Successive Interference Cancellation (SIC) is a powerful technique for managing interference in wireless networks, yet its optimal deployment in decentralized environments remains a challenge. This study investigates joint power and rate allocation in a two-user Gaussian interference channel incorporating SIC at the receivers. We characterize the global optimal solutions of the problem, and recognizing the limitations of centralized coordination, we introduce a novel decentralized algorithm for a symmetric channel configuration. Numerical results demonstrate that even without global Channel State Information, our proposed algorithm significantly outperforms traditional benchmarks, such as Orthogonal Access which suffers from temporal underutilization or greedy strategies that fail to exploit SIC gains.

Coded caching utilizes pre-fetching during off-peak hours and multi-casting for delivery in order to balance the traffic load in communication networks. Several works have studied the achievable peak and average rates under different conditions: variable file lengths or popularities, variable cache sizes, decentralized networks, etc. However, very few have considered the possibility of heterogeneous user profiles, despite modern content providers are investing heavily in categorizing users according to their habits and preferences. This paper proposes three coded caching schemes with uncoded pre-fetching for scenarios where end users are grouped into classes with different file demand sets (FDS). One scheme ignores the difference between the classes, another ignores the similarities between them and the third decouples the delivery of files common to all FDS from those unique to a single class. The transmission rates of the three schemes are compared with a lower bound to evaluate their gap to optimality, and with each other to show that each scheme can outperform the other two when certain conditions are met.

The most commonly used setting in the coded caching literature consists of the following four elements: (i) homogeneous file sizes, (ii) homogeneous cache sizes, (iii) user-independent homogeneous file popularity (i.e., all users share the same file preference), and (iv) worst-case rate analysis. While recent results have relaxed some of these assumptions, deeper understanding of the full heterogeneity setting is still much needed since traditional caching schemes place little assumptions on file/cache sizes and almost always allow each user to have his/her own file preference through individualized file request prediction. Taking a microscopic approach, this paper characterizes the exact capacity of the smallest 2-user/2-file ($N=K=2$) problem but under the most general setting that simultaneously allows for (i) heterogeneous files sizes, (ii) heterogeneous cache sizes, (iii) user-dependent file popularity, and (iv) average-rate analysis. Solving completely the case of$N=K=2$could shed further insights on the performance and complexity of optimal coded caching with full heterogeneity for arbitrary$N$and$K$.

Future wearable and portable devices with multiple transmit antennas operating below 6 GHz are constrained by regulatory limitations on the level of electromagnetic radiation a user can be exposed to, measured using the specific absorption rate (SAR). Signaling designs that are optimized to include SAR constraints can improve the performance of uplink transmission. These signaling schemes could include closed-loop beamforming, closed-loop precoding, and space-time coding, which have all been shown to achieve increased rates when optimized as a function of SAR. Previous research addressed SAR constrained optimization only within a single coherence time block. In this paper, we present transmit policies that dynamically allocate user electromagnetic radiation exposure over time. We propose three exposure allocation methods - optimal, uniform, and asymptotic - in the practical case with causal channel state information (CSI), and an on-off transmission approach for the low SAR-to-noise ratio regime. Our results demonstrate that the performance of SAR-aware transmission can be further improved by exploiting frequency and time diversity.

Coded caching is a technique for reducing congestion in communication networks by prefetching content during idle periods and exploiting multicasting opportunities during periods of heavy traffic. Most of the existing research in this area has focused on minimizing the worst case (i.e., peak) rate in a broadcast link with multiple identically distributed user requests. However, modern content delivery networks are investing very heavily in profiling their users and predicting their preferences. The minimal achievable rate of a coded caching scheme with heterogeneous user profiles is still unknown in general. This paper presents the first steps towards solving that problem by analyzing the case of two users with distinct but overlapping demand sets. Specifically, it provides a complete characterization of the uniform-average-rate capacity when the sets overlap in just one file and shows that such capacity can be achieved with selfish and uncoded prefetching. Then, it characterizes the same capacity under selfish and uncoded prefetching when the demand sets overlap in two or more files. The paper also provides explicit prefetching schemes that achieve those capacities. All our results allow for arbitrary (and not necessarily identical) users' cache sizes and number of files in each demand set.

Coded caching utilizes pre-fetching during off-peak hours and multi-casting for delivery in order to balance the traffic load in communication networks. Most of the existing research focuses on reducing the peak transmission rates with homogeneous file popularities, despite modern systems are often able to categorize users by their preferences and tend to care more about the average rather than peak rate. This paper considers a scenario with heterogeneous user profiles and analyzes the average transmission rates for three coded caching schemes under the assumption that each user can only request a subset of the total available files. In addition, it evaluates the average rate of the three schemes when the number of files is much larger than the number of users and the amount of cache memory. Furthermore, it proposes methods of cache allocations which minimize the average rate when the users have relatively small storage. Our results demonstrate connections between cache distributions which result in minimal average rate and peak rate.

