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154
Multi-Cell MIMO Cooperative Networks: A New Look at Interference
- J. Selec. Areas in Commun. (JSAC
, 2010
"... Abstract—This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multi-cell cooperation can dramatically improv ..."
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Cited by 257 (40 self)
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Abstract—This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multicell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested. Index Terms—Cooperation, MIMO, cellular networks, relays, interference, beamforming, coordination, multi-cell, distributed.
Transmitter Optimization for the Multi-Antenna Downlink with Per-Antenna Power Constraints
- IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2007
"... This paper considers the transmitter optimization problem for a multiuser downlink channel with multiple transmit antennas at the base-station. In contrast to the conventional sum-power constraint on the transmit antennas, this paper adopts a more realistic per-antenna power constraint, because in ..."
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Cited by 135 (7 self)
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This paper considers the transmitter optimization problem for a multiuser downlink channel with multiple transmit antennas at the base-station. In contrast to the conventional sum-power constraint on the transmit antennas, this paper adopts a more realistic per-antenna power constraint, because in practical implementations each antenna is equipped with its own power amplifier and is limited individually by the linearity of the amplifier. Assuming perfect channel knowledge at the transmitter, this paper investigates two different transmission schemes under the per-antenna power constraint: a minimum-power beamforming design for downlink channels with a single antenna at each remote user and a capacity-achieving transmitter design for downlink channels with multiple antennas at each remote user. It is shown that in both cases, the per-antenna downlink transmitter optimization problem may be transformed into a dual uplink problem with an uncertain noise. This generalizes previous uplink–downlink duality results and transforms the per-antenna transmitter optimization problem into an equivalent minimax optimization problem. Further, it is shown that various notions of uplink–downlink duality may be unified under a Lagrangian duality framework. This new interpretation of duality gives rise to efficient numerical optimization techniques for solving the downlink per-antenna transmitter optimization problem.
Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks
- IEEE J. Select. Topics in Signal Processing
, 2008
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Coordinated beamforming for the multi-cell multi-antenna wireless system
- IEEE Trans. Wireless Commun
"... Abstract—In a conventional wireless cellular system, signal processing is performed on a per-cell basis; out-of-cell interference is treated as background noise. This paper considers the benefit of coordinating base-stations across multiple cells in a multi-antenna beamforming system, where multiple ..."
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Cited by 120 (6 self)
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Abstract—In a conventional wireless cellular system, signal processing is performed on a per-cell basis; out-of-cell interference is treated as background noise. This paper considers the benefit of coordinating base-stations across multiple cells in a multi-antenna beamforming system, where multiple basestations may jointly optimize their respective beamformers to improve the overall system performance. This paper focuses on a downlink scenario where each remote user is equipped with a single antenna, but where multiple remote users may be active simultaneously in each cell. The design criterion is the minimization of the total weighted transmitted power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution is a practical algorithm that is capable of finding the joint optimal beamformers for all base-stations globally and efficiently. The proposed algorithm is based on a generalization of uplinkdownlink duality to the multi-cell setting using the Lagrangian duality theory. The algorithm also naturally leads to a distributed implementation. Simulation results show that a coordinated beamforming system can significantly outperform a conventional system with per-cell signal processing. I.
Sum Rate Characterization of Joint Multiple Cell-Site Processing
, 2005
"... The sum-rate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both non-fading and flat-fading channels. The focus is on a simple Wyner-like multi-cell model, where the system cells are arranged on a circle, assuming the cell-sites are lo ..."
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Cited by 68 (11 self)
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The sum-rate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both non-fading and flat-fading channels. The focus is on a simple Wyner-like multi-cell model, where the system cells are arranged on a circle, assuming the cell-sites are located at the boundaries of the cells. For the uplink channel, analytical expressions of the sum-rate capacities are derived for intra-cell TDMA scheduling, and a “Wide-Band ” (WB) scheme (where all users are active simultaneously utilizing all bandwidth for coding). Assuming individual per-cell power constraints, and using the Lagrangian uplink-downlink duality principle, an analytical expression for the sum-rate capacity of the downlink channel is derived for non-fading channels, and shown to coincide with the corresponding uplink result. Introducing flat-fading, lower and upper bounds on the average per-cell sum-rate capacity are derived. The bounds exhibit an O(loge K) multi-user diversity factor for a number of users per-cell K ≫ 1, in addition to the array diversity gain. Joint multi-cell processing is shown to eliminate out-of-cell interference, which is traditionally considered to be a limiting factor in high-rate reliable communications. This paper was presented in part at the 9
Weighted Sum-Rate Maximization using Weighted MMSE for MIMO-BC Beamforming Design
- IEEE Trans. on Wireless Comm
, 2008
"... Abstract—This paper studies linear transmit filter design for Weighted Sum-Rate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by r ..."
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Cited by 59 (2 self)
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Abstract—This paper studies linear transmit filter design for Weighted Sum-Rate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and Minimum Mean Square Error (MMSE), this paper establishes a relationship between weighted sum-rate and weighted MMSE in the MIMO-BC. The relationship is used to propose two low complexity algorithms for finding a local weighted sum-rate optimum based on alternating optimization. Numerical results studying sum-rate show that the proposed algorithms achieve high performance with few iterations. Index Terms—MIMO systems, transceiver design, smart antennas, antennas and propagation. I.
An introduction to convex optimization for communications and signal processing
- IEEE J. SEL. AREAS COMMUN
, 2006
"... Convex optimization methods are widely used in the ..."
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Cited by 56 (2 self)
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Convex optimization methods are widely used in the
Large system analysis of linear precoding in correlated MISO broadcast channels under limited feedback
- IEEE TRANS. INF. THEORY
, 2012
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Zero Forcing Precoding and Generalized Inverses
"... We consider the problem of linear zero forcing precoding design, and discuss its relation to the theory of generalized inverses in linear algebra. Special attention is given to a specific generalized inverse known as the pseudo-inverse. We begin with the standard design under the assumption of a tot ..."
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Cited by 52 (0 self)
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We consider the problem of linear zero forcing precoding design, and discuss its relation to the theory of generalized inverses in linear algebra. Special attention is given to a specific generalized inverse known as the pseudo-inverse. We begin with the standard design under the assumption of a total power constraint and prove that precoders based on the pseudo-inverse are optimal in this setting. Then, we proceed to examine individual per-antenna power constraints. In this case, the pseudo-inverse is not necessarily the optimal generalized inverse. In fact, finding the optimal inverse is non-trivial and depends on the specific performance measure. We address two common criteria, fairness and throughput, and show that the optimal matrices may be found using standard convex optimization methods. We demonstrate the improved performance offered by our approach using computer simulations.
Dynamic resource allocation in cognitive radio networks
- IEEE Signal Process. Mag
, 2010
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