Effective interference management in the multiuser interference channel strongly hinges on the channel state information's availability at the transmitters (CSIT). In a broad range of emerging large-scale and distributed networks (e.g., the Internet of Things), acquiring the CSIT is prohibitive due to the extensive information exchange that it imposes. As a result, the interference management approaches that rely on the CSIT lose their effectiveness in such circumstances. This article focuses on the two-user interference channel and proposes a broadcast approach to interference management. Its hallmark is that the transmitters, unlike the receivers, are entirely oblivious to instantaneous channel states. Each transmitter splits its message into multiple superimposed encoded information layers, where each layer is adapted to a given possible state for the combined states of all channels. Depending on the relative gain between the direct and interfering channels, each receiver opportunistically decodes a subset of both transmitters' received layers. An average achievable rate region is delineated, serving as an inner bound on the Gaussian interference channel's average capacity region in the absence of CSIT. Finally, an upper bound on the gap between the achievable sum-rate and the sum-rate capacity is established.

Effective interference management in the multiuser interference channel strongly hinges on the availability of the channel state information at the transmitters (CSIT). In a broad range of emerging large-scale and distributed networks (e.g., the Internet of Things), however, acquiring the CSIT is prohibitive, due to the extensive information exchange that it imposes. In such circumstances, as a result, the interference management approaches that rely on the CSIT lose their effectiveness. This paper focuses on the two-user interference channel, and proposes a broadcast approach to interference management. Its hallmark is that the transmitters, unlike the receivers, are completely oblivious to instantaneous channel states. Each transmitter splits its message into multiple superimposed encoded information layers, where each layer is adapted to a given possible state for the combined states of all channels. Depending on the relative strengths of the direct and interfering channels, each receiver opportunistically decodes a subset of the received layers from both transmitters. An average achievable rate region is delineated serving as an inner bound on the average capacity region of the Gaussian interference channel in the absence of CSIT. Finally, it characterizes the gap between the achievable average sum-rate and the sum-rate capacity with the full CSIT in the asymptote of high signal-to-noise ratio. Numerical evaluations show that the cost of lacking CSIT is often insignificant.

This paper proposes a broadcast strategy for the single-user slowly fading channel in which the transmission power is supplied by an energy harvesting unit. In this strategy, the channel state information (CSI) is known only to the receiver, and the transmitter is assumed to be oblivious to the CSI. The broadcast approach enables preventing outage events in the face of the lack of CSI at the transmitter. In the proposed broadcast approach, the transmitter splits its message into multiple superimposed information layers. This facilitates sustaining reliably decodable transmission rates adapted to the unknown state of the randomly-varying fading state of the channel. The objective is to characterize the optimal allocation of the randomly varying harvested power over time and across information layers in the contexts of maximizing the average communication rate and minimizing the likelihood of outage. First, the setting in which the transmitter has non-causal and complete information about the state of the energy harvesting process is considered. A closed-form characterization of the optimal allocation of power across layers is established, and an algorithm that analytically determines the exact optimal allocation of power over time is provided. Furthermore, an online algorithm is proposed for a setting in which the energy harvesting process is known only causally to the transmitter.

A two-user multiple access channel is considered, in which the channels undergo slow block fading and the state of each channel is known only to its corresponding transmitter. This paper proposes a novel broadcast strategy for multiple access communication in this channel. In the broadcast approach, in principle, a transmitter with CSI uncertainty sends multiple independent superimposed information layers where the rate of each layer is adapted to a specific channel realization. In the existing broadcast approaches to multiuser communication, the transmitters often directly adopt a single-user strategy and each transmitter adapts its transmission to one unknown channel. The novel aspect of the proposed strategy is that it adapts the designed codebooks to the state of the entire network. This is motivated by the fact that the contribution of each user to the network-wide measures (e.g., capacity region) depends not only on the user’s direct channel to the receiver, but also on the qualities of other channels. Average achievable rate region and outer bounds on the capacity region are characterized. Furthermore, the expected capacity region is investigated, where most part of the capacity region boundary is characterized. Finally, an asymptotic capacity region is also characterized.

A new broadcast strategy is designed for multiple access communication with partial channel state information at the transmitters. Specifically, a two-user multiple access channel is considered, in which the state of each channel is known only to its corresponding transmitter. In broadcast approaches, in principle, the transmitter sends multiple independent superimposed information layers, where the rate of each layer is adapted to a specific channel realization. The novel aspect of the proposed strategy is that it adapts the designed codebooks to the state of the whole network, which in contrast to the existing ones in which each transmitter adapts its transmission strategy only to the state of its direct channel to the receiver. Noting that the contribution of each user to a network-wide measure (e.g., capacity region) depends not only on the user's direct channel to the receiver, but also on the qualities of other channels, in the proposed strategy the transmitters adapt their transmissions to the combined states resulting from all users' channels. This leads to a larger achievable rate region, which is characterized and compared to two outer bounds. Furthermore, the proposed strategy is proved to achieve the sum-rate capacity asymptotically.

This paper treats the problem of optimal resource allocation over time in a finite-horizon setting, in which the resource become available only sequentially and in incremental values, and the utility function is concave and can freely vary over time. Such resource allocation problems have direct applications in data communication networks (e.g., energy harvesting systems). This problem is studied extensively for special choices of the concave utility function (time invariant and logarithmic) in which case the optimal resource allocation policies are well-understood. This paper treats this problem in its general form and analytically characterizes the structure of the optimal resource allocation policy and devises an algorithm for computing the exact solutions analytically. An observation instrumental to devising the provided algorithm is that there exist time instances at which the available resources are exhausted, with no carryover to future. This algorithm identifies all such instances, which in turn, facilitates breaking the original problem into multiple problems with significantly reduced dimensions. Furthermore, some widely used special cases in which the algorithm takes simpler structures are characterized, and the application to the energy harvesting systems is discussed. Numerical evaluations are provided to assess the key properties of the optimal resource allocation structure and to compare the performance with the generic convex optimization algorithms.