On Optimal Multi-user Beam Alignment in Millimeter Wave Wireless Systems

Directional transmission patterns (a.k.a. narrow beams) are the key to wireless communications in millimeter wave (mmWave) frequency bands which suffer from high path loss and severe shadowing. In addition, the propagation channel in mmWave frequencies incorporates only a few number of spatial clusters requiring a procedure to align the corresponding narrow beams with the angle of departure (AoD) of the channel clusters. The objective of this procedure, called beam alignment (BA) is to increase the beamforming gain for subsequent data communication. Several prior studies consider optimizing BA procedure to achieve various objectives such as reducing the BA overhead, increasing throughput, and reducing power consumption. While these studies mostly provide optimized BA schemes for scenarios with a single active user, there are often multiple active users in practical networks. Consequently, it is more efficient in terms of BA overhead and delay to design multi-user BA schemes which can perform beam management for multiple users collectively. This paper considers a class of multi-user BA schemes where the base station performs a one shot scan of the angular domain to simultaneously localize multiple users. The objective is to minimize the average of expected width of remaining uncertainty regions (UR) on the AoDs after receiving users' feedbacks. Fundamental bounds on the optimal performance are analyzed using information theoretic tools. Furthermore, a beam design optimization problem is formulated and a practical BA scheme, which provides significant gains compared to the beam sweeping used in 5G standard is proposed

Semantic Multi-Resolution Communications

Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This superiority arises from the suboptimality of SSCC when dealing with finite block-length data. Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data. To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL). This proposed framework excels at encoding data for different resolutions through hierarchical layers and effectively decodes it by leveraging both current and past layers of encoded data. Moreover, this framework holds great potential for semantic communication, where the objective extends beyond data reconstruction to preserving specific semantic attributes throughout the communication process. These semantic features could be crucial elements such as class labels, essential for classification tasks, or other key attributes that require preservation. Within this framework, each level of encoded data can be carefully designed to retain specific data semantics. As a result, the precision of a semantic classifier can be progressively enhanced across successive layers, emphasizing the preservation of targeted semantics throughout the encoding and decoding stages. We conduct experiments on MNIST and CIFAR10 dataset. The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers. This capability is particularly advantageous for prioritizing and preserving more crucial semantic features within the datasets

Distributed Cooperative Communications in Wireless Networks

PhD ThesisThe primary challenge in communication over wireless networks, unlike wireline networks, is the existence of interference and channel variations (fading). Having more users at higher data rates means that current point-to-point networks will not scale. To engineer a scalable network, we introduce a new paradigm that exploits different network characteristics. We show that cooperation between users in the net- work, network coding, not only reduces existing (destructive) interferences from other users but it can also generate constructive interference, transforming the destructive interference into useful information. In this thesis, we explore the problem of source and channel coding over wireless networks, ranging from information theoretical analysis to code design and practical implementation issues. We show that significant gains in throughput can be achieved through network coding. Despite the importance of the problem and the work done on wireless networks, little is known about network coding and the effective use of the relaying function and cooperative strategy at the intermediate nodes. A notable example is the lack of an optimal coding scheme over the relay channel, the simplest form of a network, which has remained an outstanding open question for the last three decades. We propose new approaches to network coding that improve upon the best known coding schemes by many decibels. Specifically, we develop two main coding tech- niques, one for the multi-state relay channel and the other for the multiple access with generalized feedback (MAC-GF). We show that by using the new coding tech- niques, higher transmission rates than those previously known are achievable. The first technique achieves the ultimate transmission rate (capacity) for both half-duplex and the original relay channel under certain conditions. These improved capacity re- sults for the relay channel are the only known results since Cover's in 1979 and El Gamal's in 1982. The second coding technique improves the best known achievable transmission rate for the MAC-GF by Willems in 1983. This latter result also im- proves the achievable transmission rate for the Gaussian relay channel over all other known schemes for some channel conditions. We also present a practical code design technique for the relay channel. The design gains more than 4dB over direct transmission and closes the gap to the relay channel Shannon limit to less than 1dB with a code length of only 2 x 10^4 bits. The new coding techniques and transmission strategies developed in this thesis provide important steps toward overcoming the challenges of wireless network coding

The Capacity of Average and Peak Power Constrained Fading Channels with Channel Side Information

