Multiaccess Channels with State Known to Some Encoders and Independent Messages

We consider a state-dependent multiaccess channel (MAC) with state non-causally known to some encoders. We derive an inner bound for the capacity region in the general discrete memoryless case and specialize to a binary noiseless case. In the case of maximum entropy channel state, we obtain the capacity region for binary noiseless MAC with one informed encoder by deriving a non-trivial outer bound for this case. For a Gaussian state-dependent MAC with one encoder being informed of the channel state, we present an inner bound by applying a slightly generalized dirty paper coding (GDPC) at the informed encoder that allows for partial state cancellation, and a trivial outer bound by providing channel state to the decoder also. The uninformed encoders benefit from the state cancellation in terms of achievable rates, however, appears that GDPC cannot completely eliminate the effect of the channel state on the achievable rate region, in contrast to the case of all encoders being informed. In the case of infinite state variance, we analyze how the uninformed encoder benefits from the informed encoder's actions using the inner bound and also provide a non-trivial outer bound for this case which is better than the trivial outer bound.Comment: Accepted to EURASIP Journal on Wireless Communication and Networking, Feb. 200

Rewriting Codes for Joint Information Storage in Flash Memories

Memories whose storage cells transit irreversibly between states have been common since the start of the data storage technology. In recent years, flash memories have become a very important family of such memories. A flash memory cell has q states—state 0.1.....q-1 - and can only transit from a lower state to a higher state before the expensive erasure operation takes place. We study rewriting codes that enable the data stored in a group of cells to be rewritten by only shifting the cells to higher states. Since the considered state transitions are irreversible, the number of rewrites is bounded. Our objective is to maximize the number of times the data can be rewritten. We focus on the joint storage of data in flash memories, and study two rewriting codes for two different scenarios. The first code, called floating code, is for the joint storage of multiple variables, where every rewrite changes one variable. The second code, called buffer code, is for remembering the most recent data in a data stream. Many of the codes presented here are either optimal or asymptotically optimal. We also present bounds to the performance of general codes. The results show that rewriting codes can integrate a flash memory’s rewriting capabilities for different variables to a high degree

Trajectory Codes for Flash Memory

Flash memory is well-known for its inherent asymmetry: the flash-cell charge levels are easy to increase but are hard to decrease. In a general rewriting model, the stored data changes its value with certain patterns. The patterns of data updates are determined by the data structure and the application, and are independent of the constraints imposed by the storage medium. Thus, an appropriate coding scheme is needed so that the data changes can be updated and stored efficiently under the storage-medium's constraints. In this paper, we define the general rewriting problem using a graph model. It extends many known rewriting models such as floating codes, WOM codes, buffer codes, etc. We present a new rewriting scheme for flash memories, called the trajectory code, for rewriting the stored data as many times as possible without block erasures. We prove that the trajectory code is asymptotically optimal in a wide range of scenarios. We also present randomized rewriting codes optimized for expected performance (given arbitrary rewriting sequences). Our rewriting codes are shown to be asymptotically optimal.Comment: Submitted to IEEE Trans. on Inform. Theor

Degraded Broadcast Channel with Side Information, Confidential Messages and Noiseless Feedback

In this paper, first, we investigate the model of degraded broadcast channel with side information and confidential messages. This work is from Steinberg's work on the degraded broadcast channel with causal and noncausal side information, and Csisz$\acute{a}$r-K\"{o}rner's work on broadcast channel with confidential messages. Inner and outer bounds on the capacity-equivocation regions are provided for the noncausal and causal cases. Superposition coding and double-binning technique are used in the corresponding achievability proofs. Then, we investigate the degraded broadcast channel with side information, confidential messages and noiseless feedback. The noiseless feedback is from the non-degraded receiver to the channel encoder. Inner and outer bounds on the capacity-equivocation region are provided for the noncausal case, and the capacity-equivocation region is determined for the causal case. Compared with the model without feedback, we find that the noiseless feedback helps to enlarge the inner bounds for both causal and noncausal cases. In the achievability proof of the feedback model, the noiseless feedback is used as a secret key shared by the non-degraded receiver and the transmitter, and therefore, the code construction for the feedback model is a combination of superposition coding, Gel'fand-Pinsker's binning, block Markov coding and Ahlswede-Cai's secret key on the feedback system.Comment: Part of this paper has been accepted by ISIT2012, and this paper is submitted to IEEE Transactions on Information Theor

Coding Techniques for Error Correction and Rewriting in Flash Memories

Flash memories have become the main type of non-volatile memories. They are widely used in mobile, embedded and mass-storage devices. Flash memories store data in floating-gate cells, where the amount of charge stored in cells – called cell levels – is used to represent data. To reduce the level of any cell, a whole cell block (about 106 cells) must be erased together and then reprogrammed. This operation, called block erasure, is very costly and brings significant challenges to cell programming and rewriting of data. To address these challenges, rank modulation and rewriting codes have been proposed for reliably storing and modifying data. However, for these new schemes, many problems still remain open. In this work, we study error-correcting rank-modulation codes and rewriting codes for flash memories. For the rank modulation scheme, we study a family of one- error-correcting codes, and present efficient encoding and decoding algorithms. For rewriting, we study a family of linear write-once memory (WOM) codes, and present an effective algorithm for rewriting using the codes. We analyze the performance of our solutions for both schemes

Towards Endurable, Reliable and Secure Flash Memories-a Coding Theory Application

Storage systems are experiencing a historical paradigm shift from hard disk to nonvolatile memories due to its advantages such as higher density, smaller size and non-volatility. On the other hand, Solid Storage Disk (SSD) also poses critical challenges to application and system designers. The first challenge is called endurance. Endurance means flash memory can only experience a limited number of program/erase cycles, and after that the cell quality degradation can no longer be accommodated by the memory system fault tolerance capacity. The second challenge is called reliability, which means flash cells are sensitive to various noise and disturbs, i.e., data may change unintentionally after experiencing noise/disturbs. The third challenge is called security, which means it is impossible or costly to delete files from flash memory securely without leaking information to possible eavesdroppers. In this dissertation, we first study noise modeling and capacity analysis for NAND flash memories (which is the most popular flash memory in market), which gains us some insight on how flash memories are working and their unique noise. Second, based on the characteristics of content-replication codewords in flash memories, we propose a joint decoder to enhance the flash memory reliability. Third, we explore data representation schemes in flash memories and optimal rewriting code constructions in order to solve the endurance problem. Fourth, in order to make our rewriting code more practical, we study noisy write-efficient memories and Write-Once Memory (WOM) codes against inter-cell interference in NAND memories. Finally, motivated by the secure deletion problem in flash memories, we study coding schemes to solve both the endurance and the security issues in flash memories. This work presents a series of information theory and coding theory research studies on the aforesaid three critical issues, and shows that how coding theory can be utilized to address these challenges