Index Modulation–Aided Mixed Massive Random Access

In this study, a mixed massive random access scheme is considered where part of users transmit both common information and user-specific information, while others transmit only common information. In this scheme, common information is transmitted by index modulation (IM)–aided unsourced random access (URA), while user-specific information is by IM-aided sourced random access (SRA). Practically, IM-aided URA partitions channel blocks of one transmission frame into multiple groups and then employs the IM principle to activate only part of the channel blocks in each group. IM-aided SRA allocates multiple pilot sequences to each user and activates only one pilot sequence whose index carries the data information. At the receiver, the covariance-based maximum likelihood detection (CB-MLD) is employed to recover the active compressed sensing (CS) code words of URA and information of SRA jointly. To stitch the common information at different blocks of URA, a modified tree decoder is proposed to take the IM constraint into account. Furthermore, to relax the strict threshold requirement and improve the performance, an iterative CS detector and tree decoder are employed to decode the common information, where successive signal reconstruction and interference cancellation are utilized. Finally, computer simulations are given to demonstrate the performance of the proposed scheme.

On the α-q-Mutual Information and the α-q-Capacities

The measures of information transfer which correspond to non-additive entropies have intensively been studied in previous decades. The majority of the work includes the ones belonging to the Sharma–Mittal entropy class, such as the Rényi, the Tsallis, the Landsberg–Vedral and the Gaussian entropies. All of the considerations follow the same approach, mimicking some of the various and mutually equivalent definitions of Shannon information measures, and the information transfer is quantified by an appropriately defined measure of mutual information, while the maximal information transfer is considered as a generalized channel capacity. However, all of the previous approaches fail to satisfy at least one of the ineluctable properties which a measure of (maximal) information transfer should satisfy, leading to counterintuitive conclusions and predicting nonphysical behavior even in the case of very simple communication channels. This paper fills the gap by proposing two parameter measures named the α-q-mutual information and the α-q-capacity. In addition to standard Shannon approaches, special cases of these measures include the α-mutual information and the α-capacity, which are well established in the information theory literature as measures of additive Rényi information transfer, while the cases of the Tsallis, the Landsberg–Vedral and the Gaussian entropies can also be accessed by special choices of the parameters α and q. It is shown that, unlike the previous definition, the α-q-mutual information and the α-q-capacity satisfy the set of properties, which are stated as axioms, by which they reduce to zero in the case of totally destructive channels and to the (maximal) input Sharma–Mittal entropy in the case of perfect transmission, which is consistent with the maximum likelihood detection error. In addition, they are non-negative and less than or equal to the input and the output Sharma–Mittal entropies, in general. Thus, unlike the previous approaches, the proposed (maximal) information transfer measures do not manifest nonphysical behaviors such as sub-capacitance or super-capacitance, which could qualify them as appropriate measures of the Sharma–Mittal information transfer.

Upgrading an Analog Recovery Loop for Optimized Decoding Jointly to an Increased data-Rate

The maximum likelihood detection theory improves the error-rate of a sub-optimal but cheaper, coded symbol recovery loop using oversampling proposed as an alternate solution for the decoding problem without the log-likelihood ratio computation. The former implementation delivers the output data in one-symbol delay, and the required transistor count makes this approach attractive for ultra-low-energy wireless applications. The proposed hardware upgrade includes an analog to digital converter and fixed-point accumulation logic to compute the soft values, replacing a trigger used as a hard detector. This work investigates the soft decoding in the presence of binary and non-binary source symbols. Simulation results show that the soft approach improves the signal-to-noise ratio by 3dB and 2.5 dB when the encoding rates are 1/3 and 2/3.