}}(p_{n})$-length fragments and the case of deterministic length-$(1/p_{n})$fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity.

Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a Gaussian Process (GP) whose covariance is selected from a set of standard covariance functions. From a weight-space view, this models the objective as a linear function in a feature space implied by the given covariance $K$, with an arbitrary Gaussian weight prior ${\bf w} \sim ormdist ({\bf 0},{\bf I})$. In many practical applications there is data available that has a similar (covariance) structure to the objective, but which, having different form, cannot be used directly in standard transfer learning. In this paper we show how such auxiliary data may be used to construct a GP covariance corresponding to a more appropriate weight prior for the objective function. Building on this, we show that we may accelerate BO by modeling the objective function using this (learned) weight prior, which we demonstrate on both test functions and a practical application to short-polymer fibre manufacture.

5G downlink communications are expected to suffer more from the intermittent link connectivity, and studying the erasure broadcast channel (BC) provides the fundamental understanding of their performance. Recently, the capacity region of two-user erasure BC with single-user delayed channel state information (CSI) was characterized, where one receiver does not provide its channel state to the transmitter and the other receiver, while the other receiver feeds back its state globally with unit delay. Surprisingly, the capacity region with single-user delayed CSI matches that of the erasure BC with global delayed CSI of both users. When two links have same erasure probabilities, this result is valid even when a common message intended for both users is required besides two private messages. The capacity in this case is easily achieved by adding a phase multicasting the common message after the transmission of private messages. However, when erasure probabilities are unequal, this scheme is clearly not capacity-achieving since the rate of the last phase will be limited by the weaker user having larger erasure probability. We propose a scheme which transmits the common message with rate higher than the link capacity of the weaker user in the last phase but still can ensure the decodability. The key is to simultaneously create equations of the common message at the weaker receiver and re-transmit private bits before the last phase. The proposed scheme achieves the converse under unequal link erasure probabilities, and the capacity with common and private messages is fully characterized under single-user delayed CSI.

We consider the problem of communicating over a channel that randomly “tears” the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length n and pieces of length Geometric(pn), we characterize the capacity as C = e-α, where α = limn→∞pnlogn, Our results show that the case of Geometric (Pn)-length fragments and the case of deterministic length-( 1/pn) fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity.

The radiation pattern of transmit antennas varies and fluctuates as receivers change their location, other objects move around, and due to the antenna design itself. In this paper, we demonstrate how this observation can be exploited to align most of the interference signal power and significantly increase the average achievable communication rates. More precisely, in the context of K-user interference channels, we propose a blind interference alignment scheme that combines multi-layer coding at the transmitters and a post-processing methodology at the receivers to align a significant portion of the interference signal power. Our scheme does not rely on any channel state information (CSI), hence the term blind, and only relies on the statistics of the radiation pattern fluctuations. Our proposed communication methodology overcomes some of the barriers in practical implementation of the interference alignment concept. Due to the complexity of the expressions, in this work, we numerically evaluate the achievable rates in different scenarios, demonstrate the gains of our proposed strategy, and compare our results to the prior works with perfect CSI.

The observed coherence times associated with different wireless transmitters at any given user may vary at different rates. We demonstrate how these variations can be exploited for interference management. More precisely, we propose a new opportunistic blind interference alignment (BIA) strategy in the context of K -user interference channels that exploits these variations in coherence times and provides significant data rate gains. We first provide a proof-of-concept setup in which the information about coherence time variations is available non-causally to the transmitters, and we demonstrate how transmitters and receivers can perform pre-coding and post-processing, respectively, to align a considerable part of the interference signal power. We note that in this non-causal scenario, no channel state information is available to the transmitters, and we make no specific assumption on the channel distributions. We take the key ideas of this scenario and consider a K -user interference channel in which the direct links vary at a higher pace compared to the cross links. This assumption is motivated by considering mobile users, or by using our proposed transmit antenna for stationary or low-mobility users. We show how to eliminate the need for non-causal knowledge of coherence time variations, and still provide significant capacity gains.

Racetrack memory is an exciting emerging memory technology with the potential to offer far greater capacity and performance than other non-volatile memories. Racetrack memory has an unusual error model, though, which precludes the use of the typical error coding techniques used by architects. In this paper, we introduce GreenFlag, a coding scheme that combines a new construction for Varshamov-Tenegolts codes with specially crafted delimiter bits that are placed between each codeword. GreenFlag is the first coding scheme that is compatible with 3D racetrack, which has the benefit of very high density but the limitation of a single read/write port per track. Based on our implementation of encoding/decoding hardware, we analyze the trade-offs between latency, code length, and code rate; we then use this analysis to evaluate the viability of racetrack at each level of the memory hierarchy.