An optical wireless communication approach that exploits the photo-luminescent radiation of LEDs is presented. In this approach the photo-luminescence of an array of LEDs is modulated by varying the impedance connected to the LEDs. The LEDs are also employed to harvest radiant energy making possible fully passive optical communications tags. Possible applications of this approach include short-range underwater communications. Initial experimental results suggest that communication speeds of few kilobits per second can be achieved.

Coded caching has been proven to be a useful technique for reducing traffic in networks with point-to-multipoint links. The key idea is to pre-fetch popular content at the end users during off-peak hours, and encode transmissions when resources are scarce in such a way that different users can obtain different information from the same packet.Prior works have proposed placement and delivery algorithms to address a wide range of scenarios with varying file popularities, file sizes, and cache capacities. Both centralized and distributed algorithms have been proposed, and their performance limits have been characterized in terms of peak and average transmission rates. However, existing works have focused on the case where all the end users present an identical distribution of requests; in other words, the popularity of a given file is identical for all users. This assumption is overly simplistic in modern networks, where network operators often build detailed individual profiles on each user.This paper proposes and compares the peak rate of three coded caching schemes when the end users can be classified into distinct groups with different distribution of demands. Specifically, the first scheme ignores the differences between user profiles, the second performs independent caching and delivery of each class of files, and the third ignores the similarities between user profiles. The second scheme derives a method to partition the cache between widely popular files and files that may only be requested by a subset of the users. Our analysis yields some counter-intuitive results.

Content delivery networks store information distributed across multiple servers, so as to balance the load and avoid unrecoverable losses in case of node or disk failures. Coded caching has been shown to be a useful technique which can reduce peak traffic rates by pre-fetching popular content at the end users and encoding transmissions so that different users can extract different information from the same packet. On one hand, distributed storage limits the capability of combining content from different servers into a single message, causing performance losses in coded caching schemes. But, on the other hand, the inherent redundancy existing in distributed storage systems can be used to improve the performance of those schemes through parallelism. This paper designs coded caching and delivery schemes tailored towards systems where the library is distributed across multiple servers, possibly with some redundancy in the form of maximum distance separable (MDS) erasure codes. Different schemes are proposed based on the capacity of the users’ caches, as well as the number of parity servers. The main focus is on scenarios with one (RAID-4) or two (RAID-6) parity servers, but the paper also includes simple extensions for cases with more than two or no parity servers at all. The proposed schemes are shown to reduce the worst case latency, or equivalently the peak transmission rate from any server, below that of state-of-the-art algorithms.

There has been a strong interest in understanding hybrid precoding tradeoffs for millimeter wave (mmW) multi-input multi-output (MIMO) systems. A common assumption in most of these works is that the analog part of the hybrid precoder can only be designed with phase shifters. The consequent search for analog and digital precoding matrices is solved with different black box-type optimization algorithms. In contrast, this work motivates an analog precoding structure at the base-station end that can be realized with both phase shifters and gain controls. Such a structure is necessary for interference management in multi- user transmissions and is easily realized with low complexity and cost. We then propose a feedback framework of the top-P beams over a beam alignment phase from each user. This framework allows the base-station to reconstruct the channel matrix between it and each user, and to manage interference with a simple zeroforcing solution. Such a structured solution is implemented with the amplitude and phase control of the analog part of the hybrid precoder. We finally illustrate the performance improvement with the proposed solution over simpler constructions such as beam steering that can be implemented with phase shifters alone.

Wireless networks have been adjusting their transmit power, modulation order, and coding rate based on the channel conditions for a long time. Such adaptive protocols aim to maximize the overall throughput by striking a trade-off between transmitting as much information as possible and minimizing the probability of loosing such information. However, there is little literature on adaptive retransmissions when failures occur. This paper studies the trade-offs that hybrid automatic repeat request (HARQ) protocols face when choosing the type and amount of incremental redundancy (IR) that should be sent when the decoding of a data block fails at the receiver. It proposes a method to optimize such choice for a system with non-ideal error correcting codes (ECC) and limited feedback capabilities. Additionally, it is shown through simulations that the overall data rate can be significantly increased by bundling the acknowledgements.

The aggressive scaling of NAND flash memories has caused significant degradation in their reliability and endurance. One of the dominant factors in this degradation is the inter-cell-interference, by which the programming of a cell can affect nearby neighboring cells corrupting the information that they store. This paper proposes a new data representation scheme, which increases endurance and significantly reduces the probability of error caused by inter-cell-interference. The method is based on using an orthogonal code to spread each bit across multiple cells, resulting in lower variance for the voltages being programmed in the cells. This new data representation method is also shown to present many of the advantages that spreading sequences bring to wireless communications. For example, multiple information sequences can be written on the same cells at different times without interfering with each other. It also allows storing additional information on an already programmed memory in such a way that the new information is hidden by the noise.