Conference paperWe derive the ergodic capacity of discrete-time fading channel with additive Gaussian noise subject to both peak and average power constraint. The average power can be interpreted as the cost that we incur to achieve a certain rate. On the other hand, the motivation of this analysis comes from the fact that there is also a peak power limitation in practical communication system. It is been shown that the optimal power adaption is no longer water-filling or constant power adaption which is the case where there is no limitation on the peak power. The numerical results show that the importance of peak power constraint become negligible for relatively low available average power, while it is limiting the capacity to be finite even as available average power goes to infinity

Delay-constrained Scheduling: Power Efficiency, Filter Design, and Bounds

Conference PaperIn this paper, packet scheduling with maximum delay constraints is considered with the objective to minimize average transmit power over Gaussian channels. The main emphasis is on deriving robust schedulers which do not rely on the knowledge of the source arrival process. Towards that end, we first show that all schedulers (robust or otherwise) which guarantee a maximum queuing delay for each packet are equivalent to a time-varying linear filter. Using the connection between filtering and scheduling, we study the design of optimal power minimizing robust schedulers. Two cases, motivated by filtering connection, are studied in detail. First, a time-invariant robust scheduler is presented and its performance is completely characterized. Second, we present the optimal time-varying robust scheduler, and show that it has a very intuitive time water-filling structure. We also present upper and lower bounds on the performance of power-minimizing schedulers as a function of delay constraints. The new results form an important step towards understanding of the packet time-scale interactions between physical layer metric of power and network layer metric of delay

Power Optimal Scheduling with Maximum Delay Constraints

Conference PaperMost multimedia sources are bursty in nature, a property which can be used to trade queuing delay with the resulting average transmission power [2, 3, 4]. In this paper, we study the relation between average transmission power and strict delay constraints. Our main contributions are two-fold. First, we establish necessary and sufficient conditions on the service rates of the wireless transmitter, to meet the delay deadline of every packet in the queue. Second, the conditions are used to show that a scheduler which meets a delay guarantee Dmax for each of the packet over Gaussian channels is a time-varying low-pass filter of order no more than Dmax. This confirms the intuitive explanation for power reduction due to additional queuing delay provided in [3]. Using the relation between delay bounded scheduling and linear filtering, we construct schedulers without the knowledge of source statistics. This marks a significant departure from most information theoretic work on power efficient scheduling [2, 3]. We construct the optimal time-invariant scheduler, which does not require the knowledge of the source statistics

Distributed cooperative communications in wireless networks

The primary challenge in wireless networks, unlike wireline networks, is the existence of interference and channel variations (fading). Having more users at higher data rates means that current point-to-point networks will not scale. To engineer a scalable network, a new paradigm is needed to exploit different network characteristics. We show that cooperation between users in the network can effectively exchange possible interferences from other users in favor of useful information. In this thesis, we explore the problem of source and channel coding over wireless networks, ranging from information theoretical analysis to code design and practical implementation issues. We show that significant gains in throughput can be achieved through network coding. Despite the importance of the problem and the work done in this area, little is known about network coding and the optimal relaying function at the intermediate nodes. A notable example is the communication over the relay channel, the simplest form of a network, that has been an outstanding question in the last three decades. We propose new approaches to network coding that improve upon the best known coding schemes by many dB. Specifically, we develop two main coding techniques, one for the multi-state relay channel and the other for the multiple access channel with generalized feedback (MAC-GF). These coding techniques enable us to achieve higher rates than those previously known for the relay and MAC-GF channels. The first technique provides some capacity results for both half-duplex and the original relay channel. The new capacity results for the relay channel are the only known results besides Cover's in 1979 and El Gamal's in 1982. The second coding technique improves the best known achievable rate for the MAC-GF by Willems in 1983. This results also provides a new achievable rate for the Gaussian relay channel which improves over all other known schemes for some channel conditions. We also present a practical code design technique for the relay channel. The design gains more then 4dB over direct transmission and close the gap to the relay channel Shannon limit to less than 1dB with a code length of only 2 x 104 bits

On the Capacity of `Cheap' Relay Networks

Conference PaperWe consider the communication problem in a multi-hop relay network where the intermediate relay nodes cannot transmit and receive at the same time. The motivation for this assumption comes from the fact that current radios operate in TDD mode when the transmitting and receiving frequencies are the same. We label such a node radio as a 'cheap' radio and the corresponding node of the network as a 'cheap' node. In this paper we derive the capacities of the degraded cheap relay channel and the multi-hop network with cheap nodes. The proof of the achievability parts in coding theorems are presented based on the jointly typical sequences, while the proof of the converses are derived from the direct application of the upper bounds derived in [7].Noki