We characterize the capacity region of the two-user erasure broadcast channel (BC) with single-user delayed channel state information (CSI). More precisely, we assume one receiver does not provide its channel state to the other two nodes (the other receiver and the transmitter), while the other receiver reveals its state globally with unit delay. This is a "DN" hybrid CSI at the transmitter (CSIT) setting where the transmitter has the delayed CSI of one user but not the other. Previous results developed opportunistic network coding schemes for this DN setting, which strictly enlarge the achievable rate region compared to the no-CSIT baseline. Characterization of the capacity region for the DN setting, however, remained open. In this work, we develop an improved achievability strategy and show that the capacity region, surprisingly, matches that of the erasure BC with global delayed CSI of both users. The key to such improvement over previous results is a new precoding strategy for the retransmission phase of the opportunistic network coding scheme. It harnesses the single-user delayed CSI in the retransmission phase, so that interference from the "D" receiver can be aligned at the "N" receiver. Besides erasure BCs with two private messages, an extension to BCs with an additional common message is also provided.

Recently, we showed that, rather surprisingly, the capacity region of the two-user erasure broadcast channel with global delayed channel state information (CSI) can be achieved with single-user delayed CSI only. More precisely, we assumed one receiver does not provide its channel state to the other two nodes (the other receiver and the transmitter), while the other receiver reveals its state globally with unit delay. In this work, we consider a more general setting in which feedback links are intermittent. To be precise, at any time instant, each receiver broadcasts its CSI, and this information either becomes available to the other two nodes or gets erased. For this setting, we develop a new set of outer bounds to capture the intermittent nature of the feedback links. These outer bounds depend on the probability that both feedback links are erased rather than the individual erasure probability of each feedback link. This result matches our earlier findings for the single-user delayed CSI scenario. We also provide a capacity-achieving recursive communication protocol for the scenario in which feedback links are fully correlated.

We characterize the degrees-of-freedom (DoF) region of a class of two-unicast wireless networks under the assumption of delayed channel state information at the transmitters. We consider a layered topology with arbitrary connectivity, and we introduce new outer-bounds on the DoF region of such networks through the graph-theoretic notion of bottleneck nodes. Such nodes act as informational bottlenecks only under the assumption of delayed channel state information. We also present new transmission schemes that achieve the outer-bounds. We show that unlike the instantaneous channel state information model, the sum DoF of two-unicast networks with delayed channel knowledge can take an infinite set of values. In this paper, we compare our results to the best previously known outer-bounds, and we show that the gap can be arbitrary large in favor.

In multi-user wireless packet networks, interference, typically modeled as packet collision, is the throughput bottleneck. Users become aware of the interference pattern via feedback and use this information for contention resolution and packet retransmission. Conventional random access protocols interrupt communication to resolve contention, which reduces network throughput and increases latency and power consumption. In this paper, we take a different approach, and we develop opportunistic random access protocols rather than pursuing conventional methods. We allow wireless nodes to communicate without interruption and to observe the interference pattern. We then use this interference pattern knowledge and channel statistics to counter the negative impact of interference. We prove the optimality of our protocols using an extremal rank-ratio inequality. An important part of our contributions is the integration of spatial correlation in our assumptions and results. We identify spatial correlation regimes in which inherently outdated feedback becomes as good as idealized instantaneous feedback and correlation regimes in which feedback does not provide any throughput gain. To better illustrate the results, and as an intermediate step, we characterize the capacity region of finite-field spatially correlated interference channels with delayed channel state information at the transmitters.

In multi-user wireless packet networks interference is the throughput bottleneck. Users become aware of the interference pattern via feedback and use this information for contention resolution and for packet retransmission. We consider networks with spatially correlated wireless links, and we develop an opportunistic automatic repeat request function for these networks. We prove the optimality of our protocol using an extremal rank-ratio inequality for spatially correlated channels.

We extend the lifetime of Flash memory in embedded processors by exploiting the fact that data from sensors is inherently analog. Prior work in the computer architecture community has assumed that all data is digital and has overlooked the opportunities available when working with analog data, such as the data recorded by sensors. In this paper, we introduce redundancy into the quantization of sensor data in order to provide several alternative representations. Notably, we tradeoff distortion-the difference between the sensed analog value and the digital quantization of that value-to improve lifetime. Our simulations show that when combining rate, distortion, and lifetime tradeoffs we can extend Flash lifetime at a far smaller capacity cost compared to prior work. More specifically the simulated system shows that it is possible to achieve up to 2.75× less capacity cost compared to redundant Flash memory and 1.29× less capacity cost compared to the state of the art coding schemes.

In this paper, we introduce Jenga, a new scheme for protecting 3D DRAM, specifically high bandwidth memory (HBM), from failures in bits, rows, banks, channels, dies, and TSVs. By providing redundancy at the granularity of a cache block-rather than across blocks, as in the current state of the art-Jenga achieves greater error-free performance and lower error recovery latency. We show that Jenga's runtime is on average only 1.03x the runtime of our Baseline across a range of benchmarks. Additionally, for memory intensive benchmarks, Jenga is on average 1.11x faster than prior work.

We study the capacity region of the two-user binary fading (or erasure) interference channel, where the transmitters have no knowledge of the channel state information. We develop new inner bounds and outer bounds for this problem. We identify three regimes based on the channel parameters: weak, moderate, and strong interference regimes. Interestingly, this is similar to the generalized degrees of freedom of the two-user Gaussian interference channel, where transmitters have perfect channel knowledge. We show that for the weak interference regime, treating interference as erasure is optimal while for the strong interference regime, decoding interference is optimal. For the moderate interference regime, we provide new inner and outer bounds. The inner bound is based on a modification of the Han-Kobayashi scheme for the erasure channel, enhanced by time-sharing. We study the gap between our inner bound and our outer bounds for the moderate interference regime and compare our results to that of the Gaussian interference channel. Deriving our new outer bounds has three main steps. We first create a contracted channel that has fewer states compared with the original channel, in order to make the analysis tractable. We then prove the correlation lemma that shows an outer bound on the capacity region of the contracted channel and also serves as an outer bound for the original channel. Finally, using the conditional entropy leakage lemma, we derive our outer bound on the capacity region of the contracted channel.

Motivated by embedded systems and datacenters that require long-life components, we extend the lifetime of Flash memory using rewriting codes that allow for multiple writes to a page before it needs to be erased. Although researchers have previously explored rewriting codes for this purpose, we make two significant contributions beyond prior work. First, we remove the assumption of idealized -- and unrealistically optimistic -- Flash cells used in prior work on endurance codes. Unfortunately, current Flash technology has a non-ideal interface, due to its underlying physical design, and does not, for example, allow all seemingly possible increases in a cell's level. We show how to provide the ideal multi-level cell interface, by developing a virtual Flash cell, and we evaluate its impact on existing endurance codes. Our second contribution is our development of novel endurance codes, called Methuselah Flash Codes (MFC), that provide better cost/lifetime trade-offs than previously studied codes.

Fast fading wireless networks with delayed knowledge of the channel state information have received significant attention in recent years. An exception is networks where channels are spatially correlated. This paper characterizes the capacity region of two-user erasure interference channels with delayed knowledge of the channel state information and spatially correlated channels. There are instances where spatial correlation eliminates any potential gain from delayed channel state information and instances where it enables the same performance that is possible with instantaneous knowledge of channel state. The key is an extremal entropy inequality for spatially correlated channels that separates the two types of instances. It is also shown that to achieve the capacity region, each transmitter only needs to rely on the delayed knowledge of the channels to which it is connected.

We consider the problem of multiple-input single-output broadcast channels with Rayleigh fading where the transmitter has access to delayed knowledge of the channel state information. We first characterize the capacity region of this channel with two users to within constant number of bits for all values of the transmit power. The proposed signaling strategy utilizes the delayed knowledge of the channel state information and the previously transmitted signals, in order to create a signal of common interest for both receivers. This signal would be the quantized version of the summation of the previously transmitted signals. A challenge that arises in deriving the result for finite signal-to-noise ratio regimes is the correlation that exists between the quantization noise and the signal. To guarantee the independence of quantization noise and signal, we extend the framework of lattice quantizers with dither together with an interleaving step. For converse, we use the fact that the capacity region of this problem is upper bounded by the capacity region of a physically degraded broadcast channel with no channel state information where one receiver has two antennas. Then, we derive an outer bound on the capacity region of this degraded broadcast channel. Finally, we show how to extend our results to obtain the approximate capacity of the $K$ -user multiple-input single-output broadcast channel with delayed knowledge of the channel state information at the transmitter to within $2 \log _{2} ( K \,\, + 2 )$ bits/s/Hz.

We study the capacity region of two-user erasure interference channels with local delayed channel state information at the transmitters. In our model, transmitters have local mismatched outdated knowledge of the channel gains. We propose a transmission strategy that only relies on the delayed knowledge of the outgoing links at each transmitter and achieves the outer bound for the scenario in which transmitters learn the entire channel state with delay. Our result reveals the subset of the channel state information that affects the capacity region the most. We also identify cases in which local delayed knowledge of the channel state does not provide any gain over the zero knowledge assumption. To do so, we revisit a long-known intuition about interference channels that as long as the marginal distributions at the receivers are conserved, the capacity remains the same. We take this intuition and impose a certain spatial correlation among channel gains such that the marginal distributions remain unchanged. Then, we provide an outer bound on the capacity region of the channel with correlation that matches the capacity region when transmitters do not have access to channel state information.

The coherence time of a wireless channel is often smaller than the delay with which channel state information is available at transmitters. In this paper, we aim to find the most important subset of the channel state information that transmitters need to learn with delay. We characterize the capacity region of the two-user interference channel with local delayed channel state information at transmitters. We propose a transmission strategy that only relies on the delayed knowledge of the outgoing links at each transmitter and achieves the outer-bound for the scenario in which transmitters learn the entire channel state with delay. We also show that the delayed knowledge of the outgoing links is the minimum delayed knowledge that is required to outperform the no knowledge assumption.

When several wireless users are sharing the spectrum, packet collision is a simple, yet widely used model for interference. Under this model, when transmitters cause interference at any of the receivers, their collided packets are discarded and need to be retransmitted. However, in reality, that receiver can still store its analog received signal and utilize it for decoding the packets in the future (for example, by successive interference cancellation techniques). In this work, we propose a physical layer model for wireless packet networks that allows for such flexibility at the receivers. We assume that the transmitters will be aware of the state of the channel (i.e. when and where collisions occur, or an unintended receiver overhears the signal) with some delay, and propose several coding opportunities that can be utilized by the transmitters to exploit the available signal at the receivers for interference management (as opposed to discarding them). We analyze the achievable throughput of our strategy in a canonical interference channel with two transmitter-receiver pairs, and demonstrate the gain over conventional schemes. By deriving an outer-bound, we also prove the optimality of our scheme for the corresponding model.

We characterize the capacity region of the symmetric two-user Binary Fading Interference Channel where transmitters have no knowledge of the channel state information. We show that the entire capacity region is achieved by applying point-to-point erasure codes with appropriate rates at each transmitter, and using either treat-interference-as-erasure or interference-decoding at each receiver, based on the channel parameters. The result is obtained by developing a novel outer-bound that has three main steps. We first create a contracted channel that has fewer states compared to the original channel, in order to make the analysis tractable. Using a Correlation Lemma, we then show that an outer-bound on the capacity region of the contracted channel also serves as an outer-bound for the original channel. Finally, using a Conditional Entropy Leakage Lemma, we derive our outer-bound on the capacity region of the contracted channel, and show that it coincides with the achievable region by either treat-interference-as-erasure or interference-decoding at each receiver.

To study the effect of lack of up-to-date channel state information at the transmitters (CSITs), we consider two-user binary fading interference channels with Delayed-CSIT. We characterize the capacity region for such channels under homogeneous assumption, where channel gains have identical and independent distributions across time and space, eliminating the possibility of exploiting time/space correlation. We introduce and discuss several novel coding opportunities created by outdated CSIT that can enlarge the achievable rate region. The capacity-achieving scheme relies on accurate combination, concatenation, and merging of these opportunities, depending on the channel statistics. The outer-bounds are based on an extremal inequality we develop for a binary broadcast channel with delayed-CSIT. We further extend the results and characterize the capacity region when output feedback links are available from the receivers to the transmitters in addition to the delayed knowledge of the channel state information. We also discuss the extension of our results to the nonhomogeneous setting.

In this paper, we study the capacity region of the two-user binary fading interference channel with delayed network state information at the transmitters and a noiseless output feedback link from each receiver to its corresponding transmitter. Our results include a new achievability strategy that systematically utilizes the stale network state information and the previously received signals at the receivers, in order to enhance the achievable rate region. We also derive new outer-bounds on the capacity region of such network, and we show that the delay in learning network state information results in some loss in the capacity region.

We consider the two-user interference channel with rate-limited feedback. Related prior works focus on the case where feedback links have infinite capacity, while no research has been done for the rate-limited feedback problem. Several new challenges arise due to the capacity limitations of the feedback links, both in deriving inner bounds and outer bounds. We study this problem under three different interference models: the El Gamal-Costa deterministic model, the linear deterministic model, and the Gaussian model. For the first two models, we develop an achievable scheme that employs three techniques: Han-Kobayashi message splitting, quantize-and-binning, and decode-and-forward. We also derive new outer bounds for all three models and we show the optimality of our scheme under the linear deterministic model. In the Gaussian case, we propose a transmission strategy that incorporates lattice codes, inspired by the ideas developed in the first two models. For symmetric channel gains, we prove that the gap between the achievable sum rate of the proposed scheme and our new outer bounds is bounded by a constant number of bits, independent of the channel gains.

In this paper we study the effect of rate-limited feedback on the sum-rate capacity of the deterministic interference channel. We characterize the sum-rate capacity of this channel in the symmetric case and show that having feedback links can increase the sum-rate capacity by at most the rate of the available feedback. Our proof includes a novel upper-bound on the sum-rate capacity and a set of new achievability strategies.