Mojtaba Vaezi

Coordinated Beamforming for Multi-Cell MIMO-NOMA

In this letter, two novel coordinated beamforming techniques are developed to enhance the performance of non-orthogonal multiple access combined with multiple-input multiple-output communication in the presence of inter-cell interference. The proposed schemes successfully deal with inter-cell interference, and increase the cell-edge users’ throughput, which in turn improves user fairness. In addition, they increase the number of served users, which makes them suitable for 5G networks where massive connectivity and higher spectral efficiency are required. Numerical results confirm the effectiveness of the proposed algorithms.

Non-Orthogonal Multiple Access in Multi-Cell Networks: Theory, Performance, and Practical Challenges

Non-orthogonal multiple access (NOMA) is a potential enabler for the development of 5G and beyond wireless networks. By allowing multiple users to share the same time and frequency, NOMA can scale up the number of served users, increase spectral efficiency, and improve user-fairness compared to existing orthogonal multiple access (OMA) techniques. While single-cell NOMA has drawn significant attention recently, much less attention has been given to multi-cell NOMA. This article discusses the opportunities and challenges of NOMA in a multi-cell environment. As the density of base stations and devices increases, inter-cell interference becomes a major obstacle in multi-cell networks. As such, identifying techniques that combine interference management approaches with NOMA is of great significance. After discussing the theory behind NOMA, this article provides an overview of the current literature and discusses key implementation and research challenges, with an emphasis on multi-cell NOMA.

On the capacity of the cognitive Z-interference channel

We study the cognitive interference channel (CIC) with two transmitters and two receivers, in which the cognitive transmitter non-causally knows the message and codeword of the primary transmitter. We first introduce a discrete memoryless more capable CIC, which is an extension to the more capable broadcast channel (BC). Using superposition coding, an inner bound and an outer bound on its capacity region are proposed. These bounds are then applied to the Gaussian cognitive Z-interference channel (GCZIC), in which only the primary receiver suffers interference. Upon showing that jointly Gaussian distribution maximizes these bounds for the GCZIC, we evaluate them for the GCZIC. The evaluated outer bound appears to be the best outer bound to date on the capacity of the GCZIC in strong interference. More importantly, this outer bound coincides with the inner bound for jaj equation. Thus, we establish the capacity of the GCZIC in this range and show that superposition encoding at the cognitive transmitter and successive decoding at the primary receiver are capacity-achieving.

Distributed Source-Channel Coding Based on Real-Field BCH Codes

We use real-number codes to compress statistically dependent sources and establish a new framework for distributed lossy source coding in which we compress sources before, rather than after, quantization. This change in the order of binning and quantization blocks makes it possible to model the correlation between continuous-valued sources more realistically and compensate for the quantization error partially. We then focus on the asymmetric case, i.e., lossy source coding with side information at the decoder. The encoding and decoding procedures are described in detail for a class of real-number codes called discrete Fourier transform (DFT) codes, both for the syndrome- and parity-based approaches. We leverage subspace-based decoding to improve the decoding and by extending it we are able to perform distributed source coding in a rate-adaptive fashion to further improve the decoding performance when the statistical dependency between sources is unknown. We also extend the parity-based approach to the case where the transmission channel is noisy and thus we perform distributed joint source-channel coding in this context. The proposed system is well suited for low-delay communications, as the mean-squared reconstruction error (MSE) is shown to be reasonably low for very short block length.

Distributed Lossy Source Coding Using Real-Number Codes

We show how real-number codes can be used to compress correlated sources, and establish a new framework for distributed lossy source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and correct quantization error when the sources are completely correlated. The encoding and decoding procedures are described in detail, for discrete Fourier transform (DFT) codes. Reconstructed signal, in the mean-squared error sense, is seen to be better than or close to quantization error level in the conventional approach.

Simplified Han-Kobayashi region for one-sided and mixed Gaussian interference channels

The Han-Kobayashi (HK) encoding scheme is simplified for the one-sided Gaussian interference channel and a class of mixed interference channels, with Gaussian codebooks. The simplified region significantly decreases the computational complexity of the HK region. It also provides better insight into how to use the HK scheme for these channels. It shows that time-sharing with power allocation over two dimensions is enough to achieve the border of the HK inner bound, for these channels. Moreover, a new representation of the HK region for these channels is introduced.

Systematic DFT Frames: Principle, Eigenvalues Structure, and Applications

Motivated by a host of recent applications requiring some amount of redundancy, frames are becoming a standard tool in the signal processing toolbox. In this paper, we study a specific class of frames, known as discrete Fourier transform (DFT) codes, and introduce the notion of systematic frames for this class. This is encouraged by a new application of frames, namely, distributed source coding that uses DFT codes for compression. Studying their extreme eigenvalues, we show that, unlike DFT frames, systematic DFT frames are not necessarily tight. Then, we come up with conditions for which these frames can be tight. In either case, the best and worst systematic frames are established in the minimum mean-squared reconstruction error sense. Eigenvalues of DFT frames and their subframes play a pivotal role in this work. Particularly, we derive some bounds on the extreme eigenvalues DFT subframes which are used to prove most of the results; these bounds are valuable independently.

Improved modeling of the correlation between continuous-valued sources in LDPC-based DSC

Accurate modeling of the correlation between the sources plays a crucial role in the efficiency of distributed source coding (DSC) systems. This correlation is commonly modeled in the binary domain by using a single binary symmetric channel (BSC), both for binary and continuous-valued sources. We show that “one” BSC cannot accurately capture the correlation between continuous-valued sources; a more accurate model requires “multiple” BSCs, as many as the number of bits used to represent each sample. We incorporate this new model into the DSC system that uses low-density parity-check (LDPC) codes for compression. The standard Slepian-Wolf LDPC decoder requires a slight modification so that the parameters of all BSCs are integrated in the log-likelihood ratios (LLRs). Further, using an interleaver the data belonging to different bit-planes are shuffled to introduce randomness in the binary domain. The new system has the same complexity and delay as the standard one. Simulation results prove the effectiveness of the proposed model and system.

The capacity of less noisy cognitive interference channels

Fundamental limits of the cognitive interference channel (CIC) with two pairs of transmitter-receiver have been under exploration for several years. In this paper, we study the discrete memoryless cognitive interference channel (DM-CIC) in which the cognitive transmitter non-causally knows the full message of the primary transmitter. The capacity of this channel is not known in general; it is only known in some special cases. Inspired by the concept of less noisy broadcast channel (BC), in this work we introduce the notion of less noisy cognitive interference channel. Unlike BC, due to the inherent asymmetry of the cognitive channel, two different less noisy channels are distinguishable; these are named the primary-less-noisy and cognitive-less-noisy channels. We derive capacity region for the latter case by introducing inner and outer bounds on the capacity of the DM-CIC and showing that these bounds coincide for the cognitive-less-noisy channel. Having established the capacity region, we prove that superposition coding is the optimal encoding technique.

Mobile Sensors Deployment Subject to Measurement Error

Single-cell-based coverage hole detection algorithms are developed for mobile sensors deployment under inaccurate location information and non-identical sensing ranges. Existing Voronoi-based diagrams require the exact location of sensors to guarantee a simple, single-cell-based coverage detection, and they miss the mark if the location information is inaccurate. The guaranteed Voronoi-based diagrams, proposed in this paper, extend the existing diagrams in a way to guarantee the single-cell based coverage hole detection when upper bounds on location errors are provided. Simulation results show that with inaccurate location information the proposed algorithms can largely increase the network coverage. Even if the location information is exactly known, we suggest assuming some error margins to improve the network coverage based on the proposed algorithms.