In this work we address the problem of optimal resource allocation in the uplink of a wireless cellular network with Rayleigh fading channels, where the aim is to minimize the average total energy spent for packet delivery. The devices are assumed to transmit over orthogonal resources where the total energy used for each resource is modeled as the sum of the transmit energy and an overhead circuit energy. We first derive the optimal allocation for the single-user case when varying our assumptions on both the Channel State Information available at the transmitter and the Automatic Repeat reQuest capabilities. Then, we generalize the analysis to the multi-user case and compare the results obtained for the different scenarios.

For wireless systems in which randomly arriving devices attempt to transmit a fixed payload to a central receiver, we develop a framework to characterize the system throughput as a function of arrival rate and per-device data rate. The framework considers both coordinated transmission (where devices are scheduled) and uncoordinated transmission (where devices communicate on a random access channel and a provision is made for retransmissions). Our main contribution is a novel characterization of the optimal throughput for the case of uncoordinated transmission and a strategy for achieving this throughput that relies on overlapping transmissions and joint decoding. Simulations for a noise-limited cellular network show that the optimal strategy provides a factor of four improvement in throughput compared with slotted ALOHA. We apply our framework to evaluate more general system-level designs that account for overhead signaling. We demonstrate that, for small payload sizes relevant for machine-to-machine (M2M) communications (200 bits or less), a one-stage strategy, where identity and data are transmitted optimally over the random access channel, can support at least twice the number of devices compared with a conventional strategy, where identity is established over an initial random-access stage and data transmission is scheduled.

We consider a wireless time-slotted random access channel where user arrivals are characterized by a Poisson process. Each user comes with a fixed payload, which has to be transmitted in the slot in which it arrives. If the transmission is successful, the user leaves the system, else it is dropped. The receiver and users are assumed to have knowledge of the arrival rate λ, but they are not aware of the actual number of users simultaneously attempting to communicate during a given time slot. In contrast to a conventional slotted ALOHA-based strategy where the channel is partitioned into orthogonal subchannels and each user communicates on a randomly chosen subchannel, we propose a novel strategy whereby users transmit simultaneously over the entire channel resource and the receiver jointly decodes the transmissions. Under the proposed strategy, neither users nor the receiver have prior knowledge of the active user set. Our analysis concretely demonstrates that the proposed strategy is optimal in terms of maximizing the average throughput among all uncoordinated strategies. Numerical results show that the proposal provides an order of magnitude throughput improvement compared to slotted ALOHA in a single-cell environment under a 10% maximum outage constraint.

The growing popularity of Machine-to-Machine (M2M) communications in cellular networks is driving the need to optimize networks based on the characteristics of M2M, which are significantly different from the requirements that current networks are designed to meet. First, M2M requires large number of short sessions as opposed to small number of long lived sessions required by the human generated traffic. Second, M2M constitutes a number of battery operated devices that are static in locations such as basements and tunnels, and need to transmit at elevated powers compared to the traditional devices. Third, replacing or recharging batteries of such devices may not be feasible. All these differences highlight the importance of a systematic framework to study the power and energy optimal system design in the regime of interest for M2M, which is the main focus of this paper. For a variety of coordinated and uncoordinated transmission strategies, we derive results for the optimal transmit power, energy per bit, and the maximum load supported by the base station, leading to the following design guidelines: (i) frequency division multiple access (FDMA), including equal bandwidth allocation, is sum-power optimal in the asymptotically low spectral efficiency regime, (ii) while FDMA is the best practical strategy overall, uncoordinated code division multiple access (CDMA) is almost as good when the base station is lightly loaded, (iii) the value of optimization within FDMA is not significant in the regime of interest for M2M.

