We consider the problem of transmission of a common message from a single source to two cooperating destination users, exhibiting different signal-to-noise ratios (SNR). The cooperation is performed over a noiseless link with a total capacity limitCcoop, which the users may utilize for conversation. In order to maximize the achievable throughput, it is suggested to employ a multilayer broadcast approach combined with multisession cooperation, such that every session another layer can be decoded. Since the cooperation link is limited, an efficient cooperation scheme is required. The relaying user performs compression which utilizes the side information of the decoder in the Wyner-Ziv (WZ) spirit. In multisession cooperation the compression is in the form of successive-refinement WZ coding. After the first user decodes a layer, the minimal required information is sent back to the second user to allow both of them to decode the message. The minimal required information is achievable with random coding using a binning strategy. While it is expected that multisession cooperation will be more efficient than a single-session cooperation we show that the maximal throughput is achieved with a single session cooperation. This is shown for the nonfading channel and for the ergodic fading case. Nonetheless, for the block fading channel it is already known that multilayering and multisession outperforms single session cooperation.

In this paper, we demonstrate the advantage of the inherent algebraic structure of lattice codes, for the uplink channel of a cellular deployment. The out-of-cell interference is assumed to be symmetric, as in Wyner's model. We employ a new relaying technique, compute-and-forward, which allows cell-sites to decode equations of the transmitted bits by exploiting the channel interference. However, the standard compute-and-forward technique is penalized whenever the channel coefficients are non-integer. We develop a superposition strategy to mitigate this penalty. By using part of the power towards a private message, we can effectively modify the channel seen by compute-and-forward. We demonstrate that, in certain regimes, this mixed strategy significantly outperforms decode-and-forward, compress-and-forward, and ordinary compute-and-forward.

In this work, new achievable rates are derived for the uplink channel of a cellular network with joint multicell processing (MCP), where unlike previous results, the ideal backhaul network has finite capacity per cell. Namely, the cell sites are linked to the central joint processor via lossless links with finite capacity. The new rates are based on compress-and-forward schemes combined with local decoding. Further, the cellular network is abstracted by symmetric models, which render analytical treatment plausible. For this family of idealistic models, achievable rates are presented for both Gaussian and fading channels. The rates are given in closed form for the classical Wyner model and the soft-handover model. These rates are then demonstrated to be rather close to the optimal unlimited backhaul joint processing rates, even for modest backhaul capacities, supporting the potential gain offered by the joint MCP approach. Particular attention is also given to the low-signal-to-noise ratio (SNR) characterization of these rates through which the effect of the limited backhaul network is explicitly revealed. In addition, the rate at which the backhaul capacity should scale in order to maintain the original high-SNR characterization of an unlimited backhaul capacity system is found.

A multiple-input multiple-output (MIMO) system with a distributed receiver is considered. The system consists of a nomadic transmitter with several antennas, whose signal is received by multiple agents, exhibiting independent channel gains and an additive circular-symmetric Gaussian noise. In the nomadic regime, we assume that the agents do not have any decoding ability. These agents process their channel observations and forward them to the final destination through unidirectional lossless links with a fixed capacity. We propose new achievable rates based on elementary compression and on Wyner–Ziv (WZ)or chief executive officer (CEO) processing, for both fast-fading and block-fading channels, as well as for general discrete channels. The simpler two agents scheme is solved, up to an implicit equation with a single variable. Limiting the nomadic transmitter to circular-symmetric Gaussian signaling, new upper bounds are derived, based on the vector version of the entropy power inequality. Several asymptotic settings are analyzed. In addition, the upper bounds are analytically shown to be tight for several examples, while numerical calculations reveal a rather small gap in a finite$2\,\times\,2$setting. The advantage of the WZ approach over elementary compression is shown, where only the former can achieve the optimal diversity–multiplexing tradeoff (DMT).

We study the problem of a single source transmitting, over a non-fading Gaussian channel, to two cooperating users exhibiting different signal-to-noise ratios (SNR). Both users are required to decode the source message. This model is similar to the broadcast channel with a common message, however the users cannot reliably decode the message independently, and require some form of conversation in order to decode. The cooperation is performed over a noiseless link with a total capacity limit Ccoop, which the users may utilize for conversation. In order to maximize the achievable throughput, it is suggested to employ the multi-layer broadcast approach combined with multi-session cooperation, such that every session another layer can be decoded. Since the cooperation link is limited, an efficient cooperation scheme is required. The relaying user performs compression accounting for the side information of the decoder in the Wyner-Ziv (WZ) spirit. In multi-session cooperation the compression is in the form of successive-refinement WZ coding. After the first user decodes a layer, the minimal required information is sent back to the second user to allow both of them to decode the message. The minimal required information is achievable with random coding using a binning strategy. While it is expected that multi-session cooperation will potentially be more efficient than a single-session cooperation we show that the maximal throughput is achieved in a single session.

Recent information theoretic results on cooperation in cellular systems are reviewed, addressing both multicell processing (cooperation among base stations) and relaying (cooperation at the user level). Two central issues are addressed, namely, first multicell processing is studied with either limited-capacity backhaul links to a central processor or only local (and finite-capacity) cooperation among neighboring cells. The role of codebook information, decoding delay and network planning (frequency reuse) are specifically highlighted along with the impact of different transmission/ reception strategies. Next, multicell processing is considered in the presence of cooperation at the user level, focusing on both out-of-band relaying via conferencing users and in-band relaying by means of dedicated relays. Non-fading and fading uplink and downlink channels adhering to simple Wyner-type, cellular system models are targeted.

