Fading Channels: Information-Theoretic and Communication Aspects

By Ezio Biglieri, John G. Proakis, Shlomo Shamai. 1998

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Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, background): “The long-term performance metrics are important in optimizing a wireless network under a fast fading environment where the quality of communication service is typically measured by the average performance, e.g., the ergodic capacity and the average power consumption [25].”
Wireless Information and Power Transfer: A Dynamic Power Splitting Approach (2013, background): “In this case, Tx sends information continuously with constant power P for all the fading states due to the lack of CSIT [11].”
Practical relay networks: a generalization of hybrid-ARQ (2005, background): “We assume that the fading coefficient is constant for the duration of a block and varies from block to block (cf. block fading [26]‐[ 28 ]).”
An overview of MIMO communications - a key to gigabit wireless (2004, background): “We define the outage capacity as the information rate that is guaranteed for of the channel realizations [22], [23], i.”
Optimized transmission for fading multiple-access and broadcast channels with multiple antennas (2006, background): “Given a fixed set of transmit covariance matrices for and , it was shown in [26] that all the rate-tuples in the set described below are achievable where the expectation is taken over the distribution of .”
Dynamic Resource Allocation in Cognitive Radio Networks (2010, background): “Note that DRA for traditional (non-CR) wireless networks under PTPC and/or ATPC has been thoroughly studied in the literature [12], while the study of DRA subject to PIPC and/o r AIPC as well as their various combinations with PTPC/ATPC is unique to CR networks and is relatively new.”
RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks (2019, background): “, the probability that the Shannon capacity falls below the transmission rate R [56].”
Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation (2019, methodology): “The CPU can select an arbitrary receive combining vector where The ergodic capacity is generally unknown for networks operating under imperfect CSI, but we can rigorously analyze the performance by using standard capacity lower bounds [3], [44], which we refer to as achievable SEs.”
Spectral efficiency in the wideband regime (2002, background): “Unfortunately, the capacity function and the inputs that attain it are unknown for many channels of interest, particularly in the presence of fading (cf. [11]).”
Quasi-Static Multiple-Antenna Fading Channels at Finite Blocklength (2013, background): “In such a scenario, it is plausible to assume that the duration of each of the transmitted codewords is smaller than the coherence time of the channel, so the random fading coefﬁcients stay constant over the duration of each codeword [1, p. 2631], [2, Sec.”
Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity (2008, background): “5]. Traditionally, the capacity of fading channels is studied under various transmit power constraints, and the corresponding optimal and suboptimal power allocation policies are given in, e.g., [6], [7], [8]. Recently, study on the channel capacity of SU link under spectrum sharing has attracted a ”