Cooperative Interference Management With MISO Beamforming

Abstract

This correspondence studies the downlink transmission in a multi-cell system, where multiple base stations (BSs) each with multiple antennas cooperatively design their respective transmit beamforming vectors to optimize the overall system performance. For simplicity, it is assumed that all mobile stations (MSs) are equipped with a single antenna each, and there is one active MS in each cell at one time. Accordingly, the system of interests can be modeled by a multiple-input single-output (MISO) interference channel (IC), termed as MISO-IC, with interference treated as noise.We propose a new method to characterize different rate-tuples for active MSs on the Pareto boundary of the achievable rate region for the MISO-IC, by exploring the relationship between the MISO-IC and the cognitive radio (CR) MISO channel. We show that each Pareto-boundary rate-tuple of the MISO-IC can be achieved in a decentralized manner when each of the BSs attains its own channel capacity subject to a certain set of interference-power constraints (also known as interference-temperature constraints in the CR system) at the other MS receivers. Furthermore, we show that this result leads to a new decentralized algorithm for implementing the multi-cell cooperative downlink beamforming.

Interference MAC: Impact of improper Gaussian signaling on the rate region Pareto boundary

“…By scanning through feasible rate-profile vectors and maximizing R Σ (α), we acquire the complete Pareto boundary of the rate region [19].…”

“…The achievable rate tuples of the network are to be determined under this consideration. To this end, we utilize the so-called rate-profile method proposed in [19] to characterize the Pareto boundary of the achievable rate region. Herein, the Pareto boundary defines the frontier of the achievable rate region, where an increment in the rate of one user inevitably coincides with a decrement in the rate of at least one of the other users.…”

Optimal Resource Allocation in Coordinated Multi-Cell Systems

“…It is also possible to add interference constraints to the general problem (2.1) for the purpose of simplifying the problem, while striving for an optimal solution to the original non-interference-constrained problem. This heuristic approach is further discussed in Section 3.4 and makes sense from a theoretical standpoint, because interferenceconstrained beamforming provides the optimal solution to the general problem (2.1) if the interference constraints happen to equal the interference caused by the optimal solution to (2.1) [26,215,325]. This feature is utilized in [215] to solve general resource allocation problems.…”

“…This is a generalization of max-min optimization in Example 2.7 where two fairness constraints 12 have been added [17,26,126,144,185,193,325]: 12 The fairness constraints have important bargaining interpretations in cooperative game-theoretic setups where users compete for resources [193]: The so-called KalaiSmorodinsky objective function can be formulated as (2.41) using w = u − a as the weighing factors. The vector a is the disagreement point used if bargaining fails, while w is the direction from a toward the utopia point u.…”

Decentralized Weighted Sum Rate maximization in MIMO-OFDMA femtocell networks

“…S which can be obtained from the Lagrangian of   k P  in (9), along with the constraints shown in (4) and apply similar steps as in [13]. The second one is the power allocation …”