The problem of a nomadic terminal sending information to a remote destination via agents with lossless connections to the destination is investigated. Such a setting suits, e.g., access points of a wireless network where each access point is connected by a wire to a wireline-based network. The Gaussian codebook capacity for the case where the agents do not have any decoding ability is characterized for the Gaussian channel. This restriction is demonstrated to be severe, and allowing the nomadic transmitter to use other signaling improves the rate. For both general and degraded discrete memoryless channels, lower and upper bounds on the capacity are derived. An achievable rate with unrestricted agents, which are capable of decoding, is also given and then used to characterize the capacity for the deterministic channel.

In this contribution we present new achievable rates, for the non-fading uplink channel of a cellular network, with joint cell-site processing, where unlike previous results, the error-free backhaul network has finite capacity per-cell. Namely, the cell-sites are linked to the central joint processor via lossless links with finite capacity. The cellular network is modeled by the circular Wyner model, which yields closed form expressions for the achievable rates. For this idealistic model, we present achievable rates for cell-sites that use compress-and forward scheme, combined with local decoding, and inter-cell time-sharing. These rates are then demonstrated to be rather close to the optimal unlimited backhaul joint processing rates, already for modest backhaul capacities, supporting the potential gain offered by the joint cell-site processing approach.

In this paper we investigate the achievable rate of a system that includes a nomadic transmitter with a Gaussian codebook and several antennas, which is received by multiple agents, each with a single antenna, suffering independent channel gains and additive Gaussian noise. In the nomadic regime, we assume that the agents do not have any decoding ability. These agents process their channel observations and forward it to the final destination through lossless links with fixed capacities. This paper extends a previous work [1] by providing new upper bounds on the achievable rates, and demonstrates optimality of the suggested coding scheme in several asymptotic situations. For a finite 2 × 2 setting and Rayleigh fading, the upper-bounds for both fast and block fading are demonstrated.

This work considers the problem of communication between a remote single transmitter and a destined user, with helping colocated users, over an independent block Rayleigh-fading channel. The colocation nature of the users allows cooperation, which increases the overall achievable rate, from transmitter to destination. The transmitter is ignorant of the fading coefficients, while receivers have access to perfect channel state information (CSI). We propose, for this setting, a multilayer broadcast transmission approach. The broadcast approach enables enhanced cooperation between the colocated users. That is due to the nature of broadcasting, where the better the channel quality, the more layers that can reliably be decoded. The cooperation between the users is performed over additive white Gaussian noise (AWGN) channels, with a relaying power constraint, and unlimited bandwidth. Three commonly used cooperation techniques are studied: amplify-and-forward (AF), compress–and-forward (CF), and decode–and-forward (DF). These techniques are extended by using the broadcast approach for the case of relaxed decoding delay constraint. For this case, a separate processing of the layers, which includes multisession cooperation is shown to be beneficial. Further, closed-form expressions for infinitely many AF sessions and recursive expressions for the more complex CF are given. Numerical results for the various cooperation strategies demonstrate how the multisession cooperation outperforms conventional relaying techniques.

In this paper we investigate the achievable rate of a system that includes a nomadic transmitter with several antennas, which is received by multiple agents, each with a single antenna, suffering independent channel coefficients and additive Gaussian noises. Since the transmitter is nomadic, the agents do not have any decoding ability. These agents process their channel observations and forward it to the final destination through lossless links with a fixed given capacity. Assuming Gaussian signalling, we get lower and upper bounds on the achievable rates, and demonstrate the achievability of the full multiplexing gain. We also extend the model to address multi-user systems. The asymptotic setting with numbers of agents and transmitter's antennas taken to infinity is examined, and the incompetence of the simple compression when compared to a Wyner-Ziv scheme is demonstrated. For finite setting, an upper-bound is derived, which turns out to be quite tight when compared to the Wyner-Ziv achievable rate, even for a rather small 4 × 4 system.

The common problem of a nomadic terminal sending information to a remote destination via agents with lossless connections is investigated. Such a setting suits, e.g. access points of a wireless network, where each access point is equipped with a different connection bandwidth. The case where these agents do not have any decoding ability is fully characterized for the Gaussian channel, when the transmitter uses "typical" codewords. For general discrete memoryless channels, lower and upper bounds are derived. An achievable rate with unrestricted agents, which are capable of decoding, is also given and then demonstrated by a numerical example for the Gaussian channel

An efficient scheme for the multiple-access multiple-input multiple-output (MIMO) channel is proposed, which operates well also in the single user regime, as well as in a direct-sequence spread-spectrum (DS-CDMA) setting. The design features scalability and is of limited complexity. The system employs optimized low-density parity-check (LDPC) codes and an efficient iterative (belief propagation-BP) detection which combines linear minimum mean-square error (LMMSE) detection and iterative interference cancellation (IC). This combination is found to be necessary for efficient operation in high system loads /spl alpha/>1. An asymptotic density evolution (DE) is used to optimize the degree polynomials of the underlining LDPC code, and thresholds as close as 0.77 dB to the channel capacity are evident for a system load of 2. Replacing the LMMSE with the complex individually optimal multiuser detector (IO-MUD) further improves the performance up to 0.14 dB from the capacity. Comparing the thresholds of a good single-user LDPC code to the multiuser optimized LDPC code, both over the above multiuser channel, reveals a surprising 8-dB difference, emphasizing thus the necessity of optimizing the code. The asymptotic analysis of the proposed scheme is verified by simulations of finite systems, which reveal meaningful differences between the performances of MIMO systems with single and multiple users and demonstrate performance similar to previously reported techniques, but with higher system loads, and significantly lower receiver complexity.