This paper presents an empirical study of the achievable data rates of network multiple-input multiple-output (MIMO) techniques including zero-forcing (ZF), zero-forcing dirty paper coding (ZF-DPC) and dirty paper coding (DPC) using actual 4-by-4 indoor wireless channel measurements at 3.5 GHz. Their performances are contrasted with those of conventional techniques, in which either the base stations are not coordinated (NC), or their interference is avoided using frequency division (FD) multiplexing. The measurements were taken in aisle-to-office and large unobstructed hall scenarios. The study of these results reveals that, at high signal-to-noise ratios (SNRs), DPC and ZF-DPC can yield more than a three-fold increase in attainable data rates when compared to NC and FD. The gains obtained using ZF are smaller, but still significant. At low SNRs the system is noise-(rather than interference-) limited, and only DPC exhibits gains. The evaluations in this paper also show that collaborative systems such as DPC can benefit from interference-prone environments to yield increased transmission capacity. With regard to the propagation channel, the classical log-normal plus Rayleigh/Ricean fading model, with parameters fitted to the scenario type, was found to be good at predicting the statistics of the achievable data rates of all the strategies considered.

In this paper we present a new paradigm for multiuser MIMO downlink communications called Rate Approximation with significant impact on the upcoming LTE standard. Rate Approximation combines flexibility and robustness needed for reliable communications with the terminal in the downlink and taking advantage of the channel state information. The scheme responds to two major developments in the recent literature: One observation is that it is not so important to approximate the channel but rather the rate itself. The second observation is that a fixed codebook at the transmitter is often better when simultaneously the channel state information is more accurate. Both observations are incorporated in the new scheme where the transmit and feedback codebook are strictly separated and user rates are brought to the base station subject to a controlled uniform error. The new metric is amenable to further numerical optimization. Analysis and simulations show the superior performance of the scheme and, furthermore, a strong impact on upcoming cooperative schemes is expected.

We present a new robust feedback and transmit strategy for multiuser MIMO downlink communication systems, termed Rate Approximation (RA), and analyze its performance. The new scheme combines flexibility and robustness needed for reliable communications with the user terminal, under a limited feedback constraint. The scheme responds to two major developments in the recent literature: One observation is that it is not so important to approximate the channel but rather the rate itself. The second observation is that a fixed codebook at the transmitter is often better when simultaneously the channel state information is more accurate. Both observations are incorporated in the new scheme where the transmit and feedback codebook are strictly separated and user rates are brought to the base station subject to a controlled uniform error. The analysis provides two astonishing results. First, under perfect channel state information at the transmitter RA outperforms zeroforcing beamforming (ZFBF) for a large fraction of the practically relevant signal-to-noise ratio range for the considered operating point. Second, under a limited feedback constraint the quantization error scaling of RA is shown to be doubly exponentially in the number of feedback bits, whereas the quantization error of ZFBF with random vector quantization was shown by Jindal to scale only exponentially. Simulations sustain our analytic results showing the superior performance of the new scheme.

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 capacity-limiting 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. Multi-cell 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.

The one tutorial paper and eight contributed papers in this special issue focus on cooperative communications in MIMO cellular networks.

In a cooperative multiple-antenna downlink cellular network, maximization of a concave function of user rates is considered. A new linear precoding technique called soft interference nulling (SIN) is proposed, which performs at least as well as zero-forcing (ZF) beamforming. All base stations share channel state information, but each user's message is only routed to those that participate in the user's coordination cluster. SIN precoding is particularly useful when clusters of limited sizes overlap in the network, in which case traditional techniques such as dirty paper coding or ZF do not directly apply. The SIN precoder is computed by solving a sequence of convex optimization problems. SIN under partial network coordination can outperform ZF under full network coordination at moderate SNRs. Under overlapping coordination clusters, SIN precoding achieves considerably higher throughput compared to myopic ZF, especially when the clusters are large.

Single-user, multiuser, and network MIMO performance is evaluated for downlink cellular networks with 12 antennas per site, sectorization, universal frequency reuse, scheduled packet-data, and a dense population of stationary users. Compared to a single-user MIMO baseline system with 3 sectors per site, network MIMO coordination is found to increase throughput by a factor of 1.8 with intra-site coordination among antennas belonging to the same cell site. Intra-site coordination performs almost as well as a highly sectorized system with 12 sectors per site. Increasing the coordination cluster size from 1 to 7 sites increases the throughput gain factor to 2.5.

Coordinating the reception and transmission of signals across spatially distributed base stations has been shown to improve sum-rate performance by mitigating the effects of intercell interference in Multiple-Input-Multiple-Output (MIMO) cellular networks. Relying on recent results on the freeness of certain non-Gaussian random matrices, we provide an information theoretic analysis of cooperative MIMO networks. This analysis applies to the case where full channel state information is known at a subset of the bases and where statistical information is known at all others. Tools for evaluating random matrix transforms traditionally exploited in Mean Square Error (MSE) and mutual information analysis are provided, and the general model formulation paves the way for future work, where specific scheduling and/or power assignment schemes could be embodied in the newly presented framework.

We consider a multiuser MIMO downlink system with multiple antenna mobile terminals where channel state information at the base station is provided through limited uplink feedback. In a multi-cell environment we compare two transceiver architectures based on zero forcing and unitary beamforming respectively. For both schemes we show how the additional degrees of freedom provided by multiple receive antennas can be exploited to improve system performance, by a proper design of the receive combiner. We propose channel quantization solutions that adapt to the transceiver architectures and exploit MIMO channel statistic. Numerical simulations validate the effectiveness of the proposals in a multi-cell environment where long-term proportional fairness is guaranteed among the users in the network.

Telemonitoring and remote healthcare are two ICT techniques promise to deliver cost effective long-term healthcare services to the aging population worldwide. Anticipating an emerging market opportunity, NCTU Kannon project team is creating Custos, an open composeable platform for developing remote on-demand healthcare applications using smart phones and wireless sensor networks. Custos is integrated readily with Internet web services and UPnP digital-home platform so that it can be used to detect abnormal health conditions through multi-sensor data fusion, adapt environment conditions to user needs, dispatch medical alerts and instigate remedial actions via SMS/RSS communication. Moreover, Custos is easy to program because it uses Sun SPOT and Squawk Java J2ME as its sensor/actuator hub. In this position paper, we present the design principles, system architecture and operation concepts of Custos as well as a pilot plan for clinical trials in the Geriatric Psychiatry Ward of Wei-Gong Memorial Hospital in Miaoli, Taiwan.

The 17 papers in this special issue focus on MIMO systems and applications: field experience, practical aspects, limitations and challenges. The papers are summarized here.

We consider a MIMO broadcast channel where both the transmitter and receivers are equipped with multiple antennas. Channel state information at the transmitter (CSIT) is obtained through limited (i.e., finite-bandwidth) feedback from the receivers that index a set of precoding vectors contained in a predefined codebook. We propose a novel transceiver architecture based on zero-forcing beamforming and linear receiver combining. The receiver combining and quantization for CSIT feedback are jointly designed in order to maximize the expected SINR for each user. We provide an analytic characterization of the achievable throughput in the case of many users and show how additional receive antennas or higher multiuser diversity can reduce the required feedback rate to achieve a target throughput.We also propose a design methodology for generating codebooks tailored for arbitrary spatial correlation statistics. The resulting codebooks have a tree structure that can be utilized in time-correlated MIMO channels to significantly reduce feedback overhead. Simulation results show the effectiveness of the overall transceiver design strategy and codebook design methodology compared to prior techniques in a variety of correlation environments.

We consider a MIMO line of sight (LOS) broadcast channel where each user has perfect knowledge of its own channel and feeds back 'quantized' channel information to the transmitter with finite number of bits. The rate loss incurred due to imperfect channel information at the transmitter is quantified in terms of the number of feedback bits, assuming that low-complexity zero-forcing beamforming is used by the transmitter. Moreover, in order to maintain a constant rate loss relative to a perfect CSIT system, it is shown that the number of feedback bits per user should grow linearly with the system SNR in dB but only logarithmitcally with the number of transmit antennas. Simulation results comparing zero-forcing, MMSE and conventional beamforming with greedy user selection and finite rate feedback are also presented.

We consider the MIMO broadcast channel (MIMO-BC) where an array equipped with M antennas transmits distinct information to K users, each equipped with N antennas. We propose a linear precoding technique, called multiuser eigenmode transmission (MET), based on the block diagonalization precoding technique. MET addresses the shortcomings of previous ZF-based beamformers by transmitting to each user on one or more eigenmodes chosen using a greedy algorithm. We consider both the typical sum-power constraint (SPC), and a per-antenna power constraint (PAPC) motivated by array architectures where antennas are powered by separate amplifiers and are either co-located or spatially separated. Numerical results show that the proposed MET technique outperforms previous linear techniques with both SPC and PAPC. Asymptotically as the number of user K increases without bound, we show that block diagonalization with receive antenna selection under PAPC and SPC are asymptotically optimal.

In this paper we present a multiuser MIMO experimental system and the corresponding indoor measurement results that demonstrate power of distributed and coherently-coordinated downlink transmit antennas using zero-forcing (ZF) beamforming. We present experimental performance under different conditions and communication scenarios. We consider different transmit power levels, number of transmit antennas, resolution of channel state information (CSI) quantizer as well as CSI feedback channel data rates. We demonstrate significant gains even in the presence of practical impairments.

We investigate the downlink throughput of cellular systems where groups of M antennas - either co-located or spatially distributed - transmit to a subset of a total population of K > M users in a coherent, coordinated fashion in order to mitigate intercell interference. We consider two types of coordination: the capacity-achieving technique based on dirty paper coding (DPC), and a simpler technique based on zero-forcing (ZF) beamforming with per-antenna power constraints. During a given frame, a scheduler chooses the subset of the K users in order to maximize the weighted sum rate, where the weights are based on the proportional-fair scheduling algorithm. We consider the weighted average sum throughput among K users per cell in a multi-cell network where coordination is limited to a neighborhood of M antennas. Consequently, the performance of both systems is limited by interference from antennas that are outside of the M coordinated antennas. Compared to a 12-sector baseline which uses the same number of antennas per cell site, the throughput of ZF and DPC achieve respective gains of 1.5 and 1.75.

We consider the problem of designing the vector quantizer for a multiuser eigenmode transmission applied to a MIMO broadcast channel with limited feedback. We propose a hierarchical vector quantization method based on the idea that the CDI can be refined by permitting multiple feedback steps between receivers and transmitter. At each new quantization level a new codebook is built based on the codebook and on the best codeword at the previous level. We firstly give a general definition of a hierarchical quantizer. Then we build a hierarchical quantizer based on the Fourier codebook. Finally, we propose an algorithm for implementing the hierarchical quantization principle. The proposed algorithm improves the channel quantization quality if the channel vector remains almost constant during different feedback steps, whereas allows backward steps to lower levels, in order to track channel variations. Simulations comparison with respect to the Fourier codebook and to the random vector quantization technique, confirm the effectiveness of the proposed approach.

We consider the MIMO broadcast channel where a base station with multiple antennas transmits simultaneously to many users. The optimum strategy with channel state information (CSI) at the transmitter is based on dirty paper coding (DPC) principles. In this paper, we propose a suboptimum strategy suitable when terminals are equipped with multiple antennas. This approach combines DPC and receive beamforming and is shown to provide near-optimum performance with very reasonable complexity. Beamforming design is based on the maximization of the sum rate while precoder design is based on zero forcing (ZF) criteria. We have to remark that it is not required iterative processes or interaction between transmitter and receivers to find the suboptimum solution. It is also important to mention that each terminal just needs to know its own channel to perform the optimization while the base station requires full knowledge of all users' channels

Multiple antenna techniques for spatial multiplexing (SM) and spatial division multiple access (SDMA) are efficient ways to increase spectral efficiency. We characterize downlink fixed wireless systems using a few essential parameters—including number of antennas per link, number of sectors per cell, and frequency reuse plan—and study their impact on the system spectral efficiency and user peak rates through simulation. Characterizing the system in this way provides insight into the fundamental performance of fixed wireless systems and tradeoffs between SM and SDMA that are independent of specific air interface implementations. We conclude that for a given number of transmit and receive antennas, SM is efficient for increasing user peak rates, but SDMA is more efficient for increasing system spectral efficiency.

This paper presents a generalized multiple-input multiple-output (MIMO) channel modeling technique for link level and system level simulations. The model combines the correlation approach and wave superposition approach to achieve both accuracy and efficiency. The spatial, temporal, and frequency dispersions of the MIMO channels are implicitly modeled based on any given statistics. Polarizations, channel transitions, uplink and downlink channels, and keyhole-pinhole channels are modeled as optional modules. Theoretical justifications as well as experimental verification of the model are also presented. The proposed model can be applied to system simulations for MIMO as well as other adaptive antenna applications.

The Bell Labs layered space-time (BLAST) architecture provides high capacity wireless communications in rich scattering environments. Training sequences are transmitted periodically to estimate the channel. We compare the performance without channel tracking to the performance with interpolation-based channel tracking. The optimal training interval, training length and the maximum throughput are found through computer simulations for both schemes as functions of the Doppler frequency and the number of antennas. We show that the throughput is significantly improved with channel tracking for fast continuous fading channels. However channel tracking increases the computation complexity for data detection. A sub-optimal detection algorithm with lower complexity is proposed and investigated.

The paper presents a multiple-input-multiple-output (MIMO) channel modeling technique for link level and system level simulations. The classical approach of wave superposition is adopted to generate MIMO channels that are statistically consistent in delay, frequency and angle domains. The paper proposes an efficient way to generate four dimensional MIMO channel transfer matrices based on standard channel statistics such as power delay profile (PDP) and power azimuth spectrum (PAS).

Multiple input multiple output (MIMO) systems can provide high data rate wireless services in a rich scattering environment. We study one of the proposals for MIMO systems, the Bell Labs Layered Space-Time (BLAST) architecture. Channel estimation using training sequences is required for coherent detection in BLAST. We apply the maximum likelihood (ML) channel estimator and the optimal training sequences for block flat fading channels to continuous flat fading channels, and analyze the estimation error. The optimal training length and training interval that maximize the throughput for a given target bit error rate are found by computer simulations as functions of the Doppler frequency and the number of antennas.

Providing wireless high-speed packet data services for Web browsing and streaming multimedia applications will be a key feature in future code-division multiple-access (CDMA) systems. We study down-link CDMA schemes for providing such services using multiple antennas at the transmitter and receiver. We propose a generalization of the point-to-point narrowband Bell Labs layered space-time (BLAST) system to a wideband multiple access system which simultaneously supports multiple users through code spreading. We discuss transmission options for achieving transmit diversity and spatial separation and introduce a generalization of the vertical BLAST detector for CDMA signals. Using link level simulations, we determine the bit-error rates versus signal-to-interference ratio of the various transmitter options. We then describe a novel technique for determining the system spectral efficiency (measured in bits per second per Hertz per cell sector) by incorporating the link level results with system level outage simulations. Using four antennas at the transmitter and eight antennas at each receiver, the system can support multiple receivers at 16 times the voice rate, resulting in a system spectral efficiency an order magnitude higher than a conventional single-antenna voice system.

Multiple-input-multiple-output (MIMO) systems can provide high data rate wireless services in a rich scattering environment. We study one of the proposals for MIMO systems, the Bell Labs Layered Space-Time (BLAST) architecture. Channel estimation using training sequences is required for coherent detection in BLAST. We apply the maximum-likelihood channel estimator and the optimal training sequences for block flat fading channels to continuous flat fading channels and analyze the estimation error. The optimal training length and training interval that maximize the throughput for a given target bit error-rate are found by computer simulations as functions of the Doppler frequency and the number of antennas.

Using multiple antennas at both the transmitter and receiver, BLAST exploits the multipath characteristics of a wireless channel to enable very large capacities. In an extended V-BLAST, those capacities are approached with single-dimensional coding and rate adaptation. However, such results require infinite precision for rate and power, which is not implementable due to a limited data rate on the feedback channel. We show that discretizing the rates to QAM constellations causes a 3-5 dB penalty. We then propose SQPC (successive quantization with power control), an ad-hoc low-complexity algorithm that reduces that penalty to 1-3 dB. We show that SQPC closely approaches the performance limit attainable with discrete QAM rates, which we find numerically by exhaustive search.

Multiple antennas at the transmitter and receiver can achieve enormous capacities by transmitting on the channel's eigenmodes when the channel realization is known at the transmitter. If the transmitter has no knowledge of the channel, a significant fraction of the eigenmode capacity can be achieved in an open-loop mode, but multi-dimensional coding is required. We show how the open-loop capacity can be achieved with conventional single-dimensional coding using optimum successive decoding (OSD) and simple per-antenna rate control. Using power allocation, the capacity can be further increased, although only slightly.

We consider the problem of detecting synchronous code division multiple access (CDMA) signals in multipath channels that result in multiple access interference (MAI). It is well known that such challenging conditions may create severe near-far situations in which the standard techniques of combined power control and temporal single-user RAKE receivers provide poor performance. To address the shortcomings of the RAKE receiver, multiple antenna receivers combining space-time processing with multiuser detection have been proposed in the literature. Specifically, a space-time detector based on minimizing the mean-squared output between the data stream and the linear combiner output has shown great potential in achieving good near-far performance with much less complexity than the optimum space-time multiuser detector. Moreover, this space-time minimum mean-squared error (ST-MMSE) multiuser detector has the additional advantage of being well suited for adaptive implementation. We propose novel trained and blind adaptive algorithms based on stochastic gradient techniques, which are shown to approximate the ST-MMSE solution without requiring knowledge of the channel. We show that these linear space-time detectors can potentially provide significant capacity enhancements (up to one order of magnitude) over the conventional temporal single-user RAKE receiver.

In BLAST (Bell Labs' Layered Space Time) systems, very high spectral efficiency can be achieved by employing antenna arrays at both transmit and receive sides. Coding for these array systems is an interesting topic, as such, has seen intensive research. We study coding architectures constructed from conventional codes including convolutional codes and Reed Solomon codes. Our main interest is in the performance and complexity trade-offs involved in the design of coding/decoding and signal detection algorithms. We show that iterative detection and decoding (IDD) can significantly improve the performance of coded BLAST. In some cases, IDD allows for very simple detection algorithms to be used at the receiver front end. Therefore, it may in fact reduce the overall receiver complexity. Our results again demonstrate that coded V-BLAST (vertical-BLAST) is a promising architecture to achieve the great potential of BLAST with limited complexity.

In BLAST (Bell-Laboratories LAyered Space Time) systems, multiple transmit and receive antennas are employed to achieve very high spectral efficiencies. The ideal detection method for such systems is the maximum-likelihood (ML) algorithm. However, the ML complexity increases exponentially with the number of transmit antennas and the number of bits per modulation symbol. A reduced-complexity detection method has been suggested, using ordered successive interference cancellation. We consider two other suboptimum techniques: channel-based adaptive group detection and multistep reduced-constellation detection. The goal is to reduce the two aforementioned complexity exponentials. The algorithms efficiently combine linear processing with local ML search. We limit the complexity by maintaining small ML searching areas, while maximizing the performance under the complexity constraint by optimizing the front-end linear processing and the selection of the search areas.

Multiple antennas and space-time processing are known to provide large link-level capacity gains. In this paper we investigate the system-level impact of using antenna arrays with up to four elements at both transmitter and receiver in a downlink CDMA system. We consider the following uses for multiple transmit antennas: transmit diversity, BLAST transmission (using the same spreading code to radiate independent data from different antennas), and directive beam steering. Link spectral efficiencies can be calculated as a function of the required signal-to-interference ratio (SIR) assuming capacity-approaching channel coding. System-level simulations provide the probability distribution of received SIR in a multi-cell CDMA system, with and without directive beam steering. The link- and system-level results are then combined to yield the number of equal-rate users that can be supported at some outage level. We show that 10 fold increases in user capacity can be achieved when up to four antenna elements are used on each side of the link (assuming a per-user rate of 1.25 Mbps and a 5-MHz bandwidth).

We consider the downlink rate scheduling problem for CDMA networks with multiple users carrying packets with different types of traffic ('voice/audio only', 'data-only' or mixed traffic), each of which has its own QoS requirements that need to be satisfied. At each timeslot we serve the user with the earliest deadline. If his head packet was fully served and there is still power left in the system, we repeat the procedure with the next earliest deadline user, as long as we have power resources. We can thus serve more than one user per timeslot, power permitting. We compare our proposed algorithm to the earliest deadline one, the best scheduling scheme presented by Andrews, Kumaran, Ramanan, Stolyar and Whiting (see Bell Labs Technical Journal, 1999) for similar situations and conclude that we outperform it.

We investigate the use of multiple antennas at the transmitter and multiple antennas and multi-user decorrelating detectors at the receivers for providing high-speed data on the downlink of cellular CDMA systems. We analyze the system's spectral efficiency by combining link level bit error rate simulations with system level signal-to-interference ratio simulations. We show that using random spreading codes, a spectral efficiency of 3.8 bits/chip/sector can be achieved using 8 transmit and 12 receive antennas, compared to a spectral efficiency of 0.16 bits/chip/sector for a voice system with single transmit and receive antennas.

We derive and evaluate the performance of two minimum mean-squared error (MMSE) multiuser multi-antenna detectors for multipath channels. We consider the case of synchronous BPSK data signals for which the two detectors differ in the order of the "real" operation (r) and the linear transformation (T) for mitigating multi-access interference. In the adaptive implementations of these detectors, the Tr detector does not require prior channel estimates whereas the rT does. We show that as the noise floor vanishes, these MMSE detectors are equivalent to respective rT and Tr decorrelating (zero-forcing) detectors. If the channel parameters are known exactly, we show that the performance of the rT decorrelating detector uniformly outperforms the Tr decorrelator. Semi-analytic results demonstrate the capacity gains achieved using these multiantenna detectors.

In wireless local loop (WLL) systems employing direct sequence spread-spectrum (DS/SS) code-division multiple-access (CDMA), the use of closed-loop timing and power control, short orthogonal spreading codes, and directional antennas at the subscribers result in a vastly different interference scenario compared to mobile cellular systems in which asynchronous subscribers use omnidirectional antennas. In the WLL reverse link, residual timing offsets and multipath propagation give rise to intrasector interference, and subscribers in other sectors give rise to intersector interference. We address the improvements afforded by replacing the conventional despreader by an adaptive linear minimum-mean-square-error (MMSE) detector that exploits the cyclostationary nature of the above interference. We compare the conventional and MMSE detectors for varying degrees of intrasector interference by varying the timing error, and for varying degrees of intersector interference by considering a system with two carrier frequencies per cell and a system with universal frequency reuse (and hence twice the capacity). Our results indicate that, compared to the despreader, the MMSE detector effectively mitigates the interference to enable a WLL system with higher capacity and less stringent timing control.

The IS-95 downlink channel incorporates a pilot signal to aid the mobile receiver in obtaining coherent channel estimates for use in the RAKE receiver. We propose a simple, recursive multipath noise cancellation (MNC) detector which uses an on/off switch mechanism for controlling the noise cancellation for each RAKE finger. We derive the switch mechanism which minimizes the mean-squared error (MSE) at the RAKE output. This switched-MNC detector, when compared with the conventional RAKE receiver, demonstrates significant performance gains, increasing the downlink capacity of a typical IS-95 system by up to 10%, while requiring minimal additional complexity when implemented using a simple recursive structure for the MNC function. Furthermore, we observe that almost all of the performance gains of the MMSE switched-MNC detector can be achieved using the simpler, ad-hoc, "always-on" MNC detector, in which pilot multipath noise cancellation always occurs at each finger input without regard to minimizing the MSE.