The aggressive scaling of NAND flash memories has caused significant degradation in their reliability and endurance. One of the dominant factors in this degradation is the inter-cell-interference (ICI), by which the programming of a cell can affect near-by neighboring cells corrupting the information that they store. This paper proposes a new data representation scheme which increases endurance and significantly reduces the probability of error caused by ICI. The method is based on using an orthogonal code to spread each bit across multiple cells, resulting in a more uniform distribution of voltages being programmed in the cells.

One of the main challenges keeping flash memories from achieving widespread distribution is their limited endurance. The programming and erasing from re-writes damages the cells, progressively increasing the number of errors until information can no longer be stored reliably. Most manufacturers employ powerful ECC techniques, but there is a limit to the number of errors that these can correct. When the number of errors goes beyond the capability of the ECC, it is necessary to invoke RAID, which introduces significant latency and jeopardizes the speed of the memory if used too often. This paper introduces a method for estimating the BER that a flash page will exhibit after retention and uses this estimate for wear leveling. Instead of leveling out the number of PE cycles in all the blocks, the proposed scheme attempts to wear all the blocks evenly so that they all suffer the same BER. Additionally, the estimate will be used to detect bad blocks, those likely to exhibit a number of errors beyond the ECC correction capability, and retire them from further use.

Two important performance metrics for a storage system are the latency associated with retrieving data from its storage medium and the effective lifetime of its storage medium. Both metrics are directly affected by the number of raw read errors (i.e. errors prior to exploiting error-correction mechanisms). This paper focuses on NAND flash memories, where a read is performed by comparing stored voltages with a threshold voltage. The unwanted variation of stored voltages causes read errors. This paper identifies number of flash program-erase (PE) cycles, time elapsed between writing and reading, and the page number (physical location) as the main sources of voltage variations. It then proposes a method for learning how read thresholds should vary with these parameters such that the storage controller can dynamically vary thresholds and minimize read errors. Lab experiments show that at the flash end-of-life, the proposed method lowers the raw bit-error-rate up to a factor of 6, as compared to manufacturer's default read settings.

A primary source of increased read time on NAND flash comes from the fact that, in the presence of noise, the flash medium must be read several times using different read threshold voltages for the decoder to succeed. This paper proposes an algorithm that uses a limited number of rereads to characterize the noise distribution and recover the stored information. Both hard and soft decoding are considered. For hard decoding, this paper attempts to find a read threshold minimizing bit error rate (BER) and derives an expression for the resulting codeword error rate. For soft decoding, it shows that minimizing BER and minimizing codeword error rate are competing objectives in the presence of a limited number of allowed rereads, and proposes a tradeoff between the two. The proposed method does not require any prior knowledge about the noise distribution but can take advantage of such information when it is available. Each read threshold is chosen based on the results of previous reads, following an optimal policy derived through a dynamic programming backward recursion. The method and results are studied from the perspective of an SLC Flash memory with Gaussian noise, but this paper explains how the method could be extended to other scenarios.

This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their distance from the destination and creates a structured multi-hop configuration in which each group can relay its neighbor's data. The large number of active radio nodes and the common direction of communication toward a single destination are exploited to reuse the limited spectrum resources in spatially separated groups. Spectrum allocation constraints among groups are then embedded in a joint routing and resource allocation framework to optimize the route and amount of resources allocated to each node. The solution to this problem uses coordination among the lower-layers of the wireless-network protocol stack to outperform conventional approaches where these layers are decoupled. Furthermore, the structure of this problem is exploited to obtain a semi-distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) where each node can optimize its resources independently based on local channel information.

On NAND flash, a primary source of increased read time comes from the fact that in the presence of noise, the flash medium must be read several times using different read threshold voltages to find the optimal read location, which minimizes bit-error-rate. This paper proposes an algorithm to estimate the optimal read threshold in a fast manner using a limited number of re-reads. Then it derives an expression for the resulting BER in terms of the minimum possible BER. It is also shown that minimizing BER and minimizing codeword-error-rate are competing objectives in the presence of a limited number of allowed re-reads, and a tradeoff between the two is proposed.

The aggressive scaling of the NAND flash technology has led to write noise becoming the dominant source of disturbance in the currently shipping sub-30 nm MLC NAND memories. Write noise can be mitigated by reducing the magnitude of the voltage levels programmed into the cells, which additionally translates to longer flash memory lifetime. However, if all the target levels are small and close together, the probability of error could become excessively high. It is therefore necessary to optimize the target level placement in order to achieve a trade-off between flash lifetime and error probability. This paper proposes a method to maximize flash lifetime subject to reliability constraints, and vice versa. Simulation results show that the proposed method doubles flash lifetime in comparison to a naive scheme, for a 2% reliability constraint. It also comes very close to the optimal solution obtained by brute force search, while maintaining negligible computational complexity in